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JCO PO author Dr. David R. Gandara at UC Davis Comprehensive Cancer Center, shares insights into his JCO PO article, “Plasma Proteome–Based Test for First-Line Treatment Selection in Metastatic Non–Small Cell Lung Cancer,” one of the Top Articles of 2024. Host Dr. Rafeh Naqash and Dr. Gandara discuss how the PROphet® blood test supports first-line immunotherapy treatment decisions for metastatic NSCLC patients. TRANSCRIPT  Dr. Rafeh Naqash: Hello and welcome to JCO Precision Oncology Conversations where we bring you engaging conversations with authors of clinically relevant and highly significant JCOPO articles. I'm your host, Dr. Rafeh Naqash, Podcast Editor for JCO Precision Oncology and Assistant Professor at the OU Health Stephenson Cancer Center at the University of Oklahoma.  Today, we are absolutely thrilled to be joined by Dr. David R. Gandara, Professor of Medicine Emeritus, Co-Director of the Center for Experimental Therapeutics and Cancer and Senior Advisor to the Director at UC Davis Comprehensive Cancer Center and also the senior author of the JCO Precision Oncology article entitled “Plasma Proteome–Based Test for First-Line Treatment Selection in Metastatic Non–Small Cell Lung Cancer.” This was one of the top performing articles of 2024, which is one of the reasons why we wanted to bring it in for a podcast discussion. At the time of this recording, our guest's disclosures will be linked in the transcript.  David, it is an absolute pleasure to have you today. For somebody like you who's led the field of lung cancer over the years, I'm really excited that you are going to be talking to us about this very interesting article, especially given that I think you're one of the big proponents of liquid biopsies and plasma-based testing. So, for the sake of our listeners - which comprises of academic oncologists, community oncologists, trainees - could you tell us where the biomarker landscape for non-small cell lung cancer is currently, and then we can try to take a deeper dive into this article. Dr. David Gandar: Okay. Well, thank you, Rafeh. It's a pleasure to be with you here today. And I think the current landscape for biomarkers for immunotherapy in non-small cell lung cancer is a mess. There's no better way to describe it. That makes this paper describing a new plasma proteomic assay even more important. So I'll just give you a perspective. There are 14 trials, phase three trials, that were done in first line non-small cell lung cancer advanced stage of immunotherapy versus chemotherapy and some other aspects, although they vary tremendously. Some of them were checkpoint monotherapy, some combined with chemotherapy, some combined with CTLA-4 inhibitors and so forth. 12 out of the 14 were positive, 12 got FDA approval. So there are 12 different options that an oncologist could use. Some of them were squamous cell only, some non-squamous, some used PD-L1 as a biomarker driven part of the study. Some used TMB, tumor mutational burden, some were agnostic. So when you put all of this together, an oncologist can pick and choose among all these various regimens. And by and large, it's PD-L1 that is the therapeutic decision maker.  ASCO actually, I think, has done the very best job of making a guideline, and it's, as you well know, called a living guideline, it's dynamic. And it is much easier to interpret, for me and I think for oncologists, than some of the other guidelines. It's got a green light and a red light, it may be kind of orange. And so the green light means this is a strong recommendation by the guideline committee. The orange means it's weak. For this purpose, non-small cell lung cancer, advanced stage, only a very few of the recommendations were green. It's mainly monotherapy and patients with cancers with a PD-L1 over 50%. In our surveys, at our meetings, less than 50% of oncologists in the United States are following these guidelines. Why? Because they don't trust the biomarker. And TMB has the same sort of limitations. They're not bad biomarkers, they're incomplete. They're only looking at a part of the story. So that means we need a new biomarker. And this is one that, I think, the data are quite impressive and we'll discuss it more. Dr. Rafeh Naqash: Absolutely. Like you said, abundance of many therapy options, but not necessarily everything works the same in different subsets of PD-L1 positivity or different subsets of patients with different levels of tumor burden. And like you said, again, difficulty in trying to identify the right biomarker. And that's a nice segue to this PROphet test that you guys ran. So can you tell us a little bit about the plasma proteomic assay? Because to the best of my knowledge, there's not a lot of validated plasma proteomic assays. A lot has been done on the tumor tissue side as far as biomarkers are concerned, but not much on the blood side, except for maybe ctDNA MRD testing. So what was the background for trying to develop a plasma-based proteomic test? And then how did this idea of testing it in the lung cancer setting come into play? And then we can go into the patient population specifics, the cohort that you guys have. Dr. David Gandara: Okay. Well, of course there's a company behind this assay, it's called OncoHost, and I'm a consultant for them. And they came to me two years ago and they said, “We have something different from anyone else.” And they explained the science to me, as well as some other lung cancer experts here in the United States. I'm not a proteomic expert, of course, but they developed an AI machine learning platform to assess plasma proteins in normal people and in people with cancer, and specifically then in people with non-small cell lung cancer. They identified over 7,000 proteins that had cancer implications for therapy, for resistance, for prognosis, etc., and they categorized them based on the literature, TCGA data, etc., and used this machine learning process to figure out which proteins might be most specific for non-small cell lung cancer. And that's where they started. And so out of that 7,000 proteins, where they've identified which ones are angiogenic, which ones are involved with EMT or cell cycle or whatever it might be, they distilled it down to 388 proteins which they thought were worth testing in non-small cell lung cancer. And that's when I became involved.  They had a retrospective cohort of patients that had been treated with various immunotherapies. They looked at the analytic validation first, then applied it to this cohort. It looked good. Then they had a very large cohort, which they split, as you usually do with an assay, into a test set and then a validation set. For the test set, they wanted something more than a response. They wanted some indicator of long term benefit because that's where immunotherapy differentiates itself from chemotherapy and even targeted therapy. And so they picked PFS at 12 months. And I became involved at that point and it looked really good. I mean, if you look at the figures in the manuscript, the AUC is superb about their prediction and then what actually happened in the patient. And then in this paper, we applied it to a validation set of over 500 patients in a prospective trial, not randomized, it's called an observational trial. The investigator got to pick what they thought was the best therapy for that patient. And then in a blinded fashion, the proteomic assay experts did the analysis and applied it to the group.  And so what that means is some of the patients got chemotherapy alone, some got checkpoint immunotherapy monotherapy, some got in combination with chemotherapy. None of the patients in this study got a CTLA-4 inhibitor. That work is ongoing now. But what the study showed was that this assay can be used together with PD-L1 as what I would call a composite biomarker. You take the two together and it informs the oncologist about the meaning of that PD-L1. I'll give you an example. If that patient has a PD-L1 over 50% in their cancer and yet the PROphet test is negative, meaning less than 5 - it's a 0 to 10 scale - that patient for survival is better served by getting chemotherapy and immunotherapy. However, if the PROphet test is positive and the PD-L1 is over 50%, then the survival curves really look equivalent. As I said earlier, even in that group of patients, a lot of oncologists are reluctant to give them monotherapy. So if you have a test and the same sort of example is true for PD-L1 0, that you can differentiate. So this can really help inform the oncologist about what direction to go. And of course then you use your clinical judgment, you look at what you think of as the aggressiveness of the tumor or their liver metastases, etc. So again, that's how this test is being used for non-small cell lung cancer. And maybe I'll stop there and then I'll come back and add some other points. Dr. Rafeh Naqash: I definitely like your analogy of this therapy de-escalation strategy. Like you mentioned for PD-L1 high where the PROphet test is negative, then perhaps you could just go with immunotherapy alone. In fact, interestingly enough, I was invited to a talk at SITC a couple of weeks back and this exact figure that you're referring to was one of the figures in my slide deck. And it happened by chance that I realized that we were doing a podcast on the same paper today.  So I guess from a provocative question standpoint, when you look at the PD-L1 high cohort in the subset where you didn't see a survival difference for chemo plus immunotherapy versus immunotherapy alone, do you think any element of that could have been influenced by the degree of PD-L1 positivity above 50%? Meaning could there have been a cohort that is, let's say PD-L1 75 and above, and that kind of skews the data because I know you've published on this yourself also where the higher the PD-L1 above 50%, like 90% PD-L1 positivity survival curves are much better than 50% to 89%. So could that have somehow played a role? Dr. David Gandara: The first thing to say is that PD-L1 and the PROphet score, there's very little overlap. I know that sounds surprising, but it's also true for tumor mutational burden. There's very little overlap. They're measuring different things. The PD-L1 is measuring a specific regulatory protein that is applicable to some patients, but not all. That's why even in almost all of the studies, people with PD-L1 0 could still have some survival benefit. But in this case they're independent. And not in this paper, but in other work done by this group, the PROphet group, they've shown that the PROphet score does not seem to correlate with super high PD-L1. So it's not like the cemiplimab data where if you have a PD-L1 of greater than 90%, then of course the patient does spectacularly with monotherapy. The other thing that's important here is they had a group of around a little less than 100 patients that got chemotherapy alone. The PROphet score is agnostic to chemotherapy. And so that means that you're not just looking at some prognostic factor. It's actually clinical utility on a predictive basis. Dr. Rafeh Naqash: I think those are very important points. I was on a podcast a couple of days back. I think there's a theme these days we're trying to do for JCO Precision Oncology, we're trying to do a few biomarker based podcasts, and the most recent one that we did was using a tissue transcriptome with ctDNA MRD and you mentioned the composite of the PD-L1 and the PROphet test and they use a composite of the tissue transcriptome. I believe they called it the VIGex test as well as MRD ctDNA. And when your ctDNA was negative at, I believe, the three month mark, those individuals had the highest inflamed VIGex test or highest infiltration of T cells, STING pathway, etc. So are there any thoughts of trying to add or correlate tissue based biomarkers or ctDNA based correlations as a further validation in this research with the company? Dr. David Gandara: Right. So there are many things that are being looked at, various composites looking at the commutations that might affect the efficacy of immunotherapy and how they correlate with profit positivity or negativity. And I'll just give the examples of STK11 and KEAP1. As you know, there's some controversy about whether these are for immunotherapy, whether they're more prognostic or predictive. I'm one of the co-authors among many in the recently published Nature paper by Dr. Skoulidis and the group at MD Anderson which report that for KEAP1 positive especially, but also SDK11 mutated getting immunotherapy, that that's where the CTLA-4 inhibitors actually play the greatest role. So realizing that this is still controversial, there are preliminary data, not published yet, that'll be presented at an upcoming meeting, looking at many of these other aspects, P53, SCK11, KEAP1, other aspects, TMB, that's actually already published, I think in one of their papers. So yes, there's lots of opportunities.  The other cool thing is that this isn't a test, it's a platform. And so that means that the OncoHost scientists have already said, “What if we look at this test, the assay in a group of patients with small cell lung cancer?” And so I just presented this as a poster at the world conference in San Diego. And it turns out if you look at the biology of small cell, where neither PD-L1 nor TMB seem to be very important, if you look at the biology of small cell and you form an assay, it only shares 44 proteins out of the 388 with non-small cell. It's a different biology. And when we applied that to a group of patients with small cell lung cancer, again it had really pretty impressive results, although still a fairly small number of patients. So we have a big phase three study that we're doing with a pharmaceutical company developing immunotherapy where we are prospectively placing the PROphet test in a small cell trial.  The platform can also be altered for other cancer types. And at AACR, Dr. Jarushka Naidoo presented really impressive data that you can modify the proteins and you can predict immunotherapy side effects. So this is not like a company that says, “We have one test that's great for everything.” You know how some companies say, “Our test, you can use it for everything.” This company is saying we can alter the protein structures using AI machine learning assisted process to do it and we can have a very informed assay in different tumor types and different situations. So to me, it's really exciting. Dr. Rafeh Naqash: Definitely to me, I think, combining the AI machine learning aspect with the possibility of finding or trying to find a composite biomarker using less invasive approaches such as plasma or blood, definitely checks a lot of boxes. And as you mentioned, trying to get it to prospective trials as an integral biomarker perhaps would be likely the next step. And hopefully we see some interesting, exciting results where we can try to match or stratify patients into optimal combination therapies based on this test.  So now to the next aspect of this discussion, David, which I'm really excited about. You've been a leader and a mentor to many. You've led ISLC and several other corporate group organizations, et cetera. Can you tell us, for the sake of all the listeners, junior investigators, trainees, what being a mentor has meant for you? How your career has started many years back and how it's evolved? And what are some of the things that you want to tell people for a successful and a more exciting career as you've led over the years? Dr. David Gandara: Well, thank you for the question. Mentoring is a very important part of my own career. I didn't have an institutional mentor when I was a junior investigator, but I had a lot of senior collaborators, very famous people that kind of took me under their wing and guided me. And I thought when I basically establish myself, I want to give back by being a mentor to other people. And you wouldn't believe the number of people that I'm even mentoring today. And some of them are not medical oncologists, they're surgeons, they're radiation oncologists, they're basic scientists. Because you don't have to be an expert in that person's field to be a mentor. It helps, but in other words, you can guide somebody in what are the decision making processes in your career. When is it time to move from this institution onward because you can't grow in the institution you're in, either because it's too big or it's too small? So I established a leadership academy in the Southwest Oncology Group, SWOG. I've led many mentoring courses, for instance, for ISLC, now for International Society Liquid Biopsy, where I'm the executive committee liaison for what's called The Young Committee. So ISLB Society, totally devoted to liquid biopsy, six years old now, we have a Young Committee that has a budget. They develop projects, they publish articles on their own, they do podcasts. So what I'm saying is those are all things that I think opens up opportunities. They're not waiting behind senior people, they are leading themselves.  We just, at our International Lung Cancer Congress, reestablished a fellows program where a group of fellows are invited to that Huntington beach meeting. It's now in its 25th year and we spend a day and a half with them, mentoring them on career building. I'll just give you my first, I have the “Letterman Top 10”. So my first recommendation is if all you have is lemons, make lemonade. And what I'm meaning is find what you can do at your institution if you're a junior person, what you can claim to be your own and make the very best of it. But then as you get further along in my recommendations, one of them is learn when to say ‘no'. Because as a junior investigator the biggest threat to your career is saying ‘yes' to everybody and then you become overwhelmed and you can't concentrate. So I'll stop there. But anyway, yes, mentoring is a big part of my life. Dr. Rafeh Naqash: Well, thank you, David. This is definitely something that I'm going to try to apply to my career as well. And this has been an absolute pleasure, especially with all the insights that you provided, not just on the scientific side but also on the personal career side and the mentorship side. And hopefully we'll see more of this work that you and other investigators have led and collaborated on. perhaps more interesting plasma based biomarkers. And hopefully some of that work will find its home in JCO Precision Oncology. Thank you again for joining us today. Dr. David Gandara: My pleasure. Dr. Rafeh Naqash: And thank you for listening to JCO Precision Oncology Conversations. Don't forget to give us a rating or review and be sure to subscribe so you never miss an episode. You can find all ASCO shows at asco.org/podcasts.   The purpose of this podcast is to educate and to inform. This is not a substitute for professional medical care and is not intended for use in the diagnosis or treatment of individual conditions.  Guests on this podcast express their own opinions, experience and conclusions. Guest statements on the podcast do not express the opinions of ASCO. The mention of any product, service organization, activity or therapy should not be construed as an ASCO endorsement.   Dr. David Gandara Disclosures: Consulting or Advisory Role Company: Henlius USA, Foundation Medicine, Janssen Pharma, Merck & Co, Mirati Therapeutics, Regeneron, AstraZeneca, Guardant Health, Genentech, Exact Sciences  Research Funding Company: Amgen, Genentech, Astex Pharma  

El Dr. Manel Esteller es un referente mundial en epigenética y biología del cáncer, conocido por su investigación sobre la metilación del ADN en tumores. Lidera proyectos en el Instituto Josep Carreras, desarrollando terapias innovadoras contra el cáncer. Con más de 640 publicaciones científicas de alto impacto y numerosos premios internacionales, su trabajo ha dejado una huella significativa en la biomedicina. Además, dirige el proyecto TCGA para tumores de origen desconocido. Link del producto de Eucerin

In this special 40-minute episode, of Financial Detox® Jason Labrum and Co-hosts Alex Klingensmith team up with Brian Raftery, a partner in Dentons' Trusts, Estates and Wealth Preservation practice and currently serves as co-leader of the US Region. Together, they tackle the ins and outs of estate planning with precision and expertise. Join the trio as they delve into the essential aspects of estate planning, uncovering common pitfalls and emphasizing the proactive approach needed for success. Brian sheds light on the importance of a revocable living trust and asset transfer strategies, providing listeners with invaluable insights. The discussion extends to the intricate realm of estate taxes, examining potential legislative changes and their implications for estate planning strategies. With a focus on California's unique landscape, they explore complex topics such as estate tax exemptions, foundational documents, and advanced techniques like spousal lifetime access trusts (SLATs). Additionally, the episode delves into sophisticated strategies like utilizing LLCs and discounting to optimize estate planning outcomes. 00:00 Introduction and Overview 02:14 Defining Estate Planning and Common Mistakes 05:19 The Importance of a Revocable Living Trust 07:31 Avoiding Probate and Ensuring Asset Distribution 10:22 Understanding Estate Taxes and Potential Changes 15:50 State-Specific Estate Tax Laws 19:35 Foundational Estate Planning Documents 21:38 Potential Changes to Estate Tax Exemptions 23:24 Spousal Lifetime Access Trusts (SLATs) 25:06 Utilizing SLATs for Estate Planning 31:41 Maximizing Estate Tax Benefits with LLCs and Discounting 39:05 The Importance of a Comprehensive Estate Planning Team More on Financial Detox Buy the Book Subscribe and view episodes on Youtube Follow and learn more about our IDA Wealth Advisers:  Website Linkedin Instagram Facebook Youtube More on our guest Brian E. Raftery is a partner in Dentons' Trusts, Estates and Wealth Preservation practice and currently serves as co-leader of the US Region. He is also the global co-leader of the Dentons Family Office and High Net Worth sector which provides cross-practice services to family offices and high net worth individuals. He was also awarded Best Lawyers in America, Trusts and Estates in 2020-2024. #markets #financialplanning #financialdetox #Idawealthadvisers #estateplanning #revocablelivingtrust #assets #SLATs #sunsettax #gifttax #clifftax #californiaestatetax #tax #TCGA #trust        ___________________________ Disclosures

BUFFALO, NY- January 30, 2024 – A new #research paper was #published in Aging (listed by MEDLINE/PubMed as "Aging (Albany NY)" and "Aging-US" by Web of Science) Volume 16, Issue 1, entitled, “XRCC1: a potential prognostic and immunological biomarker in LGG based on systematic pan-cancer analysis.” X-ray repair cross-complementation group 1 (XRCC1) is a pivotal contributor to base excision repair, and its dysregulation has been implicated in the oncogenicity of various human malignancies. However, a comprehensive pan-cancer analysis investigating the prognostic value, immunological functions, and epigenetic associations of XRCC1 remains lacking. In this new study, researchers Guobing Wang, Yunyue Li, Rui Pan, Xisheng Yin, Congchao Jia, Yuchen She, Luling Huang, Guanhu Yang, Hao Chi, and Gang Tian from Southwest Medical University, The Affiliated Hospital of Southwest Medical University, Yibin Hospital of T.C.M, Medical School of Nanchang University, Fourth Military Medical University, and Ohio University aimed to address this knowledge gap by conducting a systematic investigation employing bioinformatics techniques across 33 cancer types. “Our analysis encompassed XRCC1 expression levels, prognostic and diagnostic implications, epigenetic profiles, immune and molecular subtypes, Tumor Mutation Burden (TMB), Microsatellite Instability (MSI), immune checkpoints, and immune infiltration, leveraging data from TCGA, GTEx, CELL, Human Protein Atlas, Ualcan, and cBioPortal databases.” Notably, XRCC1 displayed both positive and negative correlations with prognosis across different tumors. Epigenetic analysis revealed associations between XRCC1 expression and DNA methylation patterns in 10 cancer types, as well as enhanced phosphorylation. Furthermore, XRCC1 expression demonstrated associations with TMB and MSI in the majority of tumors. Interestingly, XRCC1 gene expression exhibited a negative correlation with immune cell infiltration levels, except for a positive correlation with M1 and M2 macrophages and monocytes in most cancers. Additionally, the researchers observed significant correlations between XRCC1 and immune checkpoint gene expression levels. Lastly, their findings implicated XRCC1 in DNA replication and repair processes, shedding light on the precise mechanisms underlying its oncogenic effects. “Overall, our study highlights the potential of XRCC1 as a prognostic and immunological pan-cancer biomarker, thereby offering a novel target for tumor immunotherapy." DOI - https://doi.org/10.18632/aging.205426 Corresponding authors - Guanhu Yang - guanhuyang@gmail.com, Hao Chi - Chihao7511@163.com, and Gang Tian - tiangang@swmu.edu.cn Subscribe for free publication alerts from Aging - https://www.aging-us.com/subscribe-to-toc-alerts Keywords - aging, X-ray repair cross-complementation group 1, pan-cancer, prognosis, immune infiltration, tumor microenvironment About Aging-US Launched in 2009, Aging-US publishes papers of general interest and biological significance in all fields of aging research and age-related diseases, including cancer—and now, with a special focus on COVID-19 vulnerability as an age-dependent syndrome. Topics in Aging-US go beyond traditional gerontology, including, but not limited to, cellular and molecular biology, human age-related diseases, pathology in model organisms, signal transduction pathways (e.g., p53, sirtuins, and PI-3K/AKT/mTOR, among others), and approaches to modulating these signaling pathways. Visit our website at https://www.Aging-US.com​​ and connect with us: Facebook - https://www.facebook.com/AgingUS/ X - https://twitter.com/AgingJrnl Instagram - https://www.instagram.com/agingjrnl/ YouTube - https://www.youtube.com/@AgingJournal LinkedIn - https://www.linkedin.com/company/aging/ Pinterest - https://www.pinterest.com/AgingUS/ Spotify - https://open.spotify.com/show/1X4HQQgegjReaf6Mozn6Mc MEDIA@IMPACTJOURNALS.COM

In this instalment of The G Word, our guests engage in a compelling discussion centred around a recently published paper that supports the integration of whole genome sequencing into standard cancer care.  Our guests shed light on the transformative potential of combining health data with whole genome data. Discover how this innovative approach empowers doctors to deliver more personalised and effective care. Our guests delve into the findings of a landmark national study, unravelling the significance of identifying inherited cancers for patients and their families. The episode explores not only the scientific advancements but also the real-world impact on individuals facing a cancer diagnosis.  Our host Naimah Callachand is joined by Dr Nirupa Murugaesu, a Consultant in medical oncology at Guy's and St Thomas' NHS Foundation Trust, and the Principal Clinician for Cancer Genomics and Clinical Studies at Genomics England.  And by Professor Sir Mark Caulfield, a Professor of Clinical Pharmacology at Queen Mary University of London, and who previously served as Chief Scientist for Genomics England and was instrumental in the delivery of the 100,000 Genomes Project.    "In cancer we were sequencing sections of the tumour and comparing them to DNA inherited from your mum and dad, and that comparison allows us to work out what is driving the cancer, what may be affecting its potential for treatment and how we might choose treatments for patients.  So this is a real opportunity to create precision cancer care."     You can read the transcript below or download it here: https://files.genomicsengland.co.uk/documents/Podcast-transcripts/Whole-genome-sequencing-in-cancer-care.docx   Naimah: Welcome to the G Word.  What does it mean if we can test for inherited genes?    Nirupa: It can influence how their cancer is treated.  So it means that there may be certain types of therapy that are available if they have a specific inherited cancer gene, number one.  It also can impact in terms of preventing further or other cancers related to those genes, and it may impact the type of surgery they have, and also the type of overall cancer treatment.  And then finally, if they have got an inherited cancer, then, as I mentioned before, it may impact in terms of testing and screening for their family members.  Naimah: I'm your host Naimah: Callachand.  Today, I'm delighted to be joined by Dr Nirupa Murugaesu, who's a consultant in medical oncology at Guy's and St Thomas' NHS Foundation Trust, and the principal clinician for cancer genomics and clinical studies here at Genomics England.  And Professor Sir Mark Caufield, who's a Professor of Clinical Pharmacology at Queen Mary University of London, and who previously served as chief scientist for Genomics England and was instrumental in the delivery of the 100,000 genomes project.  Today, Mark and Nirupa are going to discuss key findings from a recent paper that's just been published in Nature.  If you enjoy today's podcast, we'd really love your support.  Please like, share and rate us on wherever you listen to your podcasts.  Now, let's get into the interview.  So first of all, Mark, I wondered if you could give me a bit of background on the 100,000 genomes project?  Mark: So the 100,000 genomes project started in July 2013 following an announcement by the then prime minister, David Cameron, that the UK would be the first health system in the world to sequence 100,000 whole genomes, which is as much as you and I can read of the genetic code.  In the case of cancer, which we focused on here, in cancer we were sequencing sections of the tumour and comparing them to DNA inherited from your mum and dad, and that comparison allows us to work out what is driving the cancer, what may be affecting its potential for treatment and how we might choose treatments for patients.  So this is a real opportunity to create precision cancer care.  Naimah: And Nirupa, can you tell me what the 100,000 genomes project meant for these patients with cancer?   Nirupa: I think, firstly, we're very grateful for all of the participants in the programme, because what it's allowed us to do is to look at the data as a whole, and having all of that sequencing data alongside clinical information has been incredibly valuable, it has also developed the infrastructure for testing.  And really I think for patients with cancer, they participated in this programme as a research project, and unusually for a research project these results were returned back to treating clinicians to clinical teams, if there may have been a result that would impact or change their management.  But I think, importantly, what it enabled is the implementation of standardised cancer testing in the NHS, and really enabling that for a wider range of patients, not just those that participated in the project.  And because of patients participating, this then allowed all of the data to be stored in a single place, and this has been incredibly valuable for clinical academics and researchers.    Naimah: And can I ask what specific types of cancer that were looked at in 100,000 genomes project?   Nirupa: Again, the project was set up such that we allowed a number of different types of cancers to be sequenced and, therefore, very permissible, because we also wanted to ensure that some of the less common and rarer cancers were also sequenced and, as you would expect, more of the common cancers as well.  In addition, I think the opportunity to sequence paediatric cancers, as well as haematological malignancies, or blood cancers, was also key as part of the cancer programme.  Here, we focus on the solid cancers, but obviously there was a much wider range of cancers that were sequenced.    Naimah: And next, can we move on to talk about the findings of the study?  Nirupa: I think, firstly, by undertaking sort of a pan-cancer analysis, it really gave us an overview of the number of target and genes that were found to be actionable.  And what I mean by that is that they have a, well, clinically relevant, and we can see that in certain cancer types, such as in brain cancers, in colon cancers, lung cancers, there were within the genome sequence more than 50% of these cancers had something that was what we would call actionable.  So there was a mutation in a gene for which this would influence treatment.  And as we started to look more across the entire cohort of patients, you can really get an idea of the fact that the more that we sequence, and the more comprehensive the testing is, the number of different types of mutations that we were able to discover.     Naimah: And when you mentioned that these findings were actionable, what does that mean?  Nirupa: So what that means is that has an impact in how the patient will be managed and treated.  It may influence, firstly, the type of surgery they have, it may influence the type of cancer treatment that they receive.  And all of this, I suppose, comes back to the point that Mark mentioned, of precision oncology, so we more precisely treat patients based on their individual cancers.     Naimah: And could you give me some examples of maybe some of these genes that were found in the study that were actionable?  Nirupa: Yes, so the types of genes also matter, or the type of mutations.  So some of them were in known cancer genes, and if you have, for example, a mutation in lung cancer, in a gene called the EGFR gene, we know that there are cancer therapies that can be provided that target specifically this mutation.  So that's one example, and this is quite well characterised and understood in oncology care.  But what we were also able to do with whole genome sequencing, is identify different types of mutations that are harder to characterise routinely.  And these are often included things that we call pan-genomic markers, where we can see what the mutational landscape is of the cancer, the different patterns of mutations can be gleaned from this, and often this can then give you an idea of the underlying biology of the cancer.  But importantly, in certain types of cancers, such as high grade serious ovarian cancer, it highlights which patients may have a particular marker that means they may or may not benefit from a particular type of therapy.  So in this particular case, the class of therapy is called PARP inhibitors.  Naimah: And how did the study compare to other similar stuff studies in the genomics area?  Nirupa: That's a really good question, and I think we looked at this from other large sequencing endeavours, such as the ICGC, TCGA, so these are big studies where have been whole genomes sequencing.  Also within the Hartwig Institute in the Netherlands, they've also undertaken whole genome sequencing for cancer patients.  And what we were able to identify is that the patterns of mutations were as expected, we found, you know, a lot of similarities.  I think the difference, the main difference is not just identifying the type of mutations across the different cancers.  But the fact that we were then able to look at the longitudinal outcome, and correlate some of these genomic markers with outcomes related to both therapies, as well as survival impact of having certain mutations in terms of prognosis.    Naimah: Mark, do you have something you'd like to add there as well?  Mark: Yeah.  So one of the things that we did in the 100,000 genomes project, was to evaluate the best way of measuring the whole of your or my genetic code.  And we discovered that very early on that if you expose the tumour to a preservative, which is called formalin which keeps the tumour preserved, that actually you could get quite a number of misleading findings.  And so to address that, the distinctiveness from former programmes, such as Nirupa mentioned, like the Cancer Genome Atlas, is that all of the tumours that we studied in this paper were actually produced under fresh tissue conditions, and have not been exposed to a preservative.  And that means that what we have is a really accurate reflection of the variation within the tumours.  And the other thing about this particular resource is it's the biggest resource.  We were able to look at 13,000 people with solid tumours, but we also had blood cancers and other cancers which also feature of this paper.    And a further remarkable thing about this is early on, Nirupa and the team and I decided that we would longitudinally life-course follow the patients and by accruing data from multiple sources in the health system.  So, every attendance at the hospital, what chemotherapy was had, we've been able in this paper to recapitulate signatures that clearly show that certain mutations are harmful.  And many of the findings that we've made are absolutely, if you look at the survival of patients particularly, you can see almost identical patterns to those in clinical trials.  What this means is that by the really rich data set which is now many billions of clinical data points on these patients, we can actually look for long-term signals of benefit and harm that perhaps would not be detected by a clinical trial that might last for six months or a year.  So this is a really valuable resource, and the really great thing is we can use what's called real-world data, which is where we take routine health data, and we can recapitulate the findings from tightly controlled clinical trials.  And I think that's quite an important finding.    Naimah: That kind of brings me onto the next question, Mark, where I want to talk about the value and benefit of genomics sequencing for cancer patients.  I wondered if you could expand?  Mark: Well, what we know from one of the genomics medicine centres which were regional hubs, is that they use the information that we return, that Nirupa outlined earlier in a report, for 25% of their patients.  Which means that they concluded having evaluated that as the clinical team locally, that there was something the patients could benefit from.  Now, what we think is this makes the case for certain cancers being part of the national genomics test directory whole genome sequencing, but it's still the case that the majority of testing for cancer is now very large focused panels that are focused on specific gene features.  But in some measure, this work is also able to reassure us that those gene features are the right ones to focus on, so this work has been very useful in that respect, even where the NHS today cannot make the financial or clinical case for using whole genomes in specific cancers.  So I think the programme's made a massive difference.    The biggest thing it's done for patients, which Nirupa was very actively involved in, is it's allowed us to create a national genomics test directory.  So when we started this, cancer genomic testing was completely random and would vary from one postcode to another, one hospital to another.  And what Nirupa and the cancer team created is a national cancer genomic test directory, which now means that standard of care, that's the basis for reimbursement, and it's available across the landscape of 56 million people.  And given that one in two of us will have cancer, this is a massive advance.    Naimah: Yeah, you've really highlighted the impact of having access to such a large database.  And I just wanted to ask as well, what are the challenges associated with implementing routine whole genome sequencing into clinical care?  Nirupa: I think as with all of these things when implementing something new within a healthcare system, it requires a level of education, upskilling and also, as Mark has touched on, how we handle the tumour tissue, so that it's handled in a genomic-friendly way to enable the best results if you like, because we want to ensure that their DNA is not damaged so that we can get accurate read-outs on the results.  So there are challenges and there is also cost implications in weighing up the pros and cons.  And I think what we were able to show, and by undertaking this sort of pan-cancer analysis, is where there are those cancer type where there is a real need for whole genome sequencing, or where it can be justified, because there are a number of different types of mutations both within the tumour.  And also from a blood sample that is also taken, so this is your constitutional DNA, so this is if there is a risk of an inherited cancer.  So we are able to pull together all of this information, and obviously that's important, not just for the patient, and their management, but also for family members.  So I think really what this shows is that where you have to identify many of these different types of mutations, whole genome sequencing enables that through a single test.   Naimah: Mark, would you like to add something else there?  Mark: One thing I think which Nirupa's very much part of, is the distinctiveness of the Genomics England approach has been to involve the NHS at every stage.  Now, what that means is we estimate that at the peak of the 100,000 genomes project, 5,000 frontline NHS staff touched the project at some point in their working week.  What that does mean is that Nirupa and the cancer team could realign the cancer tissue handling pathways.  But it also meant that we were able to upskill the frontline workforce, such that at the end of the programme, when we produced a genomic test directory, they were really up for it because they did not want all the hard work they'd put in to stop.  And so what we've done is produce the national test directory within five years of starting, that wasn't a deliverable for the project, but it was nonetheless obvious to all of us working in it, including NHS England, that there needed to be service transformation, and we've managed to effect it.    Now, if you look at other settings where perhaps Nirupa and I might have a research team, we might do it some distance from the health system, it would be in the health system, but not with the health system, then it takes between nine and 16 years to get these things into clinical practice.  And that was achieved here in five years.  So there is a lesson from this, the cancer programme particularly, because the cancer programme testing was very limited when we started, but you can take an entire workforce on a journey and leave them with the legacy of an entirely transformed system for patients.  And thankfully because we got, Nirupa and I, the NHS to agree to reimburse for the testing directory being used, we have eliminated a lot of randomness that was in the system previously.  So it's quite an important advance in that respect, and it really does show in the beautiful work that Nirupa was describing exactly how you can use this information to change an entire system.  And the NHS is not the easiest system to change in the world.  Naimah: Nirupa, you mentioned the findings show that there was potentially inherited genes.  Can you tell me what does that really mean for patients, if we're able to diagnose these inherited genes sooner in life?  Nirupa: It can influence how their cancer is treated, so it means that there may be certain types of therapy that are available if they have a specific inherited cancer gene, number one.  It also, can impact in terms of preventing further or other cancers related to those genes, and it may impact the type of surgery they have, and also the type of overall cancer treatment.  And then, finally, if they have got an inherited cancer, then, as I mentioned before, it may impact in terms of testing and screening for their family members.  And that's really key as well, because this means that their cancer can be diagnosed, if they do develop a cancer, because they're being monitored, because it's much more targeted, their approach in terms of screening for a particular type of cancer, they can potentially have their cancer treated much earlier.  Or even better, before it becomes what we call an invasive cancer but at the pre-cancerous stage.  So this has huge implications, and what we're finding actually with more and more testing – and this is not just... our study was consistent with other studies that have been published – is that when you undertake more routine testing, then you are able to identify this.  It is not common amongst the population, but in those patients where it is relevant, it really can impact their care.  Naimah: Mark, do you have something to add there?  Mark: Well, I think Nirupa's just highlighted a really important point.  So to bring that into a little bit more ways of which people listening to this can relate to it, we have a family where there was a women who had no family history of breast cancer, she developed breast cancer, and in the tumour we found that she had a BRCA 2 mutation.  We also found that she'd probably acquired that or inherited it, we don't know.  That for her meant that she could enter the Olympia trial, which was running at the time, which Nirupa alluded to earlier, was a study of PARP inhibitors.  But without that genetic makeup she'd never have got into that trial, and she probably wouldn't have been tested for BRCA at that time in the NHS because she had no family history, I think that's probably right, Nirupa.    And then there was a family-wide consequence for that, because she had a brother and son, and she also had a daughter, and the daughter was under 30 at the time and underwent BRCA testing and was BRCA 2 positive.  But she has the opportunity now to enter intensive breast screening from the age of 30, and that's what's happened.  And her brother, and this is the lady who had the breast cancer, her brother and her son may be at risk of prostate cancer, so they can consider testing.  So Nirupa makes a really important point, that when people have inherited a previous disposition to cancer, that can have a family-wide impact.  And one test in one family member can open the doors to opportunity for others to understand their risk and to be screened more actively and intensively, hopefully with meaning that if they do develop cancer it will be detected very early, or maybe we can just prevent it altogether.     Naimah: Thanks, Mark, a really good example of the impact that this testing has had.  I just wanted to touch back on your point, Mark, that you'd made about real-world data.  And I wondered actually, Nirupa, if you could kind of explain to me why it's important to link real-world data to the genomic data?  Nirupa: Yeah.  So I think the work we've done here really does emphasise this, because when we refer to real-world data, we're talking about different types of healthcare data across the population.  And we had the opportunity to link the genomic data to a number of key data sets that are curated by the cancer registry, the national cancer registry database.  And this includes things like all of the population base systemic anti-cancer therapy, so we know that for each of the participants the type of cancer therapy they receive, and also, as Mark has mentioned previously, the hospital episode.  So when patients needed to be... we can see their data in terms of admissions, investigations, and so on.  And these are really valuable data points, because you get an indication of when patients may have had to then have further testing, or if there is a risk of recurrence and importantly survival data, because a lot of this has been, in terms of a lot of the cancer genes have been well characterised and tested.    But what we were able to do here at a pan-cancer level on a large cohort of patients over a period of time, is to look at if you had a particular mutation, what is the impact of that in terms of outcome for a particular cancer type, and even more broadly, on a pan-cancer level?  And actually, as this type of data accumulates, I think the real value, and if you've got a larger number, you know, what is the value for patients who've participated in this programme going forwards, is that as that data accumulates and the numbers go up, we are able to then ask more detailed questions.  What is the impact of a particular type of mutation, or a particular type of variant within a gene?  And, importantly, what happens when you get a different sequence or a combination of genes?  And how does that impact?  And this, I feel, is the way that we are going to move more towards precision oncology, because we are beginning to understand the cancer in more detail, how it is going to behave, and then try and tailor therapies accordingly.   Naimah: And Nirupa, I wondered if you could tell me as well if the findings from this study have benefited directly those patients that were involved in the 100,000 genome project?  Nirupa: It has benefited some of the patients because, as Mark has mentioned, there are findings that we weren't expecting in terms of potentially inherited cancers and, therefore, this has had implications.  The way that the project was set up from the outset, is that we were obtaining tumour samples from patients who had not received any previous cancer therapy.  And what this meant is that this was predominantly in patients, so they were treatment naïve with early stage disease that were having surgery to treat their cancers.  And as such, what we know is that fortunately most of those patients did not require further therapy, because their cancers were treated successfully with surgery.  But what it did tell us, and what it's really highlighted, is the number of important genes that were identified.  And so whilst it may not have impacted patients directly, it's enabled us to study the biology of the different types of cancers, how they behave, along with the longitudinal clinical data.    But what it is doing now, is through the national test directory through the genomic medicine service, is enabling testing for patients that unfortunately now have more advanced cancers, but where these genomic findings are more likely to impact directly in terms of therapy.  So, for instance, as we've mentioned, the ability to have whole genome sequencing for patients with high grade serious ovarian cancers, means that this will impact the type of treatment they have.  And this also was the tumour type where we found the highest number of patients with BRCA mutations, so we have a potential inherited risk of a cancer as well.  So now what we have learnt and the infrastructure that we have developed has enabled this to have a real impact, not just for patients in the project now, but wider within the NHS.  Naimah: Mark, would you like to add something else there?  Mark: I think Nirupa's encapsulated it very well.  There were a range of benefits, so I mentioned earlier that in one centre 25% we have evidence got a benefit for their treatment for their cancer in some way shape or form.  So an example to what there might be is that some people got a medicine they wouldn't have received from routine care, and that might have been licensed for the treatment of that tumour, but it wouldn't have been the first line treatment choice.  Some people got medicines that they wouldn't have got because we don't normally associate using that medicine with that cancer, but they had a signature that showed that they were very likely to benefit.  Quite high numbers got an opportunity to get into a clinical trial, which is really important because if you look, over 50% of global oncology trials now have some kind of biomarker or diagnostic, or something like this alongside, what better than to have a comprehensive inventory of the variants and the cancer, and to be able over time to use that library to understand better the treatment course of that patient.  And that's what I think a whole genome adds, rather than the single, look at a single part of the genetic makeup.   And then finally, some had lots of mutations, really high rates of mutations, and maybe they should receive specific advance therapies, like immunotherapies.  Or alternatively, they had a feature in their genetic makeup which it looks like they inherited, as Nirupa absolutely correctly said earlier, these people need to be followed-up and they need more intensive screening, because this is how you detect cancer at an earlier stage.  And the final way people benefited is we could detect genetic changes in  their DNA that meant that if they were exposed to certain medicines, they were likely to suffer harm. And there's a particular, two medicines, 5-fluorouracil capecitabine, where possibly about 5% of people will need either a reduced dose or a completely different medicine, because it will be very harmful.  And so this is about getting the right medicine to the right patient first time, and getting the right outcome for that patient downstream.    And I think, you know, Nirupa's encapsulated it perfectly, there's a whole range of benefits that the patients can accrue from this.  And I think we should probably, Nirupa, say that people were quite cynical when we started, about what it would be that you would get over and above, for example, the cancer genome map that's at the international cancer genome consortium.  And, you know, I'd had leading cancer scientists in Britain say, "Oh well, we've discovered it all, there's nothing to find here."  And I think what this paper shows is that's not entirely true.    Nirupa: I would agree with that Mark, but I would also probably add that it highlights the value of having a large data set alongside that clinical information.  And what we were also able to do,  is whilst we very much talked about what were the gene targets that had a direct impact or genomic markers that impact care now, for which there is an approved therapy.  What we've also been able to do through this analysis, is actually highlight the number of mutations that have been identified for which there is a licence therapy in another cancer type, but not in that particular cancer type.  And what that means, is that specially now, as we have more and more biomarker-driven therapies, I mean, if we look at that compared to when the project started and now, that has increased dramatically.  And what that means is then there are sort of licensed medications that actually can be used in non-licensed indications via a clinical trial, via these very, you know, these basket studies which are across cancer types and are actually based on different types of molecular markers.  And really, we're able to show this at a pan-cancer level across the 13,000 tumours through the results from whole genome sequencing.  Naimah: You've both kind of touched on this throughout and, you know, we've talked about the development of personalised medicine.  And where do you see the future of cancer treatment in the next five years?  Maybe, Nirupa, we can go to you first?  Nirupa: That's a very good question.  I think and what I hope is that with more comprehensive and equitable and standardised testing for patients, especially within the NHS, that this will enable more personalised and targeted therapy alongside, you know, systemic chemotherapy.  And as well as that, better selection of patients that are likely to benefit from the newer immunotherapies.  And also where sequencing is very exciting, is that once we begin to understand more about the individual tumours, you know, going forwards there are a number of cancer vaccine trials, and the aim of those are to have specific vaccines that are going to target an individual's tumour.  So I think in the next five years, this is I think a very exciting space, I hope so, because we need to keep doing more in the space for our patients to try and improve therapy and precision oncology for them.  Naimah: And Mark, do you have anything to add to that point?  Mark: I think Nirupa's right, that there are new therapy extractions coming on, vaccination's one way.  But I think that what will become clear is whether we can use any molecular mechanisms for early detection of cancer.  The battleground here is that we all too often detect cancer late, when it's already outside of the organ it originated in and may be spread in other parts of the body.  It's very hard to effect a cure, almost impossible in that setting.  But what if we could detect cancer earlier?  And then what if we could place a whole genome or detailed molecular characterisation alongside that?  And then, as Nirupa suggested, give someone a vaccine tailored to their tumour that would eliminate it.  The real problem is all too often we detect cancer late, so maybe some of these new molecular diagnostics, such as cell-free tumour DNA will usher in an era of early detection.    And one of the things, and particularly before we did this project but also up until the beginning of the last decade, there were very few good biomarkers of cancer that were usable in the health system.  So we have for the first time opened the vista of having early detection, if we combine early detection with detailed molecular characterisation, possibly a whole genome, possibly another test, then I think we really can usher in the era of precision medicine.  And so I think Nirupa's absolutely right, there will be new treatments, there always will be, but what we have to do is to get detection at an earlier stage.  Naimah: We'll wrap up there.  Thank you to our guests, Dr Nirupa Murugaesu and Professor Sir Mark Caulfield for joining me today.  If you'd like to hear more about this, please subscribe to the G Word on your favourite podcast app.  Thank you for listening.   

 JCO PO author Dr. Mary Redman shares insights into her JCO PO article, “Representativeness of Patients Enrolled in the Lung Cancer Master Protocol (Lung-MAP)” Host Dr. Rafeh Naqash and Dr. Redman discuss the background of LungMAP and how it was developed to accelerate drug development and biomarker-driven therapies in lung cancer. Dr. Redman shares the initiatives undertaken to increase participant diversity in LungMAP and invites junior investigators to get involved in the project. TRANSCRIPT  Dr. Rafeh Naqash: Hello and welcome to JCO Precision Oncology conversations, where we bring you engaging conversations with authors of clinically relevant and highly significant JCO PO articles. I'm your host, Dr. Rafeh Naqash, Social Media Editor for JCO Precision Oncology and Assistant Professor at the OU Stevenson Cancer Center. Today I'm delighted to be joined by Dr. Mary Redman, Professor of the Clinical Research Division at the Fred Hutch Cancer Center and also Senior Author of the JCO Precision Oncology article, “Representativeness of Patients Enrolled in the Lung Cancer Master Protocol” or the Lung-MAP. Our guest disclosures will be linked in the transcript. Dr. Rafeh Naqash: Dr. Redman, welcome to the podcast, and thank you for joining us today. Dr. Mary Redman: Thank you very much for the invitation. Dr. Rafeh Naqash: And for the sake of this podcast, we'll just use each other's first names. If that's okay with you. Dr. Mary Redman: Please. Dr. Rafeh Naqash: And since you and I know each other through the lung working group, we've worked on some things, or planning to work on some things, this article was something that I came across recently that you published with some very well-known folks in the field of lung cancer. And I wanted to utilize the first few minutes of this discussion to understand what was the background behind Lung-MAP because I think it's very important for people to understand why this kind of an approach was started in the first place and how it has successfully created a mechanism for master protocol. So, if you could dive a little deeper into that for us, since you've been there, you've done that, and it would help our listeners understand the genesis or the origination of this whole process of Lung-MAP. Dr. Mary Redman: Happy to do so. So, Lung-MAP, the original concept goes back to February of 2012. And in February of 2012, the Thoracic Malignancy Steering Committee, the FDA and the NCI had a workshop. And the focus of the workshop was how we could accelerate drug development in lung cancer, and in particular, how we could accelerate biomarker driven therapies within lung cancer. And the outcome of that meeting was that master protocols or studies that set up infrastructures to evaluate multiple therapies, all within one infrastructure, were the way to go. And so born out of that, there were three master protocols. The Lung-MAP trial, the ALCHEMIST trials to evaluate studies in adjuvant therapy setting, and then the MATCH trial, which, of course, isn't just in lung cancer, it looks across different cancer types and looks on biomarker targets that transcend across.  So, when the Lung-MAP trial was being thought of, the idea was that while in non-squamous, non-small cell lung cancer, we had seen some advances with targeted therapies, that squamous cell lung cancer had essentially no targeted therapies that had been successfully evaluated. And therefore, there was an unmet need that squamous cell lung cancer being a more aggressive form of lung cancer than non-squamous lung cancers, and in particular in the second line setting, after patients had received platinum-based therapy, there was pretty much nothing other than docetaxel.  And so, the study was initially conceived of by Vassiliki Papadimitrakopoulou, who was at MD Anderson at the time and Roy Herbst who we had at Yale. And so therefore, we thought second line squamous cell lung cancer was an unmet need and that we could potentially have targeted therapies, given now that we had the genome atlas, the TCGA understanding of what all the potential biomarkers or targets that exist in squamous cell cancer. Concurrently, we also had the developments and improvements in next-gen sequencing. So, the technology improved for us to be able to detect these different genomic alterations that were present in these cancers. So, all of that together - an unmet need of an aggressive cancer, a better understanding of the biology and the potential to have these targeted therapies - led to the development of Lung-MAP. But in addition, what we had seen and I think most of you who have studied cancers across the country know, patients who live in urban areas or are financially more well off are more able to access therapies, whereas patients who are less well off, more rural areas, and then just in general, different race ethnicities, didn't have the access that other patients from other settings had. And so, when we conceived of Lung-MAP, it wasn't just about meeting the unmet need in terms of treatment, it was also about an unmet need in terms of accessibility of these types of studies for all types of patients who get lung cancer. And so, utilizing the National Clinical Trials Network system that has sites all over the country, I think there's something like 2500 sites around the country, which include community oncology sites and of course academic sites.  Dr. Rafeh Naqash: Excellent. Thank you so much Mary, for explaining that. Now, as you highlighted, this dates back to 2011-2012, when things were just picking up from a broad sequencing platform standpoint, rather than limited gene testing, which has been more and more, there's been more and more uptick of NGS, especially in the space of lung cancer. So, you and several others came up with this idea and eventually implemented it. And there's a significant process of thinking about something and implementing something. So, what were some of the challenges that you encountered in this process and successfully circumvented or dealt with appropriately over these years, some of the lessons or some of the processes that you were able to understand and navigate around. Dr. Mary Redman: We could spend the next hour probably talking on that topic. Anytime that you're setting up a big infrastructure, and I really do think the best way to describe Lung-MAP and a master protocol is that it's an infrastructure because the goal is to set up something where we can bring in new studies and so that everything is modular. And you complete one study, you add a new one. Things can be added while things are ongoing. And by things, I mean studies evaluating investigational therapies.   And so, anytime you're setting up an infrastructure that's never been done before, well, first of all, the complexities of different partners that had never worked together, so just understanding how best to work together, the infrastructure in terms of how to build it within our systems, the statistical and data management center had many complexities. The infrastructure in terms of how our systems at the statistical and data management center spoke to the NCI had challenges. How the NCI evaluated this protocol that had all these different studies that were coming and going.  The studies oftentimes involved therapies that were very new in their development. And so oftentimes you'd have some new safety signal that came up which required a rapid amendment. And how do you do that when you have this infrastructure, and you don't want to stop one thing for other studies to be moving forward. And that because it's a public-private partnership and the pharmaceutical partners that are partially supporting financially and scientifically, some of these studies, learning to work with them, they have a little bit more say because they are more financially involved with the studies than a study that's typically funded by the NCI. And maybe the company is only supplying drug. So, contracting had its challenges, budgets, how do we actually budget things appropriately in this new infrastructure? I talked about all of that. And then a challenge about running such a study is how do you educate the sites so that when they're approaching patients, how can they talk to a patient about, “You're going to have your tissues submitted to be tested, and then on the basis of that tumor testing you're going to be assigned to get to an investigational treatment study.” And how do you describe all of that? Dr. Rafeh Naqash: So definitely lots of lessons and experiences that you and your team have had. And the way I describe or look at Lung-MAP is one of those success stories that has redefined the way to run clinical trials from an NCTN and a SWOG cognitive group network standpoint. And going to this paper that you have published in this, your Precision Oncology, there's one aspect of clinical trials where we are always very focused on responses and survival and other clinical outcomes data. And then there is this important component that you and your team have looked at is, what is the distribution of the different kind of clinical trial participants? What kind of people are we getting in? What kind of people are we trying to cater to, and what is the unmet need gap that we still have not completely met? Could you tell us how this project started, the idea behind this project, and then some of the results that you can highlight for us today? Dr. Mary Redman: So, Lung-MAP also has a company advisory board, and we meet with them either quarterly or biannually. And one of the conversations that we were having with our industry partners or collaborators was especially after the FDA came out with some of their work saying, we think it's really important that industry does better that they enroll a more representative patient population in their studies. You see some of these studies in lung cancer with 1% or a very small percentage of Black participants, for example, whereas the US population has significantly higher levels. And so, one of the major objectives, as I said about Lung-MAP, was to enroll a more representative patient population to provide access. And as part of these conversations, we kept saying, “Well, we've done a better job.” And I was thinking, well, we actually could evaluate how we have done. And so, in thinking about that, I proposed within some of the researchers that are part of the SWOG Statistical and Data Management Center that we look at this question in particular, I approached Dr. Riha Vaidya, who is here at Fred Hutch with me, and she's a Health Economist with this idea. And she was very excited to look at this. And my initial thought was just to look at race, ethnicity, gender. And she took it one step further where she wanted to look at not only that, but also area deprivation index and then rural versus urban. So, getting at some of those other very important aspects of representativeness when we think about patient populations. And so that was how it came about.  Dr. Rafeh Naqash: Going back to some of the interesting things that you and the authors have done, is not only looked at the gender, age, but also looked at the socio-demographic representativeness. Now, there's definitely some things that you guys looked at and that Lung-MAP study did better on, and some things where maybe there's more room for improvement. Could you highlight some of those results for us today? Dr. Mary Redman: Happily. And one thing I think that it's important if one goes and looks at this paper, and as I talk through the results, so Lung-MAP opened to enrolling patients in June of 2014. And from June of 2014 to January of 2019, we exclusively enrolled patients with squamous cell histology. And then in 2019, we expanded the study to enroll all histologic types of non-small cell lung cancer. And so, in this paper that's published here in JCO Precision Oncology, we compare our patient population and Lung Map to other patients enrolled within advanced non-small cell lung cancer trials. So that's all-histologic types. And then we compared it to the SEER population, the US population evaluated by SEER. And that also is all histologic types of non-small cell lung cancer.  And so, one of the major results, as you pointed out, is that while we did well in certain areas, for example, we did not enroll as many females as the other SWOG trials and then the US population. And I think that is probably, I would attribute all of that to being the case that squamous cell lung cancer patients tend to be more male than female. So therefore, those results, I don't know that if we looked at only the data since 2019, we might actually see that we were comparable. Going through the results, as you were just asking about, compared to previous SWOG trials, we did better in terms of enrolling older patients, not as well as the SEER data. Some of the challenge is I'm not 100% clear that we'll ever be able to get perfectly there, in part because Lung-MAP, for the majority of the time, only enrolled patients who had performance status 0 or 1, and older patients tend to have higher performance status, and so they might just not have been eligible. And I do think, especially with these investigational treatments, particularly with immunotherapies, for safety reasons, we do need to enroll patients with performance status 0 or 1. We talked about the female sex versus male sex percentages and that our numbers were smaller. But if you look at SWOG trials versus SEER trials, they're pretty much identical numbers. So, I think that if we just looked at the later part of Lung-MAP, you'd see that they match. In terms of race ethnicity, the earlier part of Lung-MAP, we enrolled close to 15% of patients of nonwhite race or ethnicity. Historically, SWOG trials were slightly higher, but in the US population, it's around 21.5%, based on this year's data. And so, we did better than industry sponsored trials. So, if you look at those data, but there's definitely room for improvement. And that just in part, has to do with getting more sites, better outreach, more education, and better access.  And so, I think we have an accrual enhancement committee that does include patient advocacy groups. And I think that that is just going to be an area that we need to continue to work on. And then, as you mentioned, that we did better in terms of enrolling more patients from rural areas. We enrolled more patients from socioeconomically deprived neighborhoods, and more patients that were using Medicaid or no insurance for those who are under 65. Dr. Rafeh Naqash: Absolutely. I think those are very important results. Me, as somebody who sees people on clinical trials, both phase I and late phase, of the questions that I get commonly asked if somebody refers a patient from the community is, “Am I going to be treated on a placebo?” It's one of those common things. And the second question ends up being like, “Is my insurance going to cover some of the costs associated?” And I think understanding those concepts, whether it's from an educational standpoint or a financial barrier standpoint, is extremely important in clinical trials because at the end of the day, these are things that people use as metrics for enrolling or not enrolling themselves on a clinical trial. There are certain aspects or sensitivities associated with enrolling people, let's say, of Native American ethnicity or American Indian ethnicity, where outreach is extremely important. From a Lung-MAP standpoint, could you talk about some of the outreach initiatives that are being implemented or have already been implemented to potentially help decrease this gap of representation?  Dr. Mary Redman: I think that one of the major- and this isn't exactly outreach, but to start out with one of the things that we have, in addition, I mentioned that we had an accrual enhancement committee. We also have a site coordinators committee. And when we set up the site coordinators committee, we make certain that we have representation from the geographic regions within the country and different types of sites. And the major goal for our site coordinators committee is to give us input about how it is to implement Lung-MAP within their own institutions. And so, we want to be able to overcome any type of barriers or perceived barriers that are out there, and we want to hear it directly from those people who are working closely to enroll the patients. And so that's been a key part of everything that we've done. And so, part of that is that we've just developed educational materials. We have modified the protocol based on input that we've received from them. So that's, I think, been a major approach that we have used to try to reach more patients.  We do have a newsletter that we put out. The accrual enhancement committee has also contacted different sites to really have more conversations, one on one, just more, I guess, almost like focus type groups where you try to understand, really understanding what's coming on, what are the challenges from their perspectives. And then we've had webinars where we try, and we've had hundreds of attendees for these webinars, where we let the sites have direct access to those of us who are running the study to ask their questions. So those have been our major approaches. And I think that we're always trying to figure out how we can do better.  Dr. Rafeh Naqash: I agree with you, and I think as both physicians, providers, and the clinical trial staff as such become more and more cognizant of increasing diversity, these conversations end up happening earlier and earlier in an individual's patient's journey, where trying to see feasibility, trying to see financial aspects, getting a patient enrolled on a clinical trial gets evaluated earlier and earlier. And hopefully, with some of the measures that the SWOG or the Lung-MAP group is implementing, these percentages will see more spike in the long run for better clinical trial enrollment approach. So, Mary, now going to the science part of Lung-MAP for maybe some of the fellows or the investigators, early career investigators, who might be listening to this podcast, could you briefly explain what is the process of getting involved in Lung-MAP? Because for me, as a junior faculty a few years back, I was a fellow, and I remember at that point I hardly had any knowledge of corporate groups. SWOG, for example, was one of those that I'd heard about, but didn't necessarily know how to get involved. So, for trainees, for junior faculty, could you briefly say, what's the process? What does it involve? How would somebody propose something to Lung-MAP?  Dr. Mary Redman: Yeah, thank you for that question. And I really do hope that this actually is a way to get people to understand, and we'd love to have more engagement from more junior faculty and that's a major objective for the study. Because this infrastructure is in place, we are actually well suited to be able to mentor and bring junior faculty in. And so, the process is basically, you contact any of us that are in leadership within Lung-MAP and talk to us and we'll see if we can figure out a way. If you have an idea of a new study, wonderful. Our drug selection committee chair is Saima Waqar. She's a member of ASCO as well. I mean, one could find her and send her a note. The study chairs for Lung-MAP are Hoss Borghaei and Karen Reckamp. You can send them a note. You can send me an email, maryredman@fredhutch, and we will make certain that you are engaged and brought into the direct conversations that would lead to something.  So, it would be wonderful to have more junior faculty proposing ideas and leading sub studies, being a sub study chair. Each of our sub studies, as I mentioned before, are conducted independently, and then you are responsible for the development, conduct of the trial and writing of the paper and presenting. And so, we want all of that to happen. But we also would love to have ideas. If you think of this infrastructure as just being an amazing resource of data, we are happy to and would love to receive proposals for data analysis that could result in publication and presentation as well. So, if there's something that somebody sees as a question that they think we could answer, again, contact any of us and we will happily figure out a way how to work with you. We have a great team and a lot of capacity to be able to work with new people.   Dr. Rafeh Naqash: Thanks, Mary. And for all those listeners, trainees listening, you did get Mary's email, so try to send her an email, and hopefully she won't be complaining that there was a lot of requests. But I think all things considered, the Lung-MAP is a great data resource. As you mentioned, it's a great resource for junior investigators who are trying to build a career around clinical trials, precision medicine, and it's also a great resource, as you've shown, regarding diversity equity research from a clinical trial standpoint. So, I think it has all the components that are needed to run and create some interesting questions and answer those questions using the data set.  So now, Mary, going to the last part of the discussion here, one of the key components, we try to ask a few questions of the investigator, which in this case is yourself. Could you tell us briefly about your career trajectory, how you ended up doing what you're doing now, and what are some of the things that you've learned from and maybe advice to all the junior people listening to this podcast?  Dr. Mary Redman: Wow. Okay. Well, so if you hadn't already guessed, I'm a biostatistician. I started out in mathematics as an undergrad and then learned about biostatistics and thought that it sounded perfect for me. After I finished my doctorate, I did a year of postdoc and was starting to look for faculty positions. And if you haven't already inferred, I am a Seattle native. And so, when a position became available at the Fred Hutchinson Cancer Center here in Seattle, I applied for it, and the job happened to be with the SWOG Statistical Center. And so, you probably already guessed as well that I got the job. And so, I have been here at Fred Hutch since 2005. And when I joined Fred Hutch and the SWOG Statistical Center, which is co-located here and at Cancer Research and Biostatistics, just a mile west across Lake Union here in Seattle, the person who had been the lead statistician for the Lung Cancer Committee in SWOG, John Crowley, he was also the director of the SWOG Statistical Center and had been doing that for over 20 years, and he was ready to take some things off of his plate. And so, when I joined, they thought that I would be a great fit for the lung committee, in part because I had shown an ability to work with vibrant personalities, let's just say, which the lung community has in spades.  And so, when I started in the lung committee, David Gandara was the chair of the lung committee. And so, I worked for many, many years very closely with David, and we established a very close and really wonderful working relationship. And I learned a lot from him. I learned a lot from a lot of the other lung cancer researchers in the country and around the world. I pretty quickly became involved with the International Association for the Study of Lung Cancer and have attended most of the World Congress on Lung Cancer meetings and have gotten to know people around there. So as a biostatistician, obviously, I enjoy my mathematical and statistical skills, but I also just really enjoy learning and thinking about what I can bring to the problem where I come from a certain point of view and I love collaborating with the other people doing clinical research, in particular in lung cancer. And basically, my focus has always been on doing the best to answer our questions the most efficiently and effectively so that we can move the field forward and help people live longer. Dr. Rafeh Naqash: Thank you so much, Mary, for your time and giving us insights into your professional and personal journey. Also, thank you for listening to this JCO Precision Oncology conversations. Don't forget to give us a rating or review and be sure to subscribe so you never miss an episode. You can find all ASCO shows at asco.org/podcast. The purpose of this podcast is to educate and to inform. This is not a substitute for professional medical care and is not intended for use in the diagnosis or treatment of individual conditions.   Guests on this podcast express their own opinions, experience, and conclusions. Guest statements on the podcast do not express the opinions of ASCO. The mention of any product, service, organization, activity, or therapy should not be construed as an ASCO endorsement.    

Dr. Shannon Westin and her guest, Dr. Nanda Horeweg and Dr. Carien Creutzberg, discuss the paper "Molecular Classification Predicts Response to Radiotherapy in the Randomized PORTEC-1 and PORTEC-2 Trials for Early-Stage Endometrioid Endometrial Cancer" recently published in the JCO. TRANSCRIPT  The guest on this podcast episode has no disclosures to declare. Shannon Westin: Hello everyone, and welcome to another episode of JCO After Hours, the podcast where we get in-depth on manuscripts published in the Journal of Clinical Oncology. I'm your host Shannon Westin, Social Media Editor for the JCO and GYN Oncologist by trade. And I'm so excited about today's topic because it is a GYN Oncologist dream. Before I start, please note that none of the authors have any conflict of interest. We are going to be discussing molecular classification predicts response to radiotherapy in the randomized PORTEC-1 and PORTEC-2 trials for early-stage endometrioid endometrial cancer. And this was published in the JCO on September 20th, 2023. And we're going to be speaking to two of the lead authors. First is Nanda Horeweg. She's a senior researcher in the Department of Radiation Oncology at the Leiden University Medical Center in the Netherlands. Welcome. Nanda Horeweg: Thank you. Happy to be here. Shannon Westin: And Dr. Carien Creutzberg. She's professor at the Department of Radiation Oncology at the Leiden Medical Center as well. Carien Creutzberg: Thank you. Shannon Westin: So, let's get into it. And I want to really level set because we have a mixed audience here. So, why don't you start by speaking about the incidents and mortality of endometrial cancer? Nanda Horeweg: Yes, of course. Endometrial cancer is the sixth most common cancer in women with around 400,000 new diagnoses made globally each year. And a woman's lifetime risk to get endometrial cancer is around 3%, and the median age, the diagnosis is 61 years. Most of the women who are diagnosed with endometrial cancer are diagnosed at an early stage, around two thirds, and they have an excellent prognosis. Actually, the five-year survival rates are around 92%. For stage 2 disease, this is actually already going down a bit to 74%. Therefore, stage 3 disease is only 48%. Women that are diagnosed with advanced disease have only a five-year survival, 15%. Shannon Westin: So, given that we know the majority of endometrial cancers are diagnosed at this early stage, prior to your evaluation, what was known about the optimal way to treat this early-stage patient population? Carien Creutzberg: Well, of course, the PORTEC trials were done … were started PORTEC-1 in the 19th of the last century, and PORTEC-2 in 2002. So, at that time, there were still many, many women treated adjuvantly with external beam radiation therapy. And we just developed risk factors to decide on their risk and the incidents for radiotherapy. And in PORTEC-2, because in PORTEC-1 we had seen that most of the recurrences in these early stage cancers were in the vaginal fold, we compared local vaginal brachytherapy only three sessions within full course of pelvic radiotherapy and showed that it had similar pelvic control and survival. Of course, this study, which Nanda conducted, was a long-term analysis with many new factors known from the translational research in the tissue samples of these patients who participate in PORTEC-1 and 2. And in the meantime, we've developed much more knowledge on the molecular factors and other important factors such as LVSI, which tell us much more about the individual prognosis to patients. So, the treatment has been developing greatly in the past 20 years. Shannon Westin: Yeah, and I think this is a great case of less is more, right? We were doing so much for so many people that really didn't need it. And so, really tailoring who needs less treatment, who doesn't need any treatment, and then also, conversely, who may need more treatment that would be missed by the traditional risk factors that you're speaking of. So, I think that brings us right into my next question, which is just bringing the audience up to date on the cancer genome atlas and how that's changed the way we classify endometrial cancer. Nanda Horeweg: Yes, I think the molecular classification of the TCGA has shaped the way we think about endometrial cancer, and has huge impact on decisions on adjuvant treatments in the years to come. The TCGA performed an extensive characterization of the endometrial cancers and found that in fact, this disease exists of four different groups. And the first of the groups I'd like to discuss is the ultra-mutated group, which is characterized by POLE mutations. And this group is shown to have an excellent prognosis in many independent studies. A second group that also has a high mutational burden is characterized by microsatellite instability, and mismatch repair deficiency and has shown to have an intermediate prognosis. Then there's another group that has a low mutational burden with high copy number alterations and frequent TP53 mutations, and these have a poor prognosis. And then lastly, there's a group that does not have any of the classifying features and is often called non-specific molecular profile or TP53 wild type. And this group also has an intermediate prognosis. And then finally, there's a small group of cancers that has more than one of these classifying features, the so-called multiple classifiers. And the WHO 2020 has developed an algorithm which can be used to classify them into the four groups. And that's first on the POLE status. And for the POLE wild type tumors, they are assigned according to mismatch repair deficiency status. And for those that are mismatch repair proficient than POLE wild type, they are classified according to the TP53 status into NSMP or p53 abnormal. Carien Creutzberg: Yeah, that is because of in the ultra-mutated and hyper mutated groups, many of the other mutations are secondary mutations in the context of the ultra-mutated stage, and they behave like the first molecular group. Shannon Westin: Yeah. So, that POLE mutation is going to trump anything else, and it's so important. And I will just say as a sidebar, it's been challenging with the price of next gen sequencing sometimes to get that for everyone. So, sometimes for us when we see a p53 mutation, we actually go back and do the full next gen sequencing to make sure that we're not going to act on that core prognostic feature when it really is in the setting of that more simplistic or that more positive prognostic place. So, this is great, we already kind of highlighted a little bit PORTEC-1 and 2, but if you don't mind, I would love to get the audience a little bit more information just maybe about the populations that were included as we were figuring out how aggressive to be with radiation just to remind people of that, or to teach them that if they haven't gotten a chance to look at those studies. Carien Creutzberg: Yeah, that's important to know because PORTEC-1 was still in the era that we also treated intermediate risk stage 1 endometrial cancer patients. So, deep invasion with grade 1 and 2 or superficial invasion with grade 2 and 3. That's what we defined at that point. Then we compared external beam radiation or no further treatment, showing no survival difference, but a higher risk of recurrence with higher risk being older age over 60, grade 3 for deep myometrial invasion. And we kept those high intermediate risk factors as also similarly found by GOG-99 at the time to do PORTEC-2. So, at the time, about 50% of patients did not have an indication for adjuvant therapy anymore, and with a high intermediate risk population for PORTEC-2, we compare external beam or vaginal brachytherapy and found the benefit of vaginal brachytherapy. A simple outpatient treatment, very short with almost no side effects ensuring local control. And nowadays, using the molecular classification of PORTEC-4a, we've compared achieving treatment with or without use of the molecular factors to designated treatment. So, the standard arm is vaginal brachytherapy and investigational arm is first, a molecular risk profile. And then we give no radiotherapy for those with a favorable profile, then a brachytherapy for the intermediate ones, and for the small group is either extensive LVSI or TP53 mutation or L1 chem overexpression external beam. And we hope to show that less overtreatment and less undertreatment will benefit these patients. Shannon Westin: Yeah, I'm very much looking forward to the results of PORTEC-4a. But let's circle back and talk a little bit about your amazing work here. So, how did you leverage those patient populations from PORTEC-1 and 2 for the current study? Nanda Horeweg: Yes. Well, the PORTEC-1 and 2 study provided a unique opportunity to look into differential treatment effects for radiotherapy. And that is because these are randomized trials, so the groups are comparable, and we have long-term follow-up data that's of very high-quality. In addition, as Carien said earlier, she had the vision already back in the nineties to directly ask the patients permission for the collection of the tissue. So, we have a broader complete biobank for both of these trials, which is quite unique. And our colleagues, Professor Smit and also Charlene Goseff from the pathology department, they have done extensive work on molecular classification, and have molecularly characterized all these cases. So, this allowed us to include 880 patients in this study, which is the largest so far. And besides like the very good starting point that we have of PORTEC-1 and 2 is that we also chose a design that was optimized to conduct like real causes, the causal effects of the molecular class on radiotherapy response. So, we tried to preserve this randomization effect, the exchangeability of the groups as much by working with the intention to treat population and not excluding any patients, except for when they did not have the molecular classification assessed. And also, we looked at areas in the body that were irradiated in one group and not in the other one to really observe the effect of radiotherapy as much as possible. So, looking to the entire pelvis, so local and regional recurrences in PORTEC-1 and looking at pelvic recurrences in PORTEC-2. Shannon Westin So, how were the intervention outcomes in this study different based on the TCGA classifiers? Nanda Horeweg: Before I tell you the results of biomolecular group, I think it's good to have the starting point of the analysis here. So, the no hypothesis of my study was to see whether there was any difference, and no hypothesis is that there's no difference. So, if we find a significant effect, then we can actually say that we found something. And if we start with the POLE group, we did not find any significant difference between the groups allocated to radiotherapy or not. But we did see not a single recurrence in any of the patients that we included from both of these trials. So, technically speaking, we did not find a predictive effect of the molecular classifier, but a prognostic effect. There's no one's having recurrence, so we can deduct from that, that radiotherapy is probably over treatment. Then for the MMRd group, we did observe some recurrences, but these were not significantly different between these three groups. So, based on this study, we cannot draw conclusions on which type of radiotherapy we should give to the patients or whether we should give radiotherapy at all. This was very different for the p53 group. There, the patients had lots of recurrences, unfortunately, as we expected, but we saw a big difference in outcome compared between no radiotherapy at all if it's vaginal brachytherapy where we still had lots of recurrences, and EBRT where we hardly saw any recurrences in the pelvis. And that difference was significantly different. So, that's an indication that these patients need more than just vaginal brachytherapy, even though it's only stage 1 endometrioid endometrial cancer. And then in the last group, the NSMPs, we saw even a different pattern where patients who had had external beam radiotherapy or vaginal brachytherapy, both had an excellent local regional control, and the ones that did not receive any treatments had more recurrences. And this was also very significant. So, there, you would conclude that both therapies are appropriate, but of course, the toxicity profile for vaginal brachytherapy is much more favorable than that of EBRT. Shannon Westin: We really are getting kind of consistent data around p53 needing more treatment. And I think the natural question that comes here, for me at least, and I know we can't answer it with the work, is would chemo be — would that be that extra treatment, when we saw with PORTEC-3 that the group needed the chemotherapy the most. So, I think we'll have to continue to work through that and determine is any more treatment what we need or specific treatments really the best. So, this is so intriguing and it's nice that it's consistent, that we're seeing that across these different studies that really kind of lends strength and validity, I think to what we're finding. So, one of the actions that we're kind of moving towards and that you advocate certainly in your paper is omitting therapy for patients with POLE mutations. Are there any ongoing studies around that that will help us confirm that this is safe for our patients? Nanda Horeweg: Yeah, that's a very good point. I think the evidence is strong enough now to conduct prospective trials. And of course, these are ongoing, the PORTEC-4a trial was already briefly mentioned there. The patients with poor mutations will be randomized between observation and vaginal brachytherapy. So, that will give us a good indication whether in this high intermediate risk early-stage group omission is safe. And in addition to that, we are also conducting with the RAINBO Consortium, the RAINBO-BLUE trial, wherein patients also with high-risk features, so non-endometrioid isotypes, LVSI and higher stages are included. And also in those patients, we investigate whether the de-escalation of treatment is safe. So, we're definitely looking forward to those results to be able to transfer this knowledge to clinical practice later on. Carien Creutzberg: And maybe it's nice to add that RAINBO BLUE is connected to the Canadian Taper trial. Taper being a general de-escalation trial where the POLE patients in that trial are also feeding into the RAINBO-BLUE. And I know that in North America, many centers will participate in the Taper trial. Shannon Westin: Yes, I think everyone is very excited and I think it'll be nice to have these two very strong studies that will help us really confirm that that is 100% a test that needs to be done, cost are not — and that will help avoid overtreatment of patients. So, in line of that, have you all experienced any challenges with implementing molecular testing across patients with endometrial cancer? Any thoughts on how we could potentially simplify? You talked about the rational promise algorithm, which I think is excellent, but I'm just curious to hear your thoughts on this. Nanda Horeweg: The implementation of the molecular classification can be challenging. We have to be honest about that. And usually, it's the assessment of the POLE status that's causing the problems because that's usually done with NGS, which is quite expensive. It requires a lot of knowledge in the laboratory and it's also a bit time-consuming. So, that is the bottleneck for most laboratories and for most settings. But this is already changing in a couple of places, like in the UK and the Netherlands, it's being reimbursed by healthcare insurances, and also, in many tertiary care centers in other countries, they're already systematically performing this test. But of course, there will always be places where this is not feasible. And luckily, there are also cheaper alternatives coming up and are already available at the moment. So, one of them is, for example, standard sequencing, which is not so expensive, but a bit labor intensive. Some colleagues we work with from India have implemented that in their clinical practice and are perfectly able to molecularly classify the endometrial cancers in daily practice. Another alternative is a test that we've developed in Leiden that's called the QPOLE test, which is based on qPCR, so that's a technology which we use for our COVID test around the world, so that can be done almost anywhere. And with that, you have a very high accuracy to detect unknown pathogenic variants. And this is also published in JCO Global Oncology, and can be implemented in any center after a local validation step. And even like more companies nowadays are realizing that this is important. So, I think commercial tests are already becoming available and very more on the market soon. So, I am really hoping that it'll be more available to endometrial cancer patients. Carien Creutzberg: And they'll offer them at a very low cost and also a rapid turnaround because NGS can take like 10 days. But realizing on a more national level, if you have found one patient with a POLE mutation, the omission of cycles of chemotherapy with all of the patient care around in the hospital is worth much more than just a few POLE tests. So, we have to look at this and that's I think why our healthcare reimbursement came through that if you look at a population level, it is cheaper, and we'll do an extensive cost analysis in PORTEC-4 just to show this. Shannon Westin: That is such a good point. I love that and all of the downstream issues that happen potentially with radiotherapy or with chemotherapy, that's really brilliant. And I'm going to take that back, I love these podcasts. I always learn stuff that I immediately start to use. So, I guess then the last question is, what's next for this particular research and how might we validate what you found? Nanda Horeweg: Yes. Well, as mentioned earlier, for POLE, we have already put the next step in place. So, PORTEC-4a has completed accrual almost two years ago, and we're very much looking forward to do the final analysis within one to one and a half years. So, that will be one of the important next step. And of course, the POLE-BLUE trial is open at the moment, and within a couple of years, we also hope to learn more about this group. So, that's very exciting. Then for the mismatch repair deficient group, while we did not find any particular sensitivity for radiotherapy, and I also don't think that we will conduct another large randomized radiotherapy trial in this group — I think the results that we've observed in the metastasized setting, were really impressive results with immunotherapy are the way forward for this molecular class. And I think the next thing we should do now is prove whether this works or not in the adjuvant setting. And if that's starting with the high-risk patients, which is something we are currently doing in the MMRd-GREEN trial, which is ongoing in the Netherlands, and soon, will open internationally. And from there on, we can work forward if we see that also in this setting the immunotherapy works well. Shannon Westin: And I think GY020 also — NCI trial is also looking at the addition of immunotherapy to radiotherapy in that irony at risk. Carien Creutzberg: Absolutely. Nanda Horeweg: Yeah. And the KEYNOTE-B21 as well — oh, well, already complete accrue. Shannon Westin: The B21, yeah. So, I think those are good. Yeah, that's a really good point for that MMRd group that the immunotherapy really is the way to go, and then more work to be done with the no specific molecular profile. Nanda Horeweg: The NSMP, I think like for the early-stage group, it's quite clear that vaginal brachytherapy is a therapy of choice. But you can of course, try to identify those at such a low risk that you could deescalate treatment. And that's of course what's being done in the Taper trial and also in part, investigated in the PORTEC-4a trial. Carien Creutzberg: And those with higher risk NSMP that we are revisiting hormonal treatments because 90% are estrogen receptor positive, and they have a clearly better prognosis than those with estrogen receptor negative tumors. So, those with estrogen receptor positive tumors can in RAINBO-ORANGE, which will be run led by the UK group, see if we can improve quality of life with less intensive adjuvant treatment. And then you came to the p53 group, that's a good one to stop with. Nanda Horeweg: Yeah, we have very good indications that radiotherapy and chemotherapy is working well for this group. And this is also in line with the guidelines that have been issued in the last few years by many societies. So, I don't think we should change this base of the treatment consisting of radiotherapy and chemotherapy. But since the prognosis is still rather poor, we need to add systemic agents to reduce the risk of metastasis. And preferably, this should be like well-designed based on a proper biological underpinning, plus something that's not too toxic since we're combining the three therapies together. So, this is what we try to do in the RAINBO-RED trial where we will investigate the addition of a PARP inhibitor to chemoradiation in the p53 group. Shannon Westin: Oh, I love that. That's been my whole career, is adding PARP inhibitors wherever I can. Carien Creutzberg): We might also want to mention the HER2 inhibitors, which are also in about 20% of the p53 group has HER2 overexpression. And there is a trial being set up in NCI with trastuzumab and pertuzumab. Shannon Westin: My only concern with that one is I think that the antibody drug conjugates are so much more powerful, the TDX data that we just saw from DESTINY is so impressive. And so, I do wonder, like if we need to move on from kind of some of the older HER2, and get with the program and use some of these more powerful drugs. But with that, I just want to thank Dr. Creutzberg and Horeweg. This was such a great discussion, and obviously, near and dear to my heart talking about endometrial cancer, but I hope our audience enjoyed as well. Just as a reminder, this was a discussion on molecular classification predicts response to radiotherapy in the randomized PORTEC-1 and PORTEC-2 trials for early stage endometroid endometrial cancer, published in the JCO on 9.20.23. I am your host, Shannon Westin, and I hope you'll check out more JCO After Hours wherever you get your podcasts. Have an awesome day. The purpose of this podcast is to educate and to inform. This is not a substitute for professional medical care, and is not intended for use in the diagnosis or treatment of individual conditions. Guests on this podcast express their own opinions, experience, and conclusions. Guest statements on the podcast do not express the opinions of ASCO. The mention of any product, service, organization, activity, or therapy should not be construed as an ASCO endorsement.    

In this "Podcast Takeover," Dr. Lidia Schapira guest hosts to discuss with Dr. Shannon Westin her own JCO paper, which reports on the DUO-E Trial. Dr. Ramez Eskander also joins in this lively discussion. TRANSCRIPT The guest on this podcast episode has no disclosures to declare. Dr. Shannon Westin: Hello, everyone, and welcome to another episode of JCO After Hours, the podcast where we get in depth on manuscripts published in the Journal of Clinical Oncology. I am your host, Shannon Westin, Social Media Editor of the JCO and Gynecologic Oncologist by trade. And actually, I'm super excited today because we are going to have a podcast takeover because we are discussing my own work, which was simultaneously presented at the European Society of Medical Oncology 2023 Congress and published in the Journal of Clinical Oncology on October 21st, 2023. And this was the DUO-E trial, “Durvalumab Plus Carboplatin/Paclitaxel Followed by Maintenance Durvalumab With or Without Olaparib as First-Line Treatment for Advanced Endometrial Cancer.” Because we're discussing this work and we wanted you to have an unbiased podcast discussion, Dr. Lidia Schapira, who is a Professor of Medical Oncology at Stanford University and an Associate Editor of JCO and the Art of Oncology podcast host, is going to take over this podcast and really just pepper me with questions about this exciting work.  Welcome, Dr. Schapira.  Dr. Lidia Schapira: Thank you so much. It's such a pleasure to be with you. Dr. Shannon Westin: And before I turn over the reins, I also want to introduce one of my colleagues, who's going to be providing quite a bit of insight on this topic, Dr. Ramez Eskander, who is Professor of Obstetrics, Gynecology, and Reproductive Sciences at the University of California, San Diego. And you will know he's the principal investigator of the GY-018 study, which established pembrolizumab and chemotherapy as the new standard of care in endometrial cancer. Welcome, Ramez.  Dr. Ramez Eskander: Thank you. Thank you, Dr. Westin. It's a pleasure to be here. And congratulations again to you and your study team for this exceptional work.  Dr. Shannon Westin: Thank you. And congratulations to you.  Dr. Schapira, thank you for being here and please do take it away. Dr. Lidia Schapira: So let's start by having you tell us a little bit about the standard of care for women with endometrial cancer and advanced endometrial cancer prior to this study. Ramez, I'm going to direct this question to you first. Dr. Ramez Eskander: For many years, actually since about 2012, carboplatin and paclitaxel, which ironically is a chemotherapy backbone really across all of our gynecologic tumors, emerged as the preferred doublet chemotherapy regimen for the management of advanced-stage metastatic or recurrent endometrial cancer. It evolved through a series of different clinical trials, in fact taking us from whole abdominal radiation, systemic chemotherapy, comparing single agents to doublets and then triplet regimen of TAP to carboplatin and paclitaxel, which ultimately, then, following the presentation of GOG Protocol 209 and its publication, as the chemotherapy backbone, being carboplatin and paclitaxel. And it's been that way for many, many years. Dr. Lidia Schapira: And how effective is the regimen? Dr. Ramez Eskander: The response rates to carboplatin and paclitaxel are actually quite reasonable in the patients who have advanced-stage disease, particularly if they haven't had prior systemic chemotherapy. Response rates in the 50% to 60% range. The issue is that the responses tend to be limited and disease recurrence is an expectation in these patients who have advanced-stage disease. And so that really highlighted the importance of trying to continue to advance therapeutic opportunities in these patients to improve long-term outcomes.  Dr. Lidia Schapira: As we think about improved long-term outcomes, we're thinking about a better treatment and also a kinder treatment, perhaps one that is also less toxic. Can you talk a little bit about the population of women with endometrial cancer? Are these older women? Do they have comorbidities?  Dr. Ramez Eskander: What we're seeing is, interestingly, there has been an evolution a bit in this space. Historically, we used to think about endometrial cancer as—the phrases we used to use are type I and type II. These type I tumors, we would say, are estrogen-driven malignancies; they tend to be seen in overweight or obese patients. And we would identify them in a theoretically younger patient population. And then we had these type II, or what we termed estrogen-independent malignancies, that we would see in an older patient population. Of course, with obesity came metabolic syndrome and other cardiovascular comorbidities, etc. But really, that narrative has evolved dramatically, and that's really something that will be highlighted in, I think, our discussion of these studies today, where the nomenclature that we used to historically use has evolved because of our understanding of the molecular characterization of this disease. So we've really gone away from that, and now we understand that we're seeing all of these different heterogeneous endometrial cancer types amongst patients of different ages, different comorbidities, different races and ethnicities. And so it's created a more complex picture for us. But certainly, there are comorbidities that these patients face, and that's important as we look to identify treatments strategies that are both effective and tolerable. Dr. Lidia Schapira: My final question before we jump into this very exciting study is about the Cancer Genome Atlas work. Can you tell us how that's changed the thinking and the design of the studies? Dr. Ramez Eskander: It was a seminal publication, really, back in 2012/2013 looking at an assessment of endometrial cancers to try and determine whether or not all of these "endometrial cancers" that we used to enroll on a single study are similar or divergent. And it's important because the study I referenced that really established the standard of care, GOG Protocol 209, as carboplatin and paclitaxel, there was no real consideration of molecular characterization at all. We enrolled all patients onto this study without thinking about these variables, of course, because it was designed, conducted, and completed before the TCGA data emerged. But what we learned from the TCGA is there appeared to be four distinct molecular subgroups. There were the POLE-mutated patient population. There was the mismatch repair deficient or MSI-high endometrial cancer population. There was the copy number-high or what we say are the p53-mutated. And then the last cohort was called the NSMP (no specific molecular profile). But now, that's even evolved; some people term it TP53 wild type. That's a bit of even a heterogeneous cohort amongst itself. So we're going to take these subsets, independent of POLE and an MSI-high, and we're going to look at TP53 or copy number-high, and that will probably be divvied out further, and the NSMP, and that will probably be subdivided. But really, it gave us these four components, which has then evolved. Many of you may have heard of the ProMisE algorithm or ProMisE Plus, which looked to take the data from TCGA so that we can start to really look at it in clinical practice. So it's really revolutionized how we think about these patients, how we think about the disease, and how we design trials.    Dr. Shannon Westin: And I just want to add to that because I think that it's so important, what Ramez said about the way we were developing trials, the way we were designing trials. We knew that these classifiers—we were learning these classifiers are prognostic. Now what we're really trying to hone in on is how predictive they are. And certainly, one of the major classifiers that we're going to talking about today is mismatch repair status, and that is most definitely predictive of response to therapies. But we're still learning about the other classifiers and how we might adjust the way we treat people, even deescalating care for certain patients. That is still being proven in clinical trials, although we suspect that it's going to be borne out as other clinical trials report. Dr. Lidia Schapira: It's a perfect segue to this current trial. Tell us a little bit about the objectives and the design of DUO-E. Dr. Shannon Westin: As Ramez said, the standard of care was chemotherapy. And so we wanted to see if there was a way to improve outcomes for these women with advanced and recurrent endometrial cancer in a really clinically relevant, meaningful fashion for patients. And so we knew that this TCGA classifier, the mismatch repair, was so important, and we thought that the addition of immunotherapy to chemotherapy would most certainly work in that population but could even work in the entire population because, generally, endometrial cancer seems to be a little bit more responsive to immunotherapy and to activation of the immune system than, say, some of our other gynecologic malignancies. And so we set out to see what the addition of durvalumab, which is a PDL-1 inhibitor, would add to chemotherapy. And this was two chemo as well as followed by durvalumab maintenance.  But even further, we had some really kind of exciting science data from our lab that said that if we combined a PARP inhibitor with immunotherapy that we could accentuate on the response to therapy and we could get more benefit. And there's kind of a lot behind that, but essentially, what we thought was that the damage that's caused by the PARP inhibitors would create an activation of different immuno-pathways, like STING pathway and activating cytokine release, and that we would get this synergistic activity. So one of the other objectives was to see if the addition of the olaparib, the PARP inhibitor, to durvalumab in that maintenance setting could even further improve benefit. So we had a dual primary endpoint looking at progression-free survival, so the amount of time people live without their cancer coming back. And that endpoint was first, the durvalumab-alone arm to control, and then the second portion of that was the durvalumab/olaparib arm back to control. Dr. Lidia Schapira: So before you tell us about the results, tell us a little bit about the study itself. I mean, I was very impressed that you did it in so many different locations. Tell us about that effort.  Dr. Shannon Westin: This was a huge collaborative effort both with the GOG Foundation, the Gynecologic Oncology Group Foundation, as well as ENGOT, which is our European colleagues that do amazing clinical trials. But in addition to that, we really worked very closely with our industry partner to really make sure we spanned the globe. And so we had groups from all over the world that participated and really were exceptional. The care that was taken and the hard work that went into this type of study across the world really can't be overstated. We were very lucky to have a wonderful infrastructure group. We met weekly for a long time, just keeping an eye on the data and making sure that everything was as positive as possible and, of course, that we were watching the outcomes of the patients very closely and making sure that there was no evidence of harm or issue. And so it really did take a village, truly, to run this study and to ensure that at the end of it, we got really great data that we can trust. Dr. Lidia Schapira: So tell us the results. Dr. Shannon Westin: So DUO-E was positive for both of its primary endpoints, which was very thrilling. So for the first analysis, which is the durva-alone arm to control, we saw a reduction in the risk of progression of 29%, so a hazard ratio of 0.71. And then the addition of olaparib seemed to further enhance this benefit, so a 45% reduction in the risk of progression for a hazard ratio of 0.55. But what's really exciting is our follow-up time was pretty long; it was about 17 months, so we were able to look at a couple of different analyses, including an 18-month landmark analysis where we saw approximately 50% of the patients were still alive progression free at 18 months, as compared to only 21% of patients being alive progression free in the control arm. So there was a doubling in that progression-free survival time point at 18 months, which is thrilling. Dr. Lidia Schapira: So Ramez, as an expert in the field, what was your reaction when you read or heard these results?  Dr. Ramez Eskander: It's exciting, honestly. So we have gone a long time without seeing really significant successes in the endometrial cancer space, a testament to the fact that we hadn't yet developed our understanding of how we could move this needle forward. But Dr. Westin and the DUO-E team conducted an exceptional clinical trial, as you mentioned, international study, rational and important hypothesis to adjudicate. And what we saw here was both now we had other studies—the RUBY trial, the GY018 trial, the  AtTEnd—and now here DUO-E, which added this hypothesis of PARP maintenance in addition to checkpoint to try to augment response and consistent, really provocative data, exciting, in line with what we've seen and hopefully will continue to drive the science in this space, most importantly. Dr. Lidia Schapira: So let me ask you a follow-up question to that. What kind of scientific questions are in the air now as a result of this trial and what the trial found? Dr. Ramez Eskander: Oh, goodness. Shannon and I could both take this, I'm sure. But I think in the dMMR population, we recognize that there's a ton of data that is supportive of the fact that these tumors are immune responsive, particularly in dMMR endometrial cancer, whether it's an epigenetic promoter hypermethylation, or a mismatch repair gene mutation. I think the data has emerged that immunotherapy is here to stay for these patients in the newly diagnosed advanced stage, even chemo naïve, who need adjuvant therapy.   The pMMR population, this is where we're seeing more and more questions emerge because we realize that that may be a cohort of different cancers. And I'll let Shannon speak to this briefly, but even the incorporation of the PARP inhibitor, in addition to the checkpoint, there's a biologic rationale for combining those two together to augment response. And to see the benefit in that trial—arm three and arm two, we can look at descriptively and look at the differences, but who are those patients? Where is the PARP and the checkpoint most effective? How do we expand that to a larger population of patients potentially? These are questions that emerged because, as Dr. Weston will allude to, I know we also talk about HRR mutations, which are captured, but we even have a lot to understand about that in endometrial cancer, where we've had more research in the ovarian cancer space. Dr. Shannon Westin: Being mindful of time, because I have, like, 1,000 hypotheses that have been generated by this study, which, I think, shows it's a great study, right? Because you get some answers, and as our colleague Brad Monk says, “The only definitive study is the negative studies.” This most certainly was not that. But just kind of expanding on what Ramez said, the interesting thing about DUO-E is that really the biggest benefit for the combination of the durvalumab and olaparib was in that mismatch repair proficient group. And I personally thought that we were going to see accentuation of the impact in the mismatch repair deficient group based on the science, but that just wasn't borne out by the data. It doesn't seem that the combination has that much to add in that mismatch repair deficient group. And when we tease out the mismatch repair proficient group, I think that's where a lot of interesting information is going to come because, to Ramez's point, we're going to tease out: Is it driven by the P53-mutant population? Is it driven by the population that has homologous recombination deficiency? How do we even measure homologous recombination deficiency in endometrial cancer? So I'm super excited about what we found and how that may help us to make those decisions for the patient in front of us.  The other thing I think needs to be made mention of—and this was something we saw in DUO-E as well as AtTEnd—we had a large population of patients that were recruited in Asia, 30%. Interestingly, when we look at the forest plot, that group doesn't seem to benefit as much from the addition of the olaparib. So we really need to tease out what's different about that population because what Nicoletta Colombo presented around AtTEnd, it looked like they didn't benefit from the atezolizumab either in that study. So there's clearly something different about that population, and we have a really big opportunity to look at that since we had such a large proportion of patients that were enrolled there. So that's another, I think, really intriguing question. Dr. Lidia Schapira: So how does this fit in the context of endometrial cancer treatment, and what are we going to do with patients in the clinic? I'd love to hear both of your perspectives, starting with you, Ramez. Dr. Ramez Eskander: It's an evolving answer, to say the least. What we can say definitively is that we have a United States FDA approval for the regimen of dostarlimab plus carboplatin and paclitaxel in the mismatch repair deficient, advanced-stage/recurrent or metastatic patient cohort. And again, that's because the magnitude of benefit that we saw in the RUBY trial, which looked at that, was actually analogous to what we saw in 018, AtTEnd, and DUO-E, again, consistently highlighting the benefit of the IO and the dMMR. We have yet to see how this is going to evolve the landscape in the larger patient population, which is the pMMR patient population. And it may be that based on the data that we have, we will see immunotherapy plus carboplatin and paclitaxel as the new standard of care in the pMMR cohort, or it may not. That's yet to be defined. And I think Dr. Westin will add to this, but I think that's also going to depend on the perception of how we view the cohort. Is it one group of patients? Are we going to have to think about subsets within the pMMR population? That is an active conversation. Dr. Shannon Westin: I would just add, having treated patients on this combo regimen with the durvalumab and olaparib, I have multiple patients that still remain on study, and this—we're looking at three and four years out. I just never saw anything like that before with standard chemotherapy, so there's definitely something here. So I want to know who those patients are, who benefits really the best from the combination, and who could we just give the immunotherapy to and get that same benefit. So we obviously always want people to live as long as possible. That's the bottom line. But we don't want to overtreat. And so I think balancing that is really important. Dr. Ramez Eskander: The point that was made earlier: We have yet, aside from MMR response to checkpoint, within the pMMR population, we understand that there may be subsets, but we have yet to prospectively validate that these molecular cohorts within the pMMR population are truly defining response to a particular therapeutic strategy. So we have to be cautious not to limit the treatment opportunities for these patients without having the data that we need to do so because, as Dr. Westin mentioned, for us—whether it was the Gy018 trial, the RUBY, the DUO-E trial—what we saw is there are pMMR patients who have a dramatic response even though they are “biomarker negative.” They're pMMR, they're TMB low, they're not POLE mutated, but yet they still derive a dramatic benefit. And so that goes back to the hypothesis about why we're even combining checkpoint with chemotherapy in which, for example, in lung cancer, there's been established success and approval. So I think we're all eager to see these strategies emerge as treatment opportunities for the pMMR patients as we work to still develop additional effective opportunities. Dr. Lidia Schapira: So, based on all of this and sort of the new twists on the scientific hypotheses that are now generated, what are the next steps? Dr. Shannon Westin: Well, I think we have to see if these drugs are available for patients. So looking at things like compendium listing and regulatory approvals obviously is going to be very important. But from the things that I can control, we are looking at the different molecular subtypes and understanding the different mutation status and trying to tease out who may be driving the biggest benefits so that we can help advise and make sure that we're doing the right thing for the patients. Dr. Lidia Schapira: And wearing my supportive care hat, I have to ask you, Shannon, about the tolerability. We often find that the quality of life and studies come out after, sometimes months or years after, the original trials are published. So let me take this opportunity to ask you now: How did women tolerate these drugs? Dr. Shannon Westin: The bottom line, Lidia, is, as expected, when you add additional drugs, you see additional side-effects. I think the good thing is that we're very comfortable with immunotherapy and we're very comfortable with PARP inhibition in gynecology because we have had access to these agents and so we know how to manage the toxicities. And so, from a standpoint of incidence, there was a higher incidence of grade three and higher adverse events in the group that had durvalumab/olaparib. But this was primarily driven by anemia, which is as expected and is usually pretty time-limited at the start of olaparib. From a long-term standpoint, there was a slightly higher proportion of patients that discontinued therapy, but it actually wasn't as much as I was worried about. So we saw a 19% discontinuation rate in the group that was just the control arm, and that went up to 24% in the dual arm, so definitely higher, but not that much higher. And when we moved to maintenance, which is really where—that's where the arm becomes unique, it was much lower at about 12%. And so that's exciting to me, that patients were able to stay on a drug and were able to tolerate it.  And then, to your other point, we do have a very nice patient-reported outcomes plan, and that is actually being analyzed as we speak with the hope of presenting it at the next major meeting, our Society of GYN Oncology meeting in March. So not right away, but I think in a pretty timely fashion, we'll have those data. Dr. Lidia Schapira: Congratulations, Shannon, on leading and presenting this wonderful study. So it's been a real pleasure to chat with the two of you. Dr. Ramez Eskander: Thank you. Dr. Shannon Westin: Thanks so much, Lidia. I really appreciate it. Thanks, Ramez, for being here.  And I will just say thank you to all of our listeners. We really hope you enjoyed this episode of JCO After Hours, where we discussed the DUO-E trial, which is a phase III trial evaluating durvalumab plus carboplatin/paclitaxel followed by maintenance durvalumab with or without olaparib as first-line treatment for advanced endometrial cancer. And again, please do enjoy this publication that was online at the Journal of Clinical Oncology on October 21st, 2023. And do check out our other podcast offerings wherever you get your podcasts. Have a wonderful day. The purpose of this podcast is to educate and to inform. This is not a substitute for professional medical care and is not intended for use in the diagnosis or treatment of individual conditions.   Guests on this podcast express their own opinions, experience, and conclusions. Guest statements on the podcast do not express the opinions of ASCO. The mention of any product, service, organization, activity, or therapy should not be construed as an ASCO endorsement.      

A new editorial paper was published in Oncotarget's Volume 14 on June 12, 2023, entitled, “Are cis-spliced fusion proteins pathological in more aggressive luminal breast cancer?” A vast majority of breast cancers (~70%) are estrogen receptor-alpha positive (ER+), for which endocrine therapy is the common treatment. However, recurrence often occurs leading to tumor progression, metastasis and eventually patient death, and the underlying molecular mechanisms remain poorly understood. In this new editorial, researchers Chia-Chia Liu and Xiao-Song Wang from the University of Pittsburgh discuss their recent study regarding recurrent gene fusions — hallmarks of some cancers that resulted either from chromosomal rearrangements or from cis- or trans-splicing. “Importantly, selected oncogenic fusions have been matched with effective targeted therapy in several solid tumors. For instance, EML4-ALK, one of the most important oncogenic driver genes of non-small cell lung cancer (NSCLC) uncovered in recent years.” In addition to gene fusions resulting from genomic rearrangements, a read-through SLC45A3-ELK4 fusion transcript has been identified in prostate cancer which is associated with disease progression and metastasis. Although the whole genome and RNA sequencing provide an effective way to detect fusion genes, the downstream identification and validation of fusion genes or their products in solid tumors remains a major challenge. Through analysis of RNA-seq data from TCGA, the authors of this editorial and their co-authors recently identified a neoplastic fusion transcript RAD51AP1-DYRK4 in luminal B breast cancer (~17.5%) showing higher ki67 expression which is an indication of aggressive clinical characteristics. “In this study, we examined the utility of MEK inhibitor trametinib (Mekinist) currently used for treating melanoma with BRAF mutations, in blocking the MEK-ERK signaling driven by RAD51AP1-DYRK4 fusion. Interestingly [...] RAD51AP1-DYRK4 may endow sensitivity to MEK inhibition in luminal B breast cancer [13]. To our knowledge, this is one of the few non-traditional fusions generated by read-through events in the absence of DNA rearrangement that play an important role in tumorigenesis.” DOI - https://doi.org/10.18632/oncotarget.28438 Correspondence to - Xiao-Song Wang - xiaosongw@pitt.edu Sign up for free Altmetric alerts about this article - https://oncotarget.altmetric.com/details/email_updates?id=10.18632%2Foncotarget.28438 Subscribe for free publication alerts from Oncotarget - https://www.oncotarget.com/subscribe/ Keywords - cancer, luminal B breast cancer, RAD51AP1-DYRK4, MEK inhibitor, chimerical transcript About Oncotarget Oncotarget (a primarily oncology-focused, peer-reviewed, open access journal) aims to maximize research impact through insightful peer-review; eliminate borders between specialties by linking different fields of oncology, cancer research and biomedical sciences; and foster application of basic and clinical science. To learn more about Oncotarget, please visit https://www.oncotarget.com and connect with us: SoundCloud - https://soundcloud.com/oncotarget Facebook - https://www.facebook.com/Oncotarget/ Twitter - https://twitter.com/oncotarget Instagram - https://www.instagram.com/oncotargetjrnl/ YouTube - https://www.youtube.com/@OncotargetJournal LinkedIn - https://www.linkedin.com/company/oncotarget Pinterest - https://www.pinterest.com/oncotarget/ Reddit - https://www.reddit.com/user/Oncotarget/ Media Contact MEDIA@IMPACTJOURNALS.COM 18009220957

JCO PO author Dr. Apar K. Ganti shares insights into his JCO PO article, “Pertuzumab Plus Trastuzumab in Patients With Lung Cancer With ERBB2 Mutation or Amplification: Results From the Targeted Agent and Profiling Utilization Registry Study.” Host Dr. Rafeh Naqash and Dr. Ganti discuss clinical decision-making regarding biopsy; HER2 amplification, mutation, and targeted therapy; drug combinations; and aspects of the TAPUR and DESTINY-Lung studies. Click here to read the article!   TRANSCRIPT Dr. Abdul Rafeh Naqash: Hello and welcome to JCO Precision Oncology Conversations, where we bring you engaging conversations with authors of clinically relevant and highly significant JCO PO articles. I'm your host, Dr. Abdul Rafeh Naqash, Social Media Editor for JCO Precision Oncology, and Assistant Professor at the University of Oklahoma Stephenson Cancer Center.  Today we are joined by Dr. Apar Kishor Ganti. Dr. Ganti is a Professor of Medicine and associate director of clinical research at the Fred and Pamela Buffett Cancer Center at the University of Nebraska Medical Center. He's also a staff physician at the VA Nebraska Western Iowa Healthcare System. Dr. Ganti is the lead author of the JCO Precision Oncology article titled "Pertuzumab Plus Trastuzumab in Patients With Lung Cancer With ERBB2 Mutation or Amplification: Results From the Targeted Agent and Profiling Utilization Registry Study," which is also the TAPUR Study.  Dr. Ganti, thank you so much for joining us today. Dr. Apar Kishor Ganti: Thank you for having me. I'm happy to be here. Dr. Abdul Rafeh Naqash: For starters, Dr. Ganti, this is one of the trials from the TAPUR Basket study. So I wanted to take this opportunity since this is an ASCO initiative that has been there for a few years now. Could you tell us a little bit of background about the TAPUR initiative, what kind of trials are being run or have been run, and how it all started, basically?  Dr. Apar Kishor Ganti: The TAPUR Study or the Targeted Agent and Profiling Utilization Registry Study is a pragmatic basket trial which evaluates the anti-tumor activity of commercially available targeted agents in patients with advanced cancers and tumors that have potentially actionable genomic alterations, like mutations, amplifications, etc. And this has multiple arms in multiple malignancies, using drugs that are currently approved in different indications and not necessarily approved for the indication that's being studied. But there's preclinical data that suggests that that particular drug may potentially be active in patients whose tumors harbor those mutations. For example, this present study that we conducted utilized a combination of pertuzumab and trastuzumab, both of which are FDA-approved for the treatment of patients with HER2-positive breast cancers. And we analyzed the efficacy of the combination of these two drugs in patients with lung cancer who had either a HER2 mutation or an amplification of HER2.  Dr. Abdul Rafeh Naqash: Thank you so much for giving us that background. Going to this study specifically, which is one of the very interesting TAPUR studies, what I'm reminded of especially is NCI-MATCH, for example, which runs on a similar premise to this study, where we've seen some successes and some not as exciting combination approach successes that is what we would have wanted to see.  For lung cancer specifically, as you and I both know and perhaps many of the listeners know, there's a lot of actionable drivers that have target therapies that are approved, could you touch on some of those to give a background on where the field currently lies and what are some of the important steps with respect to obtaining next generation sequencing, perhaps in patients. So what your practice is and what you would recommend for these individuals? Dr. Apar Kishor Ganti: Certainly, non-small cell lung cancer, or non-squamous non-small cell lung cancer, to be more precise, seems to be the poster child for next-generation sequencing. And the importance of NGS testing cannot be overemphasized in these patients. For example, right now we have multiple different drivers that have drugs approved for the management of these patients. The first among them, obviously, was EGFR or epidermal growth factor receptor. And that has been followed fairly successfully by targeting ALK, ROS1, now, more recently, RET, MET, KRAS, and HER2. So if you look at lung adenocarcinomas, almost half of the patients will have a tumor with a mutation that is targetable. And so it's very important to make sure that these patients are tested for, before initiating any therapy. What makes it more important is that the standard of care for patients with non-small cell lung cancer without driver mutations is either immunotherapy or chemoimmunotherapy. And we have found that if a patient has a driver mutation, especially EGFR or ALK, even if their PD-L1 expression is extremely high, their response to checkpoint inhibitors is negligible. And so it is important to make sure that we understand what their molecular status is before starting any treatment in these patients. And I think the key point here is that every patient with advanced non-small cell lung cancer should have next generation sequencing studies done prior to initiation of treatment. Dr. Abdul Rafeh Naqash: Absolutely. And in your practice, Dr. Ganti, do you tend to do liquid biopsies concurrently when you get a new individual with a diagnosis of lung cancer, or do you do it at some other time point?  Dr. Apar Kishor Ganti: Liquid biopsies, I tend to get them, but not as frequently as some would like. I tend to believe more in tumor biopsies, and I would get liquid biopsies only in the setting where a tumor biopsy is not feasible or if I feel that the patient needs treatment more rapidly than can be expected if I got a tissue biopsy. Liquid biopsies, in my opinion, are good, but they're very dependent on the tumor fraction that is present in the sample that you send. As you very well know, not all patients who have a driver mutation necessarily shed the mutation into the blood. And therefore, even if a patient has a driver mutation in a tumor, there is a small chance that the liquid biopsy may not detect it. So I tend to be more in favor of getting tumor biopsies for next-generation sequencing. In situations where the tumor fraction is high, the concordance between tumor biopsies and liquid biopsies is fairly good. Dr. Abdul Rafeh Naqash: Thank you so much for that very important clinical decision-making thought process. At least in my practice, when tissue is often the issue, as you very well know, where you don't either have enough tumor cells or the biopsy is just enough to tell you whether it is squamous or non-squamous and not enough for any further sequencing, I try to get liquid biopsies whenever feasible and appropriate so that at least we can rule out some of the driver alterations before I put a patient on immunotherapies, due to the concern for subsequent toxicities if there are driver alterations. But I totally agree, I think tissue is definitely the standard, gold standard. And if you have overlapping mutations in tissue and liquid, then obviously it increases your confidence of treating that individual with that targeted therapy. But in general, tissue definitely, at least we should try to emphasize, and I try to do this often when I get a call from a community oncologist. I'm pretty sure you do the same where we ask for multigene broad gene testing NGS, so that especially when you have HER2 mutations, for example, you won't necessarily capture those as you show on your study here.   Now, going to your study, Dr. Ganti, could you tell us a little bit more about HER2 mutations and amplifications? And there's different levels of evidence where amplification may not lead to expression or expression may not lead to amplification. And then there is a separate category of HER2 mutations. And a lot of what we know for HER2 is from breast cancer. And recently, in the last two to three years now, is for lung cancer also. Could you tell us about how the field is shaping from a HER2 mutational landscape, an amplification landscape, in the lung cancer field?  Dr. Apar Kishor Ganti: As you rightly said, most of our knowledge from HER2 is from the breast cancer world. And frankly, I think we've been spoiled by the data on breast cancer. So, unlike in breast cancer, lung cancer seems to have a much lower frequency of HER2 alterations. And while in breast cancer, HER2 amplification seems to be important and predictive for response to HER2-targeted agents, in lung cancer, we see a combination of mutations and amplifications. So, in a large TCGA study, mutations in HER2 seem to occur in about 2% of all lung cancers. And amplification seems to be occurring in approximately a similar proportion of different patients. So, they seem to be mutually exclusive as best as we can tell.  And, unlike in breast cancer, where HER2 amplification seems to be directly associated with protein over-expression and response to tumor, the data in lung are much less robust. And so, it is not necessarily that an amplification will translate into a prediction of response to a HER2-targeted agent. And we and certain other studies have shown that patients who have HER2 amplification may not respond as well to HER2-targeted therapy as opposed to, for example, patients with HER2 mutations. So, that seems to be the discrepancy in HER2 amplification and HER2 mutations when you look at lung cancer versus breast cancer. And that's another reason why we are doing the TAPUR study at the various arms because what works in one specific cancer with the same mutation or same abnormality may not necessarily work in other cancers. Dr. Abdul Rafeh Naqash: Absolutely. Thank you for indulging into that side of things. Now, going back to your trial, could you tell us a little bit of background on the eligibility criteria, how you chose some of the different mutations? What were the levels of evidence for some of those mutations from a pathogenicity standpoint, and then what were your endpoints, since this is a clinical trial with a Simon two-stage design?  Dr. Apar Kishor Ganti: Patients who were eligible for the trial included all patients with advanced lung cancer who did not have another FDA-approved treatment or were not candidates for another treatment. They all should have been 18 years or older at the time of diagnosis and have lung cancer with either ERBB2 amplification or we looked at 13 specific mutations, insertions, or deletions, and, if the patient had any of those abnormalities identified by any clear approved next-generation sequencing testing platform, then they would be eligible for the study. We chose these because of how frequently these specific mutations occurred in lung cancer and other cancers. And so, these 13 abnormalities were chosen from the host of HER2 mutations that you can see. Patients should not have received a previous HER2 inhibitor, obviously, and their LV ejection fraction should be normal because of the known risk of decreasing cardiac function with HER2-targeted therapy. They were treated with pertuzumab every three weeks and then combined with trastuzumab. Trastuzumab was given at a loading dose, initially of 8 milligrams per kilogram, and in subsequent cycles, we used 6 milligrams per kilogram. The dose of pertuzumab was a flat dose of 840 milligrams for the first dose and 420 milligrams for subsequent doses.  We continued the treatment till progression or excessive toxicity or patient withdrawal of consent. The endpoints were disease control, which we defined as objective response or stable disease for at least 16 weeks duration. Other endpoints were progression-free survival, overall survival, duration of response, and, of course, safety. We used a Simon two-stage design, as you said. The null hypothesis was that the disease control rate would be 15%, alternative hypothesis was 35%, the power was 85%, alpha was at 10%. So, if in the first stage, less than two out of ten patients had disease control, then the cohort would be closed for futility. If two patients or more had disease control of the first 10, then we expanded to an additional 18 patients for a total study size of 28. So, as far as safety analysis, any patient who received even a single dose of treatment was included in that safety analysis.  Dr. Abdul Rafeh Naqash: Thank you so much for giving us those details about the cohort. Going to the mutation or the amplifications, I'm looking at the cohort, so it seems like more or less, to some extent, there was an equal distribution of the mutations. 50% of individuals had mutations and then around 45%, 43% had amplifications. Did that play into your expectation of how the cohort did in terms of responses or the primary endpoints that you had set? Did you see differences based on those findings of mutations versus amplifications.  Dr. Apar Kishor Ganti: Yes, we did. The disease control rate was 37%, with an overall response rate of 11%. And when you looked at patients who had a partial response, which is three patients, all of them had ERBB2 mutation. And of the patients who had stable disease, only two patients out of seven had an amplification. Five patients had the mutation. So, again, this was similar to what we had expected, that based on previous studies, patients with mutation tend to respond better than patients with alterations. Dr. Abdul Rafeh Naqash: Definitely. And going to one of the striking figures that you have in this manuscript, of course, you have the waterfall plot, and then you have the swimmer's plot and the spider plot. I'm very intrigued personally by the spider plot, which is the Figure 3 in your paper, especially with this individual that had this long, durable partial response. I believe this was the same individual with the mutation. I believe it was this 776 insertion. Was there anything, any other aspect that could have contributed to this response, or does this mutation, does it have any strong preclinical data of why the activity offer to direct therapy might be more pronounced in this mutation that you came across?  Dr. Apar Kishor Ganti: Not to my knowledge. I don't think we found anything specific or different about this particular patient compared to the others. So, as far as the mutation itself is concerned, it's a fairly common mutation, the G776 insertion. It is one of the most common mutations seen in lung cancer, and studies have shown that patients with the mutation tend to respond. But why this patient responded so long, it's difficult to say. I wish we were able to find out, but unfortunately, we were not able to. Dr. Abdul Rafeh Naqash: Sure. Another question that I wanted to ask you since this falls into the precision medicine basket study questions. Does TAPUR have a different endpoint for different sub-studies? Because I vaguely remember coming across another paper where I believe a 16-week disease control was also the endpoint. So, is that something universal in TAPUR, or is it specific for specific tumor types and different combination approaches? Dr. Apar Kishor Ganti: I believe that this is a more generalized feature of the TAPUR study, the stable disease for 16 weeks as a marker of response. Of course, different arms have additional endpoints, but I think this is one of the more common ones. Dr. Abdul Rafeh Naqash: Now, there has been some work, as you very well pointed out in your paper as well, from others related to HER2 mutations, especially the DESTINY-Lung study. Could you tell us a little bit about that for listeners who may not be well aware of the DESTINY study with trastuzumab deruxtecan targeting the HER2 mutations? Dr. Apar Kishor Ganti: So, DESTINY-Lung01 was a study of patients with ERBB2 mutated lung cancer. That study just looked at mutation-positive patients as opposed to what we did, looking at both mutation and amplification. And that study showed an overall response rate of 55%, which was much higher and led to the approval of fam- trastuzumab-deruxtecan in this group of patients. And so, one of the differences between our study and trastuzumab deruxtecan DESTINY-Lung01 study, is that our study included patients with both mutations and amplification and our study did not include any cytotoxic drug. And I believe that was one of the big differences, which may make the results of our study intriguing and potentially useful to patients who may not be able to tolerate a cytotoxic agent. Because, as you know, fam-trastuzumab-deruxtecan has the cytotoxic binder. It's an ADC and has been known to have some toxicities. And the thing about lung cancer is that these patients are relatively frail and may not be able to tolerate it. And so, that's one of the major differences, a portion at least for this combination, even though the response rates are much smaller than what we see with fam-trastuzumab-deruxtecan. Dr. Abdul Rafeh Naqash: And from your practice, have you started using this combination from your study as a potential approach for individuals who may not be candidates for trastuzumab deruxtecan in your clinic? Dr. Apar Kishor Ganti: I have not as yet because I have not come across a patient who would be eligible for this combination. In my practice, as we have TAPUR open, we have the tucatinib-trastuzumab arm that opened after this arm closed. So my priority is to try and enroll patients onto that cohort. And so I currently have one patient on that. And as you know, this is not a very common alteration, so we don't have as many patients with this. But definitely, this would be a combination that I would put patients on if I felt that they were not a candidate for fam-trastuzumab-deruxtecan.  Dr. Abdul Rafeh Naqash: So, Dr. Ganti, what's the next step after this since your study didn't meet some of its endpoints? What are you planning, or is there a plan to expand on this through the TAPUR mechanism?   Dr. Apar Kishor Ganti: Right now, I don't think that there's a mechanism through TAPUR to expand this particular cohort because there is also another cohort that opened subsequently with tucatinib and trastuzumab. But I think it would not be unreasonable to study this combination in patients who are not candidates for fam-trastuzumab because of the differences in toxicity. So that would be where I would potentially see a role for this particular combination, and I think it should be studied in that setting. Dr. Abdul Rafeh Naqash: Excellent. Now, I try to dedicate a section of this conversation for provocative discussions that may not be addressed in your paper, but I still like to get insights from experts in the field such as yourself. So comparing it to the NCI-MATCH or some other precision medicine-based initiatives, we do often see that mutations that we think might be driving the process don't necessarily lead to really high or really promising responses to targeted therapies. So in this case, do you think, from a futuristic standpoint, a proteomic-based assay, since I think you work in the proteomic space as well, that would be an interesting way to look at whether signaling actually is altered from a mutation or an amplification, suggesting that that is driving the process, so would be a more attractive target than just looking at a mutational signature?  Dr. Apar Kishor Ganti: I think definitely that should be the way we should be looking at these kinds of studies, because even in this study, even if you look at fam-trastuzumab-deruxtecan and the DESTINY-Lung01 study, we have patients who have definite, identified drivers, and even there, only about half of the patients responded. It was much smaller in our study. But basically what I'm getting at is with the best of the drugs that we have today, only half of our patients respond with HER-2 mutations, for example. So I would definitely favor a more integrated approach to identifying those patients who would be candidates for these targeted agents and not just simply relying on a specific mutation.  Since we are being provocative, I would go one step further and say, “Hey, we have AI. And there are currently AI-based technologies which look at the entire next-generation sequencing profile and try to identify which drugs could potentially be effective in those patients based on a complete understanding of their entire tumor genetic profile, rather than just looking at one or two, or three mutations.” So that, I think, would be a much more robust approach through precision medicine. So, like you just said, that patient that we had who had a prolonged response, we don't know why he or she had a prolonged response. And maybe if we identified a pathway or pathways which were overexpressed or more active in that particular tumor setting, we would be able to identify better targets and better approaches for those patients. So I think that is the way to go in the future. Dr. Abdul Rafeh Naqash: Excellent. Thanks for indulging into that provocative discussion and hopefully maybe five years down the line when we meet again or run across each other at ASCO, we will say, “Oh, it did actually happen, that multiomics is being used in a way that is suited for the need of the patient.” So matching the right patient to the right therapy at the right time.  So, Dr. Ganti, the last section is going to be dedicated to you as an individual. So you've had a very successful, brilliant career as a clinical trialist and as a lung cancer expert. Tell us, for the sake of our listeners and perhaps some of the early career junior investigators, what your career trajectory has been briefly, and what are some of the things that you felt were successful that could provide advice and insights to people who are earlier in their careers and trying to emulate what perhaps you have done or you are doing?  Dr. Apar Kishor Ganti: Well, that is a big one. I never thought of myself as being a role model for anyone, far less someone who's at the beginning of their career. But what I have always mentioned to students and residents and fellows is basically there is no substitute for hard work. Luck plays some role in this because you need to be at the right place at the right time for some of it, but hard work definitely will pay off. And the other thing that is important is not to get disheartened if your first clinical trial gets rejected or concept gets rejected, or if your first grant gets unscored. That is part of life, and persisting is probably the best way to continue.   Also, continuing to believe in yourself. I've seen a lot of folks, especially once they get into their second or third year after fellowship when things are not going the way they want to, they start to wonder, “Am I suited for this job? Am I the right person? Am I doing this correctly? Should I be doing something else?” And I think it's just a matter of time before they will find success. And also, the other thing is, if one particular approach does not work, there are always other ways that you can look at. So, for example, if you extend a bunch of clinical trial concepts that do not work out, you could potentially look at other ways of answering questions. For example, you could do retrospective analyses, come up with provocative, hypothetical generating questions that could be answered in the future in a prospective study. So there are lots of avenues to do that. And I think I was benefited by my mentors who helped me see this relatively early in my career. Dr. Abdul Rafeh Naqash: Thank you so much, Dr. Ganti, for all those valuable insights that you've learned over your career and hopefully will help some of the listeners. Before we finish, I'm going to ask you three rapid-fire questions that hopefully will let our listeners--give them a little bit of a sneak peek into you as a person. And you get like five seconds for each question. And they're not complicated questions. My first question to you is what is your favorite food? Dr. Apar Kishor Ganti: Thai food. Dr. Abdul Rafeh Naqash: What is your favorite place to go for vacation?  Dr. Apar Kishor Ganti: South Africa.  Dr. Abdul Rafeh Naqash: And what is your favorite hobby?  Dr. Apar Kishor Ganti: Reading.  Dr. Abdul Rafeh Naqash: Well, thank you so much again, Dr. Ganti. This was a very interesting conversation and hopefully, when you or others have other TAPUR-related trial results, perhaps they will again choose JCO PO as a destination for that work.  Thank you for listening to JCO Precision Oncology Conversations. Don't forget to give us a rating or review and be sure to subscribe so you never miss an episode. You can find all ASCO shows at asco.org/podcast.  The purpose of this podcast is to educate and to inform. This is not a substitute for professional medical care and is not intended for use in the diagnosis or treatment of individual conditions.   Guests on this podcast express their own opinions, experience, and conclusions. Guest statements on the podcast do not express the opinions of ASCO. The mention of any product, service, organization, activity, or therapy should not be construed as an ASCO endorsement.   Guest Bio: Dr. Apar Kishor Ganti, MD, MS, is professor of medicine and Associate Director of Clinical Research, Fred & Pamela Buffett Cancer Center at the University of Nebraska Medical Center and Staff Physician at VA Nebraska Western Iowa Health Care System.   Guest COIs: Apar Kishor Ganti, MD, MS Consulting or Advisory Role: AstraZeneca, Jazz Pharmaceuticals, Flagship Biosciences, Cardinal Health, Sanofi Genzyme, Regeneron, Eisai Research Funding: Apexigen (Inst), NEKTAR Pharmaceuticals (Inst), TopAlliance BioSciences Inc (Inst), Novartis (Inst), Iovance (Inst), Mirati Therapeutics (Inst), Chimeric Therapeutics (Inst)  

In this episode Harriet and Grahame discuss the provisions of sections 86 and 87 of the Taxation of Chargeable Gains Act. This powerful piece of anti-avoidance is relevant to anyone who deals with trusts outside the United Kingdom, especially trustees, beneficiaries and settlors. You can find out more about Grahame, here: Grahame Jackson You can find out more about Harriet, here: Harriet Brown

A new research paper was published in Oncotarget's Volume 14 on May 4, 2023, entitled, “Human LY6 gene family: potential tumor-associated antigens and biomarkers of prognosis in uterine corpus endometrial carcinoma.” The human Lymphocyte antigen-6 (LY6) gene family has recently gained interest for its possible role in tumor progression. In this new study, researchers Luke A. Rathbun, Anthony M. Magliocco and Anil K. Bamezai from Villanova University carried out in silico analyses of all known LY6 gene expression and amplification in different cancers using TNMplot and cBioportal. In addition, the team analyzed patient survival by Kaplan-Meier plotter after mining the TCGA database. “We report that upregulated expression of many LY6 genes is associated with poor survival in uterine corpus endometrial carcinoma (UCEC) cancer patients.” Importantly, the expression of several LY6 genes is elevated in UCEC when compared to the expression in normal uterine tissue. For example, LY6K expression is 8.25× higher in UCEC compared to normal uterine tissue, and this high expression is associated with poor survival with a hazard ratio of 2.42 (p-value = 0.0032). Therefore, some LY6 gene products may serve as tumor-associated antigens in UCEC, biomarkers for UCEC detection, and possibly targets for directing UCEC patient therapy. “Further analysis of tumor-specific expression of LY6 gene family members and LY6-triggered signaling pathways is needed to uncover the function of LY6 proteins and their ability to endow tumor survival and poor prognosis in UCEC patients.” DOI - https://doi.org/10.18632/oncotarget.28409 Correspondence to - Anil K. Bamezai - anil.bamezai@villanova.edu Sign up for free Altmetric alerts about this article - https://oncotarget.altmetric.com/details/email_updates?id=10.18632%2Foncotarget.28409 Subscribe for free publication alerts from Oncotarget - https://www.oncotarget.com/subscribe/ Keywords - LY6 gene family, uterine cancer, tumor-associated antigen, patient survival, biomarker About Oncotarget Oncotarget is a primarily oncology-focused, peer-reviewed, open access journal. Papers are published continuously within yearly volumes in their final and complete form, and then quickly released to Pubmed. On September 15, 2022, Oncotarget was accepted again for indexing by MEDLINE. Oncotarget is now indexed by Medline/PubMed and PMC/PubMed. To learn more about Oncotarget, please visit https://www.oncotarget.com and connect with us: SoundCloud - https://soundcloud.com/oncotarget Facebook - https://www.facebook.com/Oncotarget/ Twitter - https://twitter.com/oncotarget Instagram - https://www.instagram.com/oncotargetjrnl/ YouTube - https://www.youtube.com/@OncotargetJournal LinkedIn - https://www.linkedin.com/company/oncotarget Pinterest - https://www.pinterest.com/oncotarget/ Reddit - https://www.reddit.com/user/Oncotarget/ Media Contact MEDIA@IMPACTJOURNALS.COM 18009220957

A new research paper was published in Oncotarget's Volume 14 on March 21, 2023, entitled, “Attenuation of cancer proliferation by suppression of glypican-1 and its pleiotropic effects in neoplastic behavior.” Glypicans (GPC1-6) are associated with tumorigenic processes and their involvement in neoplastic behavior has been discussed in different cancer types. In this recent cancer-wide GPC expression study, researchers Fang Cheng, Victor Chérouvrier Hansson, Grigorios Georgolopoulos, and Katrin Mani from Lund University and Genevia Technologies used clinical cancer patient data in The Cancer Genome Atlas to reveal net upregulation of GPC1 and GPC2 in primary solid tumors. On the other hand, GPC3, GPC5 and GPC6 displayed lowered expression patterns compared to normal tissues. “[...] we identify and propose a mechanism where GPC1 interacts with extracellular matrix mediating signal transduction by mitogenic molecules involving TGF-β and p38 MAPK.” Focusing on GPC1, the researchers conducted survival analyses of the clinical cancer patient data that revealed a statistically significant correlation between high expression of GPC1 and poor prognosis in 10 particular cancer types: bladder urothelial carcinoma, brain lower grade glioma, liver hepatocellular carcinoma, colon adenocarcinoma, kidney renal clear cell carcinoma, lung adenocarcinoma, mesothelioma, ovarian serous cystadenocarcinoma, uterine corpus endometrial carcinoma, and uveal melanoma. In vitro studies targeting GPC1 expression by CRISPR/Cas9 or siRNA or treatment with an anti-GPC1 antibody resulted in attenuation of proliferation of cancer cells from bladder carcinoma, glioma and hepatocellular carcinoma patients (T24, U87 and HepG2 cells). Further, GPC1 overexpression exhibited a significant and negative correlation between GPC1 expression and proliferation of T24 cells. Their attempt to reveal the mechanism through which downregulation of GPC1 leads to attenuation of tumor growth using systematic Ingenuity Pathway Analysis indicated that suppression of GPC1 results in ECM-mediated inhibition of specific pro-cancer signaling pathways involving TGF-β and p38 MAPK. The team also identified differential expression and pleiotropic effects of GPCs in specific cancer types. This emphasizes their potential as novel diagnostic tools and prognostic factors, and open doors for future GPC targeted therapy. “It is plausible to measure circulating GPCs in serum, plasma or urine using a variety of methods including ELISA, urine cell sediments or exosome isolation [13, 24]. Further, detection and quantification of GPC1 by histopathological and immunohistochemical methods in tumor biopsies could be a new way to predict the biological outcome. The results of this investigation would also emphasize the potential of GPCs as novel tumor antigens, and open for GPC targeted immunotherapy. GPC targeted immunotherapy would be of high value, especially as we move into an era of precision and personalized cancer therapy.” DOI: https://doi.org/10.18632/oncotarget.28388 Correspondence to: Katrin Mani - katrin.mani@med.lu.se Keywords: Glypican-1, TCGA, bladder carcinoma, hepatocellular carcinoma, glioma About Oncotarget Oncotarget is a primarily oncology-focused, peer-reviewed, open access journal. Papers are published continuously within yearly volumes in their final and complete form, and then quickly released to Pubmed. On September 15, 2022, Oncotarget was accepted again for indexing by MEDLINE. Oncotarget is now indexed by Medline/PubMed and PMC/PubMed. To learn more about Oncotarget, visit https://www.oncotarget.com and connect with us: SoundCloud - https://soundcloud.com/oncotarget Facebook - https://www.facebook.com/Oncotarget/ Twitter - https://twitter.com/oncotarget Instagram - https://www.instagram.com/oncotargetjrnl/ YouTube - https://www.youtube.com/@OncotargetJournal LinkedIn - https://www.linkedin.com/company/oncotarget MEDIA@IMPACTJOURNALS.COM

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.26.513928v1?rss=1 Authors: chattopadhyay, c., Bhattacharya, R., Roszik, J., Khan, F., Wells, G. A., Villanueva, H., Qin, Y., Bhattacharya, R., Patel, S., Grimm, E. A. Abstract: Uveal melanoma (UM) originating in the eye and metastasizing to the liver is associated with poor prognosis. Here, we investigated whether the IGF-1/IGF-1R signaling axis is involved in UM growth and metastasis. TCGA dataset analysis reveals that UM has high IRS-1 expression, which is the first substrate of IGF-1R. Furthermore, IRS-1 is over-expressed in all UM cell lines tested (relative to non -cancer/normal cells) and in matched eye and liver UM tumors. Therefore, we targeted IRS-1/2 in UM cells as well as UM tumors developed on a chicken egg chorioallantoic membrane (CAM) model, and subcutaneous (subQ) UM tumors grown in mice using NT157, a small molecule inhibitor of IRS-1/2. NT157 treatment in UM cells resulted in reduced cell survival and cell migration, and increased apoptosis. NT157 treatment also significantly inhibited UM tumor growth in the in vivo chicken egg CAM and subQ mouse models, validating the in vitro effect. Moreover, NT157 appears more effective than a monoclonal antibody-based approach to block IGF-1R signaling. Mechanistically, through reverse phase protein array (RPPA) analysis, we identified significant proteomic changes in the PI3K/AKT pathway with NT157 treatment. Together, these results suggest that NT157 inhibits cell survival, migration in vitro and tumor growth in vivo via inhibiting IGF-1 signaling in UM cells. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

A new research paper was published in Oncotarget's Volume 13 on September 28, 2022, entitled, “Genomic alterations predictive of poor clinical outcomes in pan-cancer.” Genomic alterations are highly frequent across cancers, but their prognostic impact is not well characterized in pan-cancer cohorts. In this new study, researchers Crystal S. Seldon, Karthik Meiyappan, Hannah Hoffman, Jimmy A. Guo, Neha Goel, William L. Hwang, Paul L. Nguyen, Brandon A. Mahal, and Mohammed Alshalalfa from the University of Miami, Massachusetts General Hospital, Broad Institute of MIT, University of California San Francisco, and Dana-Farber Cancer Institute and Brigham and Women's Hospital used pan-cancer cohorts from The Cancer Genome Atlas (TCGA) and the MSK-IMPACT study to evaluate the associations of common genomic alterations with poor clinical outcome. “There is growing interest in genomic profiling (i.e. tumor mutations, copy number (CN) alterations) for cancer therapy and precision oncology to inform treatment decisions and identify patients for relevant clinical trials [1].” Full press release - https://www.oncotarget.net/2022/10/04/oncotarget-genomic-alterations-predictive-of-poor-clinical-outcomes-in-pan-cancer/ DOI: https://doi.org/10.18632/oncotarget.28276 Correspondence to: Mohammed Alshalalfa - mohamed.alshalalfa@gmail.com Keywords: genomic alterations, TP53, TCGA, poor prognosis About Oncotarget: Oncotarget (a primarily oncology-focused, peer-reviewed, open access journal) aims to maximize research impact through insightful peer-review; eliminate borders between specialties by linking different fields of oncology, cancer research and biomedical sciences; and foster application of basic and clinical science. To learn more about Oncotarget, visit Oncotarget.com and connect with us on social media: Twitter - https://twitter.com/Oncotarget Facebook - https://www.facebook.com/Oncotarget YouTube – www.youtube.com/c/OncotargetYouTube Instagram - https://www.instagram.com/oncotargetjrnl/ LinkedIn - https://www.linkedin.com/company/oncotarget/ Pinterest - https://www.pinterest.com/oncotarget/ LabTube - https://www.labtube.tv/channel/MTY5OA SoundCloud - https://soundcloud.com/oncotarget For media inquiries, please contact: media@impactjournals.com

In this the second episode of the Transfer of Assets Abroad Regime discussion Harriet and Grahame discuss the various types of motive defence available to taxpayers. They also compare TOAA to its related but very different capital gains tax charge s86 TCGA.

At the National Cancer Institute, Tony Kerlavage knows quite a bit about managing very large pools of data. When NCI launched the Genomic Data Commons, it aimed to democratize access to the genomic data in The Cancer Genome Atlas and other sources. Since then, though, Kerlavage points out that our data types and volumes have only grown. Now NCI is taking a “Commons of Commons” approach to link pools of well-structured data. “The more data we can bring together in a well-structured way, the more value it has in the long run,” he believes. He advocates for sharable Python notebooks and reusable R programming, believing significant investments in data hygiene and interoperability delivers more value than simply mining data lakes with artificial intelligence tools—for now, at least. The challenge for researchers, Kerlavage says, is to view their work with an eye to the future: How might someone else use this data going forward? Links from this episode:  Bio-IT World BioTeam NCI Launches Genomic Data Commons Bob Grossman's Vision of the Commons of Commons BioTeam's Approach to Collaborative Dictionary Authoring 

Aging-US published "Identification of RNA binding protein interacting with circular RNA and hub candidate network for hepatocellular carcinoma" which reported that 17 DERBPs, which were commonly dysregulated in HCC from The Clinical Proteomic Tumor Analysis Consortium, The Cancer Genome Atlas and International Cancer Genome Consortium projects, were utilized to construct the RBP-circRNA network. Additionally, gene set enrichment analysis showed that dysregulated TARDBP might be involved in some pathways related to the HCC pathogenesis. Therefore, a hub RBP-circRNA network was generated based on TARDBP. RNA immunoprecipitation and RNA pull-down confirmed that hsa_circ_0004913 binds to TARDBP. These findings, published in Aging-US, indicated a certain RBP-circRNA regulatory network potentially involved in the pathogenesis of HCC, which provides novel insights into the mechanism of study and biomarker identification for HCC. Dr. Yuhan Chen from The Southern Medical University said, "Hepatocellular carcinoma (HCC) is most common types of primary liver cancer." Previous studies have demonstrated that circRNAs can act as sponges of RNA binding protein, in the meantime RBPs are also able to participate in back-splicing. Therefore, the interaction with RBPs can be also regarded as a crucial element to explore functions of circRNAs. However, there are very few studies related to the effects of RBP-circRNA interactions on HCC, which requires more exploration. In this study, the authors screened out the differently expressed circRNA in HCC cases from Gene Expression Omnibus database and predicted the RBPs binding to DEcircRNA. After evaluating the expression level of RBPs in HCC from The Clinical Proteomic Tumor Analysis Consortium, International Cancer Genome Consortium and The Cancer Genome Atlas projects, they utilized 17 common DERBPs to construct the RBP-circRNA regulatory network in HCC. These findings indicated that certain RBP-circRNA networks may be closely related to HCC, which provides ideas for the mechanism of study for HCC. The Chen Research Team concluded in their Aging-US Research Output, "we identified some DERBPs interacting with circRNAs and generated RBP-circRNA regulatory networks for HCC. Among the DERBPs, high TARDBP expression was corelated with high grade, advanced stage and low macrophage fraction of HCC. We also constructed the hub RBP-circRNA network based on TARDBP and confirmed that hsa_circ_0004913 could bind to TARDBP, which may provide new clues for HCC mechanism study. However, there are also some limitations in our study. First, we only used TCGA, ICGC and CPTAC projects for analysis and little data resulted in only one RBP with prognostic significance, which may lead to the loss of some potential functional RBPs. Second, we didn't classify samples according to the etiology and these identified circRNAs and RBPs may not be representative in HCC with different etiologies. Moreover, the number of HCC cases with circRNA data included in this study is relatively small. Due to our current lack of HCC samples and no survival information of HCC with circRNA expression profiles in GEO, we could not verify the expression and assess the prognostic value of circRNAs for HCC. In summary, our results indicated that some RBP-circRNA networks take a potential part in the pathogenesis of HCC and provide a new perspective for further mechanism study and biomarker development of HCC." DOI - https://doi.org/10.18632/aging.203139 Full Text - https://www.aging-us.com/article/203139/text Correspondence to: Yuhan Chen email: cspnr1@126.com Keywords: hepatocellular carcinoma, autophagy, progression, miR-513b-5p, PIK3R3

Oncotarget published "The acylfulvene alkylating agent, LP-184, retains nanomolar potency in non-small cell lung cancer carrying otherwise therapy-refractory mutations" which reported that KEAP1 mutant NSCLCs further activate NRF2 and upregulate its client PTGR1. LP-184, a novel alkylating agent belonging to the acylfulvene class is a prodrug dependent upon PTGR1. The authors hypothesized that NSCLC with KEAP1 mutations would continue to remain sensitive to LP-184. LP-184 demonstrated highly potent anticancer activity both in primary NSCLC cell lines and in those originating from brain metastases of primary lung cancers. LP-184 activity correlated with PTGR1 transcript levels but was independent of mutations in key oncogenes and tumor suppressors. Correlative analyses of sensitivity with cell line gene expression patterns indicated that alterations in NRF2, MET, EGFR and BRAF consistently modulated LP-184 sensitivity. These correlations were then extended to TCGA analysis of 517 lung adenocarcinoma patients, out of which 35% showed elevated PTGR1, and 40% of those further displayed statistically significant co-occurrence of KEAP1 mutations. The gene correlates of LP-184 sensitivity allow additional personalization of therapeutic options for future treatment of NSCLC. Dr. Aditya Kulkarni from The Lantern Pharma, Inc. said, "KEAP1, KRAS, TP53 and STK11/LKB1 are among the commonly altered genes with considerable clinical prevalence in non-small cell lung cancers (NSCLC)." The authors profiled primary and metastatic in vitro models of NSCLC for their sensitivity to LP-184 as well as standard of care agents, evaluated gene correlates of LP-184 response, and obtained evidence on in vivo anti-tumor effect of LP-184. Mutated KEAP1 and concomitant decreased KEAP1 activity in cancer cells induces greater nuclear accumulation of NRF2, causing enhanced transcriptional induction of antioxidants, xenobiotic metabolism enzymes, and drug efflux pumps, thereby rendering KEAP1 mutations predictive of chemotherapy resistance in NSCLC patients. The identification of a trend toward detrimental overall survival among a subset of platinum-treated NSCLC patients harboring co-occurring KRAS and STK11 mutations could label a more aggressive molecular subtype of NSCLC. They therefore investigated LP-184 sensitivity in NSCLC cell lines harboring individual or concomitant mutations in KEAP1, KRAS, TP53 and STK11. They sought to assess LP-184 activity in a panel of selected NSCLC adenocarcinoma cell lines, determine associations between genomic and transcriptomic profiles and responses of cell lines tested, and compare in vitro potency of LP-184 with that of approved chemotherapy agents. The Kulkarni Research Team concluded in their Oncotarget Research Output, "Our key findings demonstrate that the alkylating agent LP-184 has nanomolar potency in several NSCLC cell lines and is more potent than selected approved alkylating chemotherapeutics. Additionally, LP-184 has the potential to target tumors with elevated PTGR1 regardless of presence of other co-occurring mutations but is especially found to be effective in the background of clinically significant KEAP1 mutations. We propose further evaluation of LP-184 in multiple PTGR1 high NSCLC settings that may not necessarily be mutually exclusive, including in highly prevalent KEAP1 and KRAS mutant tumors (Figure 6), and in patients with lack of actionable targets or resistance-related genes with no effective therapy options available." DOI - https://doi.org/10.18632/oncotarget.27943 Full text - https://www.oncotarget.com/article/27943/text/ Correspondence to - Aditya Kulkarni - aditya@lanternpharma.com Keywords - non-small cell lung cancer, acylfulvene, alkylating agent, PTGR1, LP-184 About Oncotarget Oncotarget is a bi-weekly, peer-reviewed, open access biomedical journal covering research on all aspects of oncology.

This week Harry sits down with Vangelis Vergetis, the co-founder and co-executive director of Intelligencia, a startup that uses big data and machine learning to help pharmaceutical companies make better decisions throughout the drug development process. Vergetis argues that if you put a group of pharma executives in a conference room, then add an extra chair for a machine-learning system, the whole group ends up smarter—and able to make more accurate predictions about which drug candidates will succeed and which will fail.Bringing better analytics into the pharma industry has been an uphill battle, Vergetis says. One survey by McKinsey, his former employer, showed that financial services companies were the most likely to adopt AI and machine learning tools; the least likely were the building and construction trades. But just one rung up from the bottom was healthcare and pharmaceuticals. "The impact that AI could have on health care is "enormous," Vergetis says. "It's in the trillions. But in terms of AI adoption, we are right above construction—and no offense to construction, but it's not the most innovative industry."But with the proper data, machine learning algorithms can help drug makers form far more accurate predictions about the probability that a new drug will perform well in Phase I clinical trials, or whether a drug that's succeeded in Phase I should be advanced to Phase II. "For years we've seen the productivity of R&D declining in our space in pharma and biotech, and I refuse to accept that," Vergetis says. "In the era of a lot of data becoming available, in the era of us being able to use techniques like machine learning to do something with that data, there's gotta be a way to reverse that trend."Please rate and review MoneyBall Medicine on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:• Launch the “Podcasts” app on your device. If you can't find this app, swipe all the way to the left on your home screen until you're on the Search page. Tap the search field at the top and type in “Podcasts.” Apple's Podcasts app should show up in the search results.• Tap the Podcasts app icon, and after it opens, tap the Search field at the top, or the little magnifying glass icon in the lower right corner.• Type MoneyBall Medicine into the search field and press the Search button.• In the search results, click on the MoneyBall Medicine logo.• On the next page, scroll down until you see the Ratings & Reviews section. Below that, you'll see five purple stars.• Tap the stars to rate the show.• Scroll down a little farther. You'll see a purple link saying “Write a Review.”• On the next screen, you'll see the stars again. You can tap them to leave a rating if you haven't already.• In the Title field, type a summary for your review.• In the Review field, type your review.• When you're finished, click Send.• That's it, you're done. Thanks!Full TranscriptMoneyBall Medicine - Vangelis Vergetis TranscriptHarry Glorikian: I'm Harry Glorikian, and this is MoneyBall Medicine, the interview podcast where we meet researchers, entrepreneurs, and physicians who are using the power of data to improve patient health and make healthcare delivery more efficient. You can think of each episode as a new chapter in the never-ending audio version of my 2017 book, “MoneyBall Medicine: Thriving in the New Data-Driven Healthcare Market.” If you like the show, please do us a favor and leave a rating and review at Apple Podcasts. Harry Glorikian: My guest today is Vangelis Vergetis, the co-founder and co-executive director of Intelligencia. It's big-data analytics startup focused on the pharmaceutical industry. And the argument Vergetis makes to potential clients is that you can take any group of 10 drug development experts in a conference room, and make them a lot smarter by adding an eleventh chair for a machine-learning system.Of course, there's always an art to deciding which drug candidates should advance to clinical trials; which Phase 1 trials should advance to Phase 2; and so on. Decisions that like are risky and expensive, and you can't make them without having a lot of old-fashioned experience and instinct around the table.Even so, sometimes the experts are biased and the experience doesn't apply. And there's only so much data they humans can keep in their heads. And let's be honest: if decision makers at the big drug companies were that smart and talented, they'd have more home runs and fewer strikeouts.Vergetis argues that we've got the historical data and the computing power today to make far more informed predictions about which drug programs to push forward. And if more drug companies used those tools, he thinks, it might reverse the decline in R&D productivity.In the conversation you're about to hear, we talked about how Vergetis and his co-founder Dimitrios Skaltsas started Intelligencia; how they built their own datasets; how they work with clients; and why it is that he and I think a lot alike—to the point of using the same MoneyBall metaphor when we talk about transforming drug discovery and healthcare.So here's my conversation with Vangelis Vergetis.Harry Glorikian: Vangelis, welcome to the show. Vangelis Vergetis: Thank you. Very good to be here. Harry Glorikian: You know, it's interesting. I was looking at the company and looking at what you guys are doing. And I, I've probably talked to, I don't know, close to 70 experts in different areas of healthcare, drug discovery, computer science you know. Out of all those people, I honestly think you and your company Intelligencia might be the most exact reflection of the argument I was making in my 2017 book MoneyBall Medicine. In fact, I actually think you used the MoneyBall metaphor in your own talks. So I want to start out with having you explain the parallels between your company and what Billy Bean did at the Oakland A's.Vangelis Vergetis: it's very funny. You say this Harry, by the way when we started the company, what is it, three, three and a half years ago now, we had a slide actually. You know, baseball did it in the nineties. Is it about time that healthcare does the same? and going through the MoneyBall analogy. So look, the quick or the easiest way to explain it, right, it's the analogy of how do you pick baseball players and build a winning baseball team and how do you pick drug candidates and development programs and build a winning pipeline?So, you know, back in the day, what baseball did is a lot of experts in a big conference room. And these guys have watched—and I say guys, because yeah, they were primarily guys—they watched, you know, thousands of baseball games each, and they had their own perspectives and views and biases and experience in terms of what's you know, who's a good baseball player and who's not, and who they want on the team and how do they complement each other.And that's how they built a baseball team and, you know, the, the kid comes in and, you know, the chubby kid, I think Jonah Hill, right, and tells Brad Pitt, or Billy Bean in real life, I think we can do this differently. And that's a little bit of the analogy here, look, it's not a perfect analogy, like everything. Right? But the analogy here is how do you go from when you design a clinical trial or when you think about the pros and cons and the risks of a development program, how do you take that conversation from a room full of people, the oncology PhD, the statistician, the person who's developed dozens of drugs in the past and so on, and you inject some data science and machine learning capability into that conversation. There is art in drug development. We'll be the first one to acknowledge that, the same way there's art in baseball. So I would not expect that you know, that room gets replaced by a machine in any shape or form and definitely not in the, in the near or even medium, medium future. But the idea is, you know, if you have 10 people in the room, can you pull up an 11th chair, have the machine learning algorithms, sit at a chair. And provide a very unbiased data-driven perspective into that conversation. So that, that, that's what we do. Harry Glorikian: So we're going to, I want to get into some of the details, but I want to step back and fill in some history here for the people and how Intelligencia got started. If I'm not mistaken, your background is computer science, not biology. Right? Okay. And your co-founder Dimitrios [Skaltsas] is trained in law. So you both spent times at McKinsey, is that where you guys met? Vangelis Vergetis: So we, it's a, it's a good, good both of those good points. So you have a former lawyer—which we don't hold against him, we still like him very, very much—and a former computer scientist or electrical engineer who are running a company in drug development. Like, how does that work? A couple of things. As you, as you rightly pointed out, we met at McKinsey. We were both part of the healthcare practice there. Initially I was in the, in the US. Dimitrios was in Europe. We met 10 years before starting a company just running client projects together. We kept in touch over the years. And at some point, I think it was 2014, Dimitrios moved to New York, moved to the US with McKinsey and took some AI responsibilities. McKinsey was doing some internal AI. I think it was called McKinsey Solutions or something like that.So we became closer when he was in New York. We were both in healthcare for the better part of the last a decade, and we were looking for, what is the opportunity? You know, what's the area in, in drug development or frankly in pharma more broadly, where we believe we can have an impact.And it was partly us thinking through different areas. It was frankly customers or clients coming. We were both at McKinsey and we have done this study over and over again. Right. How do you design a better clinical trial? We had, I had done this, I don't know, two dozen times, maybe more. And clients kept asking McKinsey or us, Hey guys, you know, we understand how you do this and you do it very well, but are you using machine learning? Are you using data? And after saying no for about, you know, 50 times we said, okay, we should stop saying no and just go build the damn business. So here we are. Harry Glorikian: Yeah, no, I know that. I mean, from my days having Scientia Advisors, they ask over and over and over again and you keep it. It's great profitability by the way, but because you sort of know the answer. But you couldn't have picked a harder space though this is not a trivial exercise, especially if you go back to 2014 where some of the data was not even truly available or not in a format or not labeled or, or, or, or, or, or—right, to where we are today.Vangelis Vergetis: We started the company basically in 2018. The biggest challenge, I think you, you, you rightly put it, it's getting your hands on the right data. You need to answer the question you want to answer. And we took that view by the way. And some people go differently and I'll have my biases, my own biases, I'll admit. In a lot of places, what we've seen, particularly some big pharma, because they're sitting on a vast amount of their own data, but whether it's CTMS data or whatever clinical trial data they have, and the exercise they mentally do is okay, I have all this data. What questions can I answer? What can I do? And there's a lot of value there. We can answer a lot of good questions. But sometimes the question you ask needs more data than what you have, and you're kind of force-fitting it a little bit and say, yeah. Okay. But maybe I can answer most of it. Well, not really. So we flipped it. We asked the question, the question is, what is the risk of this clinical development program or the flip side of it? How likely is it that this clinical program or this drug will eventually reach a patient, will eventually receive approval by the FDA and be used by a patient. Then we went there. We said, okay, if that's the question, what data do we need to answer that question? Some of it very easily accessible. Some of it doable, but you need to build data pipelines. You need to clean it up. It's a little bit messy, whatever. Some of it doesn't exist. We've got to build it from scratch. So if you do it the other way and say, what do I have, you'll ignore that piece that says, doesn't exist. I have to build this from scratch. You're going to try to solve the problem with the other stuff.And then you realize it's not enough. So we asked the question and then we went very systematically to get all the data we needed to train the machine learning models. To answer that question. Harry Glorikian: Sounds like a consulting approach. What do we need to fill the two by two? So I totally get it. What are the biggest limitations you see right now from pharma's current method of assessing clinical trial risk? Vangelis Vergetis: Yeah, there is, there's a few and some are bigger. Some are smaller. And it's, it's hard to paint the whole industry with a broad brush, but there are some technical limitations that everybody has like as humanity, as a scientific community. Do we really understand drug biology or biology? Really well, human biology. I don't know. We understand it well enough, but from the, total knowledge, biological knowledge, we probably know this much. That's one challenge and it's a technical challenge or a scientific challenge.A technical challenge is and I think you put your finger on it, data availability. But it goes beyond, can I get my hands on the right data? Is it curated in a particular way? Is it well annotated? Is it labeled? Does it have the same quality? Is it consistent? You know, I, I take data from this genomic database. I pick data from that genomic database. Are they structured the same way? Kind of combine them or how much work do I need to do combine them. Now, it's a solvable problem. You know, the understanding of biology. It is solvable over time, but not immediate. The technical aspect of, can I make data consistent, solvable, is incredibly painful, and very few people have the patience for it or are willing to, I mean, we've killed a lot of brain cells pulling that data together, but we've done it.And then there's a third group, I think, of challenges that I would put in the broader, you know, cultural umbrella. You know, there is the, what I call the “every drug is unique” syndrome. A lot of people out there will say, well, you know, there's so many differences between drugs and programs and all that, there's no way you can use machine learning to estimate the success of this drug. Most of it not true, actually there's that syndrome there is the—and it's actually very interesting in the pharma industry, particularly, or in biotech—here is the “I want to see very quick results. I want to try this AI thing, whatever this AI thing is. Let me try it for two, three months. Show something quick. If I can show us a quick win. Great. If not, I'll throw it away. I don't have the patience for it.” And this is an industry that will easily not even think about investing 10 years and a billion dollars to develop, forget clinical, in the preclinical world, to discover a new target or a new molecule that could cure Alzheimer's or pancreatic cancer or something. So we are an industry that we're very much into putting an enormous amount of resources, time, patience, to discover a drug, but when it comes to incorporating an AI system methodology model that may help us tremendously, we are impatient. “Three months. Let's see what I can do. Oh, no results? Throw it away. I'll never see it again.” And there's a little bit about this, I think in all fairness, companies are getting better. So most of the large pharmas, they have now chief digital officers or chief innovation officers with a whole structure underneath them and mandates and all that. So I don't want to be too, too pessimistic here. Right. There's a lot of effort. And I think the industry at the very least has acknowledged they have a cultural barrier that needs to be overcome. But I don't think we're fully there in how we overcome it. But we're making progress, Harry Glorikian: But it's interesting, right. I look at existing big pharma and the lumbering ways they sort of move forward in fits and starts. And, you know, do I want to disrupt my kingdom to implement this thing? I mean, there's, there's a lot of human psychology that's involved here and a lack of understanding right. Of fully understanding this and what it can do for them in different areas.Then I look at the startups that literally from day one are totally data purpose-built right. Everything they're looking at is, “What's the data. How do I label it? Where are we going to use it? How do I manipulate it?” I mean, literally it is from the ground up. And I always think to myself sooner or later on my bet is that the startup is going to out maneuver the big guy.I mean, Google started from as a purpose-built entity and it's, you know, it, it outstrips most of its competitors and reshapes industries. I always think it's harder to take an existing entity and reprogram its DNA rather than have a predesigned piece of DNA from, from day one. Vangelis Vergetis: Harry it's an incredibly interesting thought, and I don't have an answer for it. And only time will tell. I would expect some pharma companies, whether we're talking about big pharma, you know, the big 10 or, you know, the, the massive guys or some of the, you know, in our industry, it's very funny, like a mid-sized biotech, it's still a $20 billion business. So, but I would bet some of them, to use your words, will adapt, will reprogram their DNA to some degree, a little bit painfully, it's going to be a little bit slow or they're going to have some false starts, but somehow they'll, they'll get there. Some others will just buy and we've seen this in the industry, right? So, interesting startup, I'll just buy them. And a few of these have already happened. We've seen, what is it, Flatiron was bought by, I believe it was Roche, right? Yes. There's many other similar examples. That's probably one of them more, the bigger ones, the more prominent ones. So I would expect this reprogramming of DNA will not fully happen organically. Some of it will happen by big pharma realizing, “Yeah. We need to play, you know, if we, if we're not a data company in a few years from now, we're, we'll be nowhere, right? How do we get there? Let's get our stuff stuff organized, and maybe we'll go make a couple of select acquisitions and eventually we'll get there.”So I think all of these flavors will materialize in some shape or form, and some companies will lose. Some companies will do the investments and put the, hire the right people and make the right acquisitions and, and, and they will continue to grow. Harry Glorikian: Yeah. And I look at it as an analogy to like, if I look at say JP Morgan or Goldman Sachs, I mean, they are the amount of money that they're spending trying to transition to this new capability is, we're not spending the same amount of money in pharma for sure. Right? Not even close. Vangelis Vergetis: I don't know the actual amount of money, because I haven't done the analysis. I haven't seen numbers. But my former employer, McKinsey, has done quite a bit of work. I think it was MGI. So MGI is McKinsey's think tank, it's the McKinsey Global Institute. They had done a lot of work on this. And I remember seeing a chart that I thought was, was mind boggling. Areas that are way ahead in AI, or industries that are way ahead in AI, I would say financial services. So the Goldmans and JP Morgans and Morgan Stanleys and some of the world's high-tech of course, and a few others. Who's at the bottom? I think it was like building materials or construction, which I get it. Second from the bottom? Health care. It was literally that bad.Well, it's true. If you look at the data, the, the sad thing for me the part that we need to think about as an industry, the promise or the impact that AI can have in healthcare. And I'm talking about healthcare more broadly now, including hospitals and payers, not just drug development or a pharma. But the impact that AI can have on health care is enormous. It's in the trillions. But in terms of AI adoption, we are right above construction and no offense to the construction, but it's not the most innovative industry.Harry Glorikian: So, this is why I love investing in this area, because it's such an incredible, I mean, some of the other opportunities are still incredible, don't misunderstand me, but this is at its nascent stage in my mind, where the opportunity is dramatic to sort of move the ball forward. Okay. Which brings me to the next question, which is, you know, and you don't have to name any names or anything like that. Walk us through sort of a real world example of how you help a client in practice. Vangelis Vergetis: Ooh. Maybe I'll give you two examples. You asked for one, I'll give you two. Actually I'm gonna give you more, but let, let's start with that. So where do we typically you know, we work with several flavors of customers, right? So we, we serve some of the largest, you know, top five big pharma companies we serve. Some of the smaller, even private biotechs. And we serve a bunch of the mid sized biotechs or midsize pharma companies. One area that that comes or one example is a specific program. So I'll, I'll pick on an actual example. So a specific, it's a phase two asset on a phase two program. It was a combination program, I believe for pancreatic [cancer] that our client was running. It was the phase two. It had been going on for about a year, I want to say. So it was in the middle of phase two, they were starting to see some interim results.They hadn't published anything. They were starting to see some interim results, but they were still waiting for the phase three to complete. And then there were basically three questions with increasing degrees of difficulty, if you will. Question number one, how likely is it that this program, so this combo, so our molecule with, I believe it was chemo for pancreatic cancer, will eventually reach a patient, will eventually receive regulatory approval by the FDA? That was question number one, which is our bread and butter. This is what our algorithms do. I'll make up the number now. It's a, you know, 13%, which by the way, for pancreatic cancer, phase two, that's not bad. The second question was, okay, now let's start thinking forward. So at the end of phase two, we're able to show ABC, how does that probability change? Because given the interim results we've seen, we have pretty decent conviction we'll be able to show something in that range when it comes to OS or ORR or whatever end points we're measuring. What will our probability to change to. It's 13 now, will it go to 20 or we'll go to zero?What if we managed to show something better or something worse. So in that sense, we're trying to calibrate and say, based on what we show at the end of phase two, how do we make a decision? Should we go to phase three or not? Is it too risky still? And it needs to be derisked further? Or are we comfortable with the risk we're taking, and we're willing to write a, you know, $200 million check to run a phase three program. So we did the simulations, if you will, of the analysis to say, based on what your phase two will show, here's what you should expect your risk to be at the beginning of phase three. That was the second layer. The third layer went even a step further and said, okay, let's assume we are now comfortable moving forward. So the risk is within what we're willing to take given the size of the prize, right? Because if you do get this drug approved, we estimate an enormous commercial potential. So we're willing to take significant risks here. How should we do this? So help us think through how different choices for continuing our development program affect our chances for approval.For example, should we run a smaller phase two-B and then two large phase three trials. Should we scrap the phase two-B and go straight to pivotal phase three and do a much larger trial. And there are different trade offs there that have to do with costs, time and risk. We help them think through from the middle of phase two where they are today, how likely is it that they go approved? How will that evolve once they publish results? And if they decide to move forward, what the best path forward is from a risk point of view. So that's one example. Well, I'll spare you. The second one, I spent too long on the first one. Harry Glorikian: So you've written this machine learning model, right? So, and I want to say there's at least a hundred factors, clinical trial, design outcomes, regulatory process, you know, the biology itself that you mentioned, right? The history. You have to train a model like that. Where did you get the data to train this complex model?Vangelis Vergetis: There's no single. So I wish there was. So we we've been to now dozens of data sources. So I think what I said at the very beginning, right? Some of the data was easy to get. So for example, there is a bunch of data that clinical trials.gov has. Of course we have that, and everybody else has that. That's very easy to get right. Valuable, but very easy to get, which is good.There are some data where you need to, it's publicly available, but you need to spend a lot of time cleaning up and curating. So think of genomic databases, whether it's TCGA or GTX, or, you know, dozens of other genomic databases that needs a lot of analysis and lot of processing and a lot of cleanup before you create features out of that data to put in your machine learning algorithms. So that's a, probably a second group.And a third group that goes back to the point initially that, you know, not all the data you want to answer, the question, is available. So you have to build it yourself. We built it ourselves. So an example, there is clinical trial outcome. So there is no to our knowledge and we looked hard. There is no data you can buy that has in an incredibly consistent, systematic way, all the outcomes of clinical trials in a particular therapeutic area for the last 20 years. So let's say, I mean, I mean, oncology, I'll give you an example. There's been a few thousand trials in the last 20 years. Let's say since 2000, we need to know every end point that this trial measured. How many patients were in each patient cohort or in each arm of the trial. What was the value of that endpoint? What ORR did they achieve? What OS did they achieve? Whatever. When was that? Because sometimes we say, OS, Overall Survival, well, was it measured at six months or 12 months. One layer more of specificity of exactly how the end point was captured. And then you need the number. How many patients survived at the six month mark or whatever it is. So there's all that, all that stuff that you need, and then you need it, not just for the trial or the program you're assessing, that's easy to do, right? It's one program. We can get it from the, from the pharma company themselves. We need it for every single trial that has ever succeeded in the past. And for every single trial that has ever failed. That's how you train a machine learning algorithm. That was very painful. We have a whole team in Athens, actually. So if the name didn't give it up, I'm from Greece originally. I've been in New York for like 25 years now, but I'm from Greece originally. So a lot of the team is based in Greece and part of that team, they're a very highly educated team and, you know, PhDs in biology, oncology, immunology, pharmacology, all the ologies. And that team curates in an incredibly systematic way all that data, before our data engineers and before our machine learning team can take over to build models. Right? So to answer your question in a short way, dozens of data sources, some easy to get some much harder with a lot of processing. And some we had to just create from scratch. Harry Glorikian: I mean, that was just thinking about what you were saying. That, that last piece we were just discussing. I mean, I can imagine to hospitals and to doctors that would be—if you could put that into interesting matrix, they could get an interesting view into these drugs instead of memorizing off the top of their head. It's it, you know, I always find all these discussions with companies that have data. I can think of five other things to do easily. Once you've got the data source. Vangelis Vergetis: We've been discussing internally, both as a team, but also with our advisors and even our customers at this point where they're coming to us on the saying, Hey guys, that's amazing what you have. We'll pay you money. Can we now do this. Can we now do that. And some of that we would love to do and we're entertaining it. Some of it, you know, we, we're still a growing company or, you know, there's 40 of us total in the company. You also don't want to get distracted by too many shiny objects. You know, find the right shiny object and focus on a couple of them, but not too many.So for some of them, we'll say, look, we could do it. We can, we don't have the time. We don't have the bandwidth today. Maybe later. For some of them we would say, yeah, that's incredibly interesting. And we were planning to go there anyway. Let's do it faster together. So we're discussing with one of our customers today about building something that goes beyond risk and starts thinking about the commercial implications of what happens when a drug actually gets approved. So it's not just predicting approval, but can you predict anything in the commercial space, whether that's revenue reimbursement market shares and so on. [musical transition]Harry Glorikian: I want to pause the conversation for a minute to make a quick request. If you're a fan of MoneyBall Medicine, you know that we've published dozens of interviews with leading scientists and entrepreneurs exploring the boundaries of data-driven healthcare and research. And you can listen to all of those episodes for free at Apple Podcasts, or at my website glorikian.com, or wherever you get your podcasts.There's one small thing you can do in return, and that's to leave a rating and a review of the show on Apple Podcasts. It's one of the best ways to help other listeners find and follow the show.If you've never posted a review or a rating, it's easy. All you have to do is open the Apple Podcasts app on your smartphone, search for MoneyBall Medicine, and scroll down to the Ratings & Reviews section. Tap the stars to rate the show, and then tap the link that says Write a Review to leave your comments. It'll only take a minute, but it'll help us out immensely. Thank you! And now back to the show.[musical transition]Harry Glorikian: If you have it to say, what is your defensible advantage, your special sauce? Like, what is it that you're doing for pharma that they can't somehow reproduce for themselves? Vangelis Vergetis: That's a great question, Harry. I will say a couple of things. Some are softer, some are harder. On the softer side, and probably more important by the way, is the persistent focus you know, unrelenting pursuit of what we're here to build. In a larger company, it's too easy to lose focus, budgets, get cut, people, get reassigned, promoted, change departments, move.So it's very hard to get a team together to focus on something for an extended period of time and only do that. So that's probably one thing when, when you compare it to a larger pharma company, right. The, the second thing would be. Bringing together people with very different expertise and experiences.So if you go to our office in Athens—and not the last year, given all the mess, we're all living in with coronavirus—but if you go to our office in Athens either before that, or hopefully very soon, it's a room and you have, you know, the data scientist is sitting here. The oncology PhD is right next to her. Right across is the data engineer. The drug developer is sitting over there. The statistician is there. So it's literally having all those people in one room or in, you know, a series of rooms in one floor, let's say, where they work together on the same topic. And it sounds a little bit mundane and it sounds a little trite, but it makes a difference for the biologist to be listening into, as these computer scientists or data scientists are talking about their models. And I'm sitting here entering all the biological clinical data from this New England Journal of Medicine article that I'm reading. I actually understand how they use it and I can offer an idea. I can say, Hey, actually, I can capture it in a way that will help you guys given what you're discussing. So all those things help.So that's the second element, which is a team of you know, we use diversity in many ways. So a diverse team, not just in the, in the racial or, or, you know any other perspective, but also in experiences and backgrounds. And the third one, which is the more technical one. It's the data we actually do have. It does take an enormous amount of time, a lot of people, an enormous amount of effort to actually build and create the data cube that we have. Nobody else has this. It's incredibly painful but we've done it. So that does set us apart. There are companies out there that are trying to solve the same or very similar questions or answer very similar questions based on a much more limited set of data. And they fall short. They're okay. But they will short of, of our predictive power. Not because they're not doing anything wrong, not because they're not good data scientists, all of those things are fine. They just don't have the data we have. Harry Glorikian: And so that brings me to that next question. In all of these models, there there's little issues, fraught throughout the process…Vangelis Vergetis: Oh my God. There's so many. And some of them are longer. Harry Glorikian: Many, right, that you have to think through. Right. That's why whenever somebody says, oh yeah, I've got the perfect answer, I'm like, it's impossible. Perfect? No, right. So what is the accuracy? I mean, if you said your predictive algorithm, how do you, how do you, first of all, what do you compare it against? And then let me just pick and say, if I will, putting it against a traditional way of making decisions. How do you measure your accuracy? And then do you go back and look at real world evidence versus the system?Vangelis Vergetis: Yeah. So we we've done a few things that are very interesting. There is a standard metric for machine learning. So let's not get too technical or I don't know how technical your audience is. But there's the AUC, which is Area Under the Curve, which means the area under the ROC curve…whatever, there's a metric called AUC. It's pretty much a number between 0.5 and 1. I mean, technically it could be low as 0.5, but that's a silly, so it's a number between 0.5 and 1. The higher it is the more predictive your model is. We are in the high eighties, low nineties, which is, which is incredibly predictive for a problem this nuanced and this hard. If you do image recognition and you use deep learning for image recognition, you get close to 0.999.These are very different problems. So with a standard AUC metric, we score very highly and we've compared that with what others have published in literature. And we are higher than at least what we've seen published. But by others then you do obvious things, right? So, so what do you do, you say, okay, let me take an example of hundred trials or a hundred programs for which my algorithm predicts that they are, let's say in the 20 to 30% success.All right. So my algorithm says all of these hundred fall in the 20 to 30% range. Now let me follow them over time and see what happens. What do you want? Ideally you want 25% of them to succeed, you know, somewhere in the middle. And it most often that's what happens. So when we say zero to 10 on average, let's say 7% of them succeed.When we say 10 to 30 on average, 22% succeed. When we say 30 to 50 on average, 39% succeed. So you do that on a large amount of trials, and then you start gaining confidence that dammit, what this algorithm or what this model is telling me eventually reflects reality. Now, of course, these are averages, right? So there will be trials for which you say 5% and they succeed. Now the obvious thing there to say is, and what we like about this actually, it's a true probability measure. So 5%, what does it mean? Right. I don't need to tell you. 5% means one out of 20 should succeed. Otherwise it's not 5%. If every, if every trial for which you say 5% fails, well, it's not 5%. It's zero. So if you say 5%, you should have one out of 20 succeeding. So you want to see that and you do see that, which is good. Similarly, if you go to a drug developer and you say, you know, 80%, they've never heard a higher number in drug development. Those numbers are rarely exist. So 80% to a drug developer means success. Well, no, it means two out of 10 will fail. Right. So you want to see that you run statistical checks, like the bins that I mentioned, Brier scores, AUC. So you run a bunch of statistical tests and you get very high predictive power. Look, I'll summarize it like this in the beginning of phase two, which is pretty early in drug development, right? So you still have, five, six years of, of development left ahead of you. The predictive power of our algorithms are about 90%. So we can tell you with 90% confidence that the probability that we give you is the right probability. When we tell you 20 it's 20, when we tell you it's 60 it's 60, we don't give you a one-zero estimate, we'll give you a number. And we're 90% confident on that number. Harry Glorikian: That's a pretty bold statement. So I'll, you know, let's, let's think about it here though. Right? So two things, right? Mof this stuff at some point has to be explainable, which is typically an issue in machine learning is the explainability of the model. So how have you designed it in a way where you can be like, yeah. Okay. This is why I got to this answer. Vangelis Vergetis: It's a great point. I wish we could do exactly what you said. But we can come close. So a couple of things, culturally, and for the right reasons, if you go, eh in front of the EVP of R&D in a large pharma company or the head of portfolio, whatever, and you tell them the answer is 42, they're going to throw you out of the room. They want to know, “Where does the 42 coming from? Why are you telling me this? Give me some, I need to know what can I do about it? I need to understand it.” Which it's very human and it's also the right thing. So we run, by design, we run machine learning models that are explainable. And there is explainability work being done in the academic community even for, let's say deep learning models, which are still much less explainable than a random forest or a KNN or, or something like that. So we run explainable machine learning algorithms. We spend a lot of time on explainability.And if one goes on our platform or uses our software, if you look at the number and then you literally click on a thing that says, explain to me why, and you see all the features that contribute to that answer and how important each feature is. So the reason I'm telling you that your probability is 42 is because on the positive side—and I'm making it up for a second, right?—a target that's a gene that's highly expressed in the tissue. You're going after let's say the lung or, or, or the breast or the liver or whatever it is. The cancerous tissue versus the healthy tissue. You've designed a very good trial with the right endpoints. It's well sized with the, the amount of patients you're putting in. You have a biomarker, which is a good thing, blah, blah. And maybe we'll also say on the negative side, by the way you know, as a company, you may not have that much experience in this particular disease area. So I'm dinging you a little bit. And the regulator hasn't said anything special about you, you haven't received any breakthrough or accelerated approval or anything like that. The gene you picked is highly expressed, but there has been zero, it's a first in class indication. If it's a first in class molecule that has been no approvals in the past of that target. So that tells me it's a little more risky than the 20th PD1 in the market. So it will give you all that.And people can do two things with that. One, and perhaps less important, but important. It gives them confidence that they understand why the machine is telling something. They can wrap their head around it and they can get more confident, even though I can tell you, yeah, I've run the statistics and the predictive power is 90%, you want to be able to understand it. You want to touch it. You want to feel it. You want to understand why? So it does that. The second thing it does is you might be able to do something about it. So back to the simulation, right? What do we help our customer? I can maybe assess for you what the difference will be if you use the biomarker versus not. If you have a larger trial with another arm or not. If you use this endpoint versus that endpoint. So you may be able to say, okay, I understand that the probability is 42%, but if I change these three things, can I make it 50? And those eight points in PTRS and probability of approval are massive in terms of NPV or whatever, evaluation you use. Harry Glorikian: That was going to be what I would, one of my next questions is, so you're doing all this. And so do they always act on the data or in some cases, do they make a different decision based on what the model said?Vangelis Vergetis: Both. So, and, and the model is not a black or white model, right? It's not going to tell you do this, or don't do this, or move to phase three or don't move to phase two. I'll give you an example, if you are in oncology if I tell you that this asset has a 80% probability of success versus 60% probably of success. It probably doesn't matter. You're going to move ahead. Anyway. It's high enough and the risk is too low. You might as well do it. So sometimes, you know, at the extreme, it may not make a big difference whether if I tell you it's a 5% probability versus a 3% probability, do you actually care? It's pretty damn low. Now in a lot of cases though, they, they fall somewhere in the gray zone and this is where a lot of other factors come in. So what do we think of that commercial potential. What are our competitors doing? How does it fit broadly with the rest of our pipeline and all of the other assets, both approved and the programs we have out there. So there's a lot of other considerations that go into making a decision, whether I move to phase three or whether I de-risk it, or you know, what I do.But for the most part what we've seen is our customers act on the information. They are able to take that information, enhance their decision-making process and make at the end of the day, a better decision either because they stopped something they should have stopped, they progressed something they should have progressed, or they designed the trial a little bit differently, or they  you know, put a program in place that maximizes the potential of the asset they have in their pipeline.So all of those things happen. The last thing I'll say, Harry, and this one is where we see a lot of action as well, is in business development. So while most of our, we're not, most actually, a lot of our work is in R&D. So pharma companies developing their own molecules. We see two more areas where this approach is gaining a lot of steam.Actually one is business development. So as I'm looking not for my own pipeline, but as I'm looking to identify or attract programs out there that I may want to go buy or partner with or in-license and do all sorts of things. So we work with a customer early on phase one and they said, you know, what are the innovative, if you will, first-in-class assets in phase one, so risky stuff for a particular indication, RA or IBD or Parkinson's or pancreatic cancer, whatever it is for the indication that I care about, what are the phase one programs out there that one are scientifically innovative. So I don't want the me-too drugs. I don't want the 21st PD1 in the market, but I want something innovative. And two, can I see that list ranked from a risk point of view or from an attractiveness point of view, you know, some have a 2% chance of approval. Some have a 20% chance of approval. Well, I want to talk about the 20.Yes. And we've, we've helped customers identify molecules and programs like that, where they go and they have a conversation with a biotech in south San Francisco or in Zurich, Switzerland, or in Tokyo or wherever, with that biotech about in-licensing or partnerships or acquisitions or whatever it is. So with that we've seen quite a bit of action.Harry Glorikian: Machine learning takes hold in drug development. What's the big picture outcome. What do you think, you know, how do you think…is it the Intelligencias of the world that are going to change the dynamic? Is it going to be the companies themselves? You know, I believe this is going to have a profound impact on how things are done and what goes forward. Vangelis Vergetis: Here's what I'd love to see Harry, I'd love to see… For years we've seen—and there's some change recently—we've seen the productivity of R&D declining in our space in pharma and biotech. I refuse to accept that. In the era of a lot of data becoming available, in the era of us being able to use techniques like machine learning, to do something with that data, there's gotta be a way to reverse that trend, that declining trend in R&D productivity, and see it going up again. Who benefits? Patients, where they see better drugs reaching them faster and curing disease. And of course the broader community of pharma companies, biotechnology companies and so on. So the, the big picture is I'd love to see the productivity of R&D in our space increase.And AI, whether it's Intelligencia—and I'm hoping, and I'm sure we will, but there we'll be honest there and that's great. We all need to think through, you know, how do we reverse the trend? So in, in pharma or, or in drug development, I see that as the big picture you know, how do I pick the winners? How do I invest behind the winners? How do I make sure I don't create any, you know, biases in that way where I miss some of the drugs that would have existed had I made the right choice and make my R&D dollars and R&D hours and effort much more productive at the end of the day for delivering drugs to people that need them.Harry Glorikian: So I saw you were quoted in a report from a law firm called Orrick that I liked. I think you were paraphrasing Derek Lowe from Novartis where you said, “It is not that AI will replace drug developers. It's that the drug developers who use AI will replace those who don't.” And coming back to the beginning, you know, do you think this is happening across the board in all businesses? Whether it's on experimental drugs or winning baseball teams.Vangelis Vergetis: Yeah. So it's a great question. Look, I think it is happening across all industries but each industry is different. So I think the scale of impact and the scale of adoption to date are very different across industries.We talked about, you know, we used construction as an example earlier. If you think about construction, the impact that AI will have a construction, it's not zero. I know one, a friend and a mentor runs a cement business and their AI. I'm not joking. They're using AI in cement production to make it more environmentally friendly, increased productivity, increased—he'll do all those things. So yeah, there will be impact. But it's going to be less in construction and building materials than it is in healthcare. Or it's going to be built different in, in, in financial services, let's say that, than it is in travel and tourism. Again there are opportunities for machine learning in travel and tourism. Probably less than in banking or financial services broadly or healthcare. To attempt to answer your question, because I don't know, I don't know what the answer is, I can tell you what my bias is or my view. Yes, it will be used across industries, but the scale of impact will be materially different, whether you're in healthcare or in travel.And two, the adoption to date is very different. All this excitement about AI and all this energy and all this impact that it can have, it's fantastic, and it will have it, but let's also be thoughtful here. I think we all are. But you need experts. There's a lot of art and a lot of things that happen. There's art in drug development. There is art in baseball, there's art, in a lot of things. There is instincts, gut feels that humans have. Some of it is bad because it's biased, but some of…he didn't miss it. There's decisions that doctors make every day as they treat patients. Forget drug development, that yes, that can be made better by AI. Maybe they can be guided by AI, but I'm not sure an AI will take over a physician's job and anytime soon.Harry Glorikian: No, I mean, I think the two together always, at least right now, will equate to step wise function up, right? The AI may not miss a piece of data that the physician didn't see. I've been with physicians where they call it and they were missing a piece of data. Had they had that data, that decision would have been different. The machine isn't going to miss that last piece, right, necessarily. And so I think the two together can be much more powerful than any one alone per se.Vangelis Vergetis: Yeah. And it varies a lot by the use case, meaning can a machine read a lung image or can it tell me if this picture is a dog or a cat? Yeah. Probably can do it better than a human or, or equally good, equally well. But in use cases that are much more intricate than, you know, reading looking at an image, whether it's building a baseball team or designing a phase three trial or anything approaching that level of complexity, the two need to come together and will for a long time to come. So I think Derek is right in that sense. Yeah. If, you know, the ones that use drug development will replace the ones that don't, but AI by itself is not going to replace everybody. Not anytime soon. Harry Glorikian: Yep. I agree. Well, listen, it was great to speak to you. I look forward to continuing our conversation, because I can see that there's many areas of overlap. And it's been great. Vangelis Vergetis: Thank you, Harry. I appreciate it. Harry Glorikian: Thank you. Vangelis Vergetis: Bye.Harry Glorikian: That's it for this week's show. You can find past episodes of MoneyBall Medicine at my website, glorikian.com, under the tab “Podcast.” And you can follow me on Twitter at hglorikian.  Thanks for listening, and we'll be back soon with our next interview.

The discoveries medical researchers and drug developers can make are constrained by the kinds of questions they can ask of their data. Unfortunately, when it comes to clinical trial data, or gene expression data, or population health data, it feels like you need a PhD in computer science just to know which questions are "askable" and how to frame them. This week, Harry talks with the founders of a startup working to solve that problem.Tag.bio aims to make it possible for any worker in the life sciences sector—even if they don't have a PhD in computer science or data science—to interrogate their data quickly and automatically. The idea is to help them uncover trends or connections in their data that would otherwise require months of work and help from a data scientist or a data engineer.The company was founded in 2014 as a spinoff from the University of California, San Francisco Cancer Center. That’s where co-founder Jesse Paquette first invented a system that let oncology researchers ask guided questions of their data without help from a bioinformatics expert. Now Paquette is Tag.bio’s chief science officer, and in this episode, he's joined by Tag.bio CEO Tom Covington to talk about how the startup's technology works and why easier access to data is critical to faster progress in drug discovery and to the whole idea of precision medicine.Please rate and review MoneyBall Medicine on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:• Launch the “Podcasts” app on your device. If you can’t find this app, swipe all the way to the left on your home screen until you’re on the Search page. Tap the search field at the top and type in “Podcasts.” Apple’s Podcasts app should show up in the search results.• Tap the Podcasts app icon, and after it opens, tap the Search field at the top, or the little magnifying glass icon in the lower right corner.• Type MoneyBall Medicine into the search field and press the Search button.• In the search results, click on the MoneyBall Medicine logo.• On the next page, scroll down until you see the Ratings & Reviews section. Below that, you’ll see five purple stars.• Tap the stars to rate the show.• Scroll down a little farther. You’ll see a purple link saying “Write a Review.”• On the next screen, you’ll see the stars again. You can tap them to leave a rating if you haven’t already.• In the Title field, type a summary for your review.• In the Review field, type your review.• When you’re finished, click Send.• That’s it, you’re done. Thanks!TranscriptHarry Glorikian: I’m Harry Glorikian, and this is MoneyBall Medicine, the interview podcast where we meet researchers, entrepreneurs, and physicians who are using the power of data to improve patient health and make healthcare delivery more efficient. You can think of each episode as a new chapter in the never-ending audio version of my 2017 book, “MoneyBall Medicine: Thriving in the New Data-Driven Healthcare Market.” If you like the show, please do us a favor and leave a rating and review at Apple Podcasts.Harry Glorikian: In healthcare and drug discovery, everybody’s got data. Knowing what to do with your data and how to get value out of it is the trick. That’s what we’ve spent the last 60-something episodes of this podcast talking about. Unfortunately, when it comes to clinical trial data, or gene expression data, or population health data, it feels like you need a PhD in computer science just to know what questions to ask and how to ask them.But there’s a startup in San Francisco that aims to break down that barrier and make it possible for any worker in the life sciences sector to interrogate their data quickly and automatically. The idea is to help them uncover trends or connections in their data that would otherwise require months of work and help from a data scientist or a data engineer.The company is called Tag.bio, and it was founded in 2014 as a spinoff from the University of California, San Francisco Cancer Center. That’s where co-founder Jesse Paquette first invented a system that let oncology researchers ask guided questions of their data without help from a bioinformatics expert.Now Paquette is Tag.bio’s chief science officer. And I’ve got him here today, together with chief executive officer Tom Covington, to talk about how Tag.bio’s technology works, and why easier access to data is critical to faster progress in drug discovery and to the whole idea of precision medicine.Harry Glorikian: Tom, Jesse, welcome to the show. Tom Covington: Thanks, Harry. Thanks for having us. Harry Glorikian: So, I’m trying to wrap my head around Tag.bio and, and all the technical details and everything, but, but sort of, I want to step back and give people who are listening the chance to understand the organization and the goals. And so I'll start with a grand vision question and it’d be like, okay: What's wrong with precision medicine and the way that we're sort of looking at data today?Tom Covington: Yeah, I think first and foremost, precision medicine is at its heart is a bit of a data management problem. There are disparate data sources within healthcare and life sciences, so that to truly enable kind of an N of 1 or small N medicine, it requires the integration of those data types and the ability to ask questions of those disparate data sources.And there isn't really, or there previously has not been, a great solution for that problem. As a part of that, given the complexity of the underlying data, there are experts in manipulating and analyzing data, but they are not the same experts that are going to be practicing medicine or advancing science, the knowledge workers in the healthcare and life sciences space. If they have a question that could be answered in data, they have to hand off that question to an expert in manipulating or analyzing data. So data scientists, bioinformaticians analysts, and the like. And that process is slow and human powered.And so if you have a question, it can be answered in data, and you're a physician, it may take you one to two months to get an answer. We're trying to take that process and turn it into something that takes two minutes or less. Harry Glorikian: I keep thinking we'll just hybridize them and then we'll have the best of both worlds. But I think that might take too long based on my experience when we first came up with the term bioinformatics, right? Stick two people in a room and have them figure it out. And that, that took a while. But what's not working so well. What's not working as well as it could in this whole life science arena. How do you guys see what you guys are working on bringing that one step closer to being more fundamentally useful and providing value to the industry? Tom Covington: Yeah, so I think the easiest way to think about it is with kind of use case examples. So we were working with a researcher who had published a paper on thymoma, the cancer, and when that was uploaded to the cancer genome atlas, and, theoretically, they had mined this data for all of its worth, we gave him access to the platform. And over the course of the evening and a glass of wine, he found three novel insights in his data that warranted publication in a paper. And essentially what we did was reduce the cost of him asking and answering the question of data. Whereas previously it would have taken him months to ask one of these questions, he was able to ask and then iterate on the questions until he found the right question that generated the right output. That allowed, that was novel. And I think that's the big advantage of this kind of acceleration of discovery that happens via platforms like ours. Harry Glorikian: So go ahead, Jesse. Jesse Paquette: A lot of people are going to look at data problems in the life sciences and healthcare space, and they're going to say, well, the problem has to do with the siloization of data. It has to do with the quality of data. It has to do with the integratability of data, and a lot of cultural problems that exist in the system. And then they're going to fall back to the old adage, which is, 90% of the time of a data scientist or data engineer is just processing the data, working on the quality, getting the data in analysis-ready shape.So why we get the question: Why aren't we solving that problem? Well, it's a hard problem. And ultimately what we've realized over many years is that if you're spending 90% of your time working on processing and transforming and getting data analysis-ready, you don't have enough time to do any of the analyses that you really want him to do. Case in point, this TCGA data set. They did analyses, basically what they could, they got published and they wanted to do so much more, but the data has so much value and you have to spend so much time just getting it ready. This is really what we're trying to accomplish and making this data just rapidly. In an assembly of line, sort of an analyzable.Harry Glorikian: Yeah, I'm trying to draw analogies to other things that I see going on in the tech industry, like, codeless sort of programming, where people who aren't familiar with the data analytics side of it can sort of pull different analytics packages or, or scripts that they can use to run on their data without having to know how to code everything up from scratch. Is that a reasonable analogy to make? I mean, the other one that I was thinking of earlier that was GitHub, right. Where people can access these things that are written once by one person, but used by multiple people. So you don't have to always go back to a data scientist and say, do this for me. Jesse Paquette: Yeah, I can, I can take that. In some sense what you're talking about is a marketplace and we do have a longer term vision for being a great marketplace for resources around an analysis of data. So if, if we have a really good turnkey connector to a critical data source, like an electronic medical record or a genomics data source, we can bring that in and people could use our system to build hybrid solutions. In many ways, it's, it's similar to, I think the way JavaScript works with NPM, or R works with  all of its R libraries or Python works with all of its Python libraries. There's this whole world of really useful stuff out there that you can sort of just swap in and out and, and, and make useful.And I think our system really does that very well with data sources and data modules that represent algorithms or apps workflows on data. That's a long-term vision of ours. Definitely. I think in the short term, what we're focusing mostly is the low-code system and being able to deploy useful application layers on top of data in such a way that you can just do it really quickly with robustness and security, and then also get to iterate with the end users. It’s very important that you actually, if you're going to build an application for a physician or for a researcher, you have to work with them to make sure that it's really useful. Harry Glorikian: Yeah, that was the word I was actually, it's funny that escaped me. Low-code was the word. There's too many damn new words that I need to keep track of for all these changes that are happening. So your VP of customer, Mark Mooney, said that you guys are solving this, quote “Last mile” of data analysis. What does he mean by that? Yeah. Tom Covington: Yeah, so if you think about it from a physician's perspective, it gets back or scientist's perspective. It gets back to this long lag between making a request, an analysis request, and getting a result. We touched on GitHub earlier. Even if we had something—well, let's say you've got something in GitHub that can be reused by others. How big a population can it be reused by? It's likely, if it's in GitHub, it's likely for data scientists and other practitioners of those arts. For the physicians, the knowledge workers who are trying to extract the insights and make discoveries and data, they need a place where they can actually ask questions. And that's where this kind of low-code application development environment helps, because you can very quickly build and deploy apps that speak the language of the domain expert, and allow them to ask their questions as they come to them, as opposed to having to work with or through a data scientist to generate those insights.Harry Glorikian: But it is the data scientists that are helping build certain parts of this, right? So they're not excluded from the process. Tom Covington: So no, no, they're critical to the process. They basically, instead of most of the time, and Jesse can speak more fluently on this, or eloquently on this, but traditionally, if you have a request, you hand it off to a data scientist, they tend to do ad hoc analysis.So they're like, okay, what are, what are the tools that I've got at my disposal? What's the fastest way to generate this answer for the requester? And they will use various scripts and various languages and come up, generate the output. If there is a follow-up question, some of that may be reusable, but not all of it.And then the process of extracting, doing a follow-up question can take a lot of time. If the data scientist instead builds an analysis app that allows reparameterization and the ability to, for the end-user, to ask 10,000 variants that have a similar type of question, then they can do the work once, publish it, and then lots of people can use that basic workflow to answer their specific question. Harry Glorikian: So it's basically, over time, any organization will end up theoretically with a library of these analytic tools that then they can use in different variation. And so theoretically then maybe the data scientist can work on more complex issues.Tom Covington: Exactly. The more fun stuff. Harry Glorikian: Yeah. Okay. So, let's go to history, right? You guys started this in 2014. I don't even know if there was a low-code movement happening in tech in 2014. I'm not so sure. Jesse Paquette: WordPress is low-code. Tom Covington: I guess we started it a bit, a bit early. But it was, based on some of Jesse's, his kind of career as a bioinformatician and what he saw as shortcomings within the industry and the ultimate job of empowering physicians and scientists to make discoveries and, find insights quickly. He recognized that this was a constraint in the pace of innovation. And so we, when we started the company, nobody was talking about data mesh. I'm not even sure there was much around low code other than, as Jesse mentioned, in WordPress. But there has been a shift in the past, I would say, in the past couple of years towards data mesh as a an improved solution for data lakes and data warehouses. And low-code is the preferred path forward for developing software applications. Harry Glorikian: Yeah, Jesse. I mean, I think if I remember correctly, you were doing that, you were doing sort of analytics of gene sequence data at Life, right? So is this, is that where you, the epiphany came? Jesse Paquette: Before then, actually. I was working at the UCSF cancer center and as Tom described, I was in a situation where I was working with a number of really talented researchers, these knowledge workers that had interesting datasets, but it all required computational analysis. They were either too big or too complex. And I found myself repeatedly doing a lot of analyses. And at some point I thought,  what? I can start to automate this. I can start to automate that. And I put together a platform for a specific purpose. It's called EGAN, E-G-A-N, which stands for exploratory gene association networks. And it was basically a new way of looking at data as well as a new way of structuring data so that these analyses can be done more repeatedly, and in more of a workflow. Jesse Paquette: And then I went to Life Tech and worked on similar applications. I went to a company called Ayasdi in Palo Alto. That was they, they had a, just a blockbuster algorithm which is, which is still really cool. And they were building applications around that and I was working on their life science applications for them, and it really comes down to the user experience. Physicians, they need to be able to start with something and know how to use it out of the box or with very minimal training. And when they come back and when they have that question again, two weeks later, they want to be able to come right back to the application and use it like they're using email or they're using Google or just like using the, tapping on their phone. And, and it was, it was interesting. We started working in sports. And with the sports users specifically with an NFL team, our earliest iteration of our platform, what we had was a very complicated user experience. And we showed them how to do this really cool analysis analyzing when a certain receiver was getting passes and scoring touchdowns. And he said, well, great, but can you do it again for a different player? And I said, Oh yeah, well, I just have to click here, here, here, here, here, here, and here. The light bulb went off and we realized all we should have to do is just choose a different player. That's how it should work.Harry Glorikian: Yeah, but this is sort of like, I should be using it. Cause I'm asking questions all the time about a company or a technology or, and there's all this data behind it that I'm sort of putting together to do my analytics of what makes a good company, what doesn't make a good company. When is a technology on its upwardly mobile curve, right? So I'm, it's not the same type of data, but it's definitely data that I will make decisions based on. So a tool like this, I can see has more application than just where you guys are focused. But Tom, your background is mechanical engineering manufacturing, clean energy. How did you two get together and start a bioinformatics company? Tom Covington: Yeah, well, so Jesse and I have known each other for about 12, 13 years now. We played soccer together every Monday night and I knew a little bit about what he was doing. But one night after a match, we often would, go have a beer afterwards. And we started talking about, or he started talking about his idea. And his idea was essentially built on the foundations of EGAN, which he had developed to allow biologists to do some of their own pathway analysis when he was at UCSF. And as he started talking about it, I realized, I thought back to my time, because I was a race engineer for Honda for several years. And we were always generating large amounts of data at the track every weekend and trying to analyze that to improve the software, come up with new algorithms, new ways of controlling the engines. And I was pretty good at torturing the data in Excel, but that was the limits of my capabilities.And what I recognized that he was describing was a tool for people like myself, to allow me to rapidly find insights in complex data. And that was pretty appealing. And this is, as you kind of alluded to, is a fairly generic platform. We have aimed it at the healthcare and life sciences space, because from our perspective, precision medicine is a, it's a long described Holy Grail. There are some inherent challenges specifically with the kind of the disparate data sources and bringing them together. And, my wife is a physician at UCSF, Jesse's worked at UCSF, I've worked at UCSF. We kept getting pulled back into the healthcare life sciences space. And so we decided to focus there and we think it's a, satisfying and fantastic opportunity. At some point we may evolve beyond precision medicine, but for right now, we're very clearly focused on precision medicine and the opportunities that it provides.Harry Glorikian: So I like the word tortured, torturing data. I got it. I got it. I got to use that in a few places that, that, that I've always tried to be nice to the data, so it's nice to me, but I'm happy to torture it. And it does sound like there's a more of a generic application to what you guys are creating. I know that that everything requires some focus, but this does look like it could be used in a lot of other spaces that, even if you drew diagrams of, of adjacent areas that would give you that expansion.So I was thinking like, what does Tag.bio mean? And I'm thinking, Does, is it based on, Jesse's previous work of tag based analysis? Or how, how did, where did that name come up from? Jesse Paquette: Essentially? Yes. I mean, if we had to, if it had to answer quickly. Yes. I mean, Tom, I don't think has the whiteboard where we drew all of the possible names of the company and started to put together portmanteaus and stuff. I think his kids have long since drawn over that multiple times. We were going for a lot of things with the name. We wanted it to be short. We wanted to sort of not be a name that people had to ask, How do you say that? Which a lot of startups get, right? You try to come up with a really crafty way of spelling something. And then that's your first question is like, how do you say that? So it's clear. And it uses the .bio domain for better or for worse. And it really relates to the concept which we had initially, which was, which I had even farther back going back to UCSF which is based around treating categorical data as sets.And, and so it gets a bit into the mathematics of things. And that basically, if we talk about the set of patients who lived versus the patients who died right in categorical data, it's represented as sort of deceased or alive. Right. And, and many times algorithms are just going to look at that and treat them as words, or treat them as, as certain things as statisticians would.But if you consider those to be sets and you can start to intersect sets with others, like you have treatment, right? So some people were treated and some people responded well and some people didn't respond well. Some people weren't treated and they responded well or didn't respond well. And all of a sudden you started thinking about that using set mathematics tags is a good concept for that.Tom Covington: Yeah, the simplest explanation is we live in tagged data and we came from biology. Harry Glorikian: So you guys have been working on this for seven years, right? If my, if my math is correct. And that's enough time, for both the product and business model to have evolved. I'm assuming that it has a few times can you walk me through how the platform has changed over time or that how the concept for the ideal customer,  for the platform has changed?Tom Covington: Yeah. Jesse Paquette: Can I, if I could start from the technical side, I don't think that the form has changed really at all. It's, it's exactly what we designed seven years ago. It's just gotten a whole lot better based on all of the, the team members that we brought in to do the workforce, all the things that Tom and I don't do particularly well. We've been able to complement ourselves with cloud architecture people working on projects in specific healthcare or life science areas. But when it comes down to the core tech and how useful it is and how scalable it is, I don't think it's changed. So I'll let Tom talk about the business, because that has changed.Tom Covington: Yeah, we had our original vision was to essentially mirror the worldwide web, but for data. So in a worldwide web, you've got data, you've got web servers, you've got a communication protocol HTTP, and you've got browsers for interfacing with that content. And we wanted to mirror that for data. And so we have data servers, we've got a smart API as a communication protocol, and you can similarly access content on those data servers via a web portal. That concept is [gone, but] the platform has remained the same. What we've learned through customer interactions is how to improve the user experience and around accessing data. And I think that, in, in our explorations, in multiple verticals, speaking about that NFL team, like that really simple kind of aha moments like, Oh, that's going to be critical for kind of any user. And so we've learned a lot from the interactions with customers about how to improve the user experience. So I think from the platform perspective, and the kind of flexibility and generic applicability of it, we have by looking at a bunch of different verticals, initially, we, we learned what was going to be core across verticals. Tom Covington: Part of the reason for the focus on healthcare life sciences is they, on the surface they look pretty different in terms of their data types. But if we have, we've developed a platform that can be kind of agnostic to data types and analysis types. And so, it is well-suited to marrying two disparate types of data together. And so for us, the opportunity of precision medicine is one that. Kind of emerged from those realizations and those learnings from other customers from the types of people that want to use it and the, the, how the businesses evolved. Originally, we started with kind of researchers, people that were not quite high enough in an organization to make buying decisions. We've since learned and we, now approach it a higher level within an organization. And that makes—because this is a concept that requires It's different enough that it requires some vision and some, there are various users within an ecosystem, whether it be on the IT side security side, all the way up to the end user domain experts. And so you, you need to approach at a high enough level of an organization that they can see the vision. And be receptive to the idea that the current status quo is not working well enough and not fast enough. And the cost of answering your question from data is just far too high. And if it is that high, you were fundamentally limiting the pace of innovation within an organization.Harry Glorikian: Yeah. I mean, because I was thinking to myself, I'm like, the next level would be like, again, if somebody writes the analytics part that can be reused at multiple organizations, right. That just theoretically speeds everything along, regardless of the data source that it's ingesting. But how did you guys come about this whole idea of like, quote, “analysis apps” and do you guide users to like, this might be the right one for you to click on, to use for this? Or do you guys just provide the platform? Tom Covington: Jesse. Do you want to take that? Jesse Paquette: I mean, there's the technical aspect and then there's the business aspect. I'll talk about the technical aspect and it's something that we're learning about with every interaction we have with a user or a customer. With big organizations there are policies in place they're either formalized SOPs or there are rigid sort of cultural silos and, and things like that. And it, and as everybody knows, even if you have the most useful thing, if you don't Institute some form of change management or training within the organization, you're not going to get the adoption that you need, even if you just have the best tool ever. If you put Google in front of somebody who's never seen Google before, they still might not use it unless you actually turn on their phone and point their fingers at it. And so we do make some effort to onboard users.We think it's very useful. We also then get to observe their experience and learn about the naive user experience. Something we care about specifically. And the experienced user is also important. We find that we have some power users who just love our system and they have no problem trying to do all sorts of fancy things with it, to the point where they want more apps. And, and at that point it's, it's up to us or their in-house development team to start giving them some more apps on some, maybe some new data that they need. And it's, so we, we do spend a fair amount of time with our users. Yeah, Tom?Tom Covington: Yeah, I think I'm kind of from a big picture perspective. Like the platform is flexible enough that you can build very simple apps and also very sophisticated apps. So, an example of a simple app would be, how much does this particular drug cost within a hospital system? That's a simple dropdown, any user can see the title of the app and click on it and know exactly what it's going to do. And you get into more complicated, where it may be doing some advanced clustering algorithm, and you've got to select the cohort that you want to look at. But it's the, it's designed so that the data scientist developer of these apps can write them in a way that will speak to the end user.So, a healthcare app is going to a physician who is gonna understand that intuitively versus a researcher at a large pharma organization, they're gonna have different data, different analysis needs, their apps are gonna speak their language. And so it's a lot of it is down to, and this is one of our learnings through these various customer interactions, was that we need to enable the building and deployment of apps that speak the language of the domain expert and make it really easy and intuitive for them. When they just, they see an app they're like, “Oh, I know what this is going to do automatically because I can, I recognize the, the analysis methodology, or I recognize the data fields in there.”But it's, it's all tied around making the user experience as easy as possible. So there is minimal onboarding. One of the things that other software platforms that allow analyses don't do so well with is the user experience. You've got, just think about something like Excel. If I build an Excel model and then share it with you, you may have questions or concerns about tweaking anything, because you don't know what went into that Excel model. And you can add all sorts of things. You can do all sorts of things. There's all, there's all sorts of functionality available within the front end of Excel. And honestly, there's too much complexity. And even Excel can be over overwhelming to somebody who hasn't used it before. And we're trying to make something that the least sophisticated computer user would be able to understand just from clicking around and trying it and running an analysis. Harry Glorikian: I should start using this myself for all this stuff I try to do. But how hard is it to sell the product, and the big ideas behind it, to potential customers. I mean, do they, do they go like, “Oh my God, I totally get it. Now I'm jumping on this.” Or is it,  I don't want to call it a slog, but how much education does it take for an organization to get this big idea?Tom Covington: Yeah. So it previously has been a slog, because there is enough, it is enough of a shift in the thinking that it takes some time for them to understand and use cases and deployments. Some of the large pharma and health care organizations that we're currently at, it has certainly helped. The other thing that has really helped make things go faster is the recent kind of adoption of data mesh as a kind of a new paradigm for the next generation of data lakes and data warehouses. Domain-specific data products, the fact that other people are talking about that.And then, we essentially built to that seven years ago, has certainly made things easier. It's, there's less education that has to happen from us respective to a customer. Also low-code, that is something that, for the most part you can just say, and that people kind of intuitively understand because there are other examples in the marketplace. And so I think that, we started the company pretty early relative to where the market was. But now the market is kind of catching up in terms of understanding the core concepts. And so that has made customer acquisition a lot easier. Jesse Paquette: I'd like to add one more thing. So we've been talking a lot about end user experience. And that's been our primary focus from the beginning. Over the last couple of years, we have learned about a second domain of user experience, which is equally important, which is the developer experience. And we've always been trying to support our internal developers and our collaborator developers and our customer developers but working on improving their experience.So if they're data scientists, they should be able to work natively in R and Python to develop on our platform, they should be able to bring in their own algorithms into our platform in their own visualizations. If they are more of a front-end application developer, they want to use JavaScript. And they're okay using the JSON low-code templates to configure the platform and the data nodes. If they're data engineers, they're going to be working on the data plumbing layer, and we need to have a very good API system and set of SDK software development tools, right, for mapping the data in, from the, the, the state-of-the-art data platforms that they're very proud of.So we want to fit very nicely within the things that people have already been building and in doing so we find that customers are, the reception that we're getting is much more positive because instead of saying, “You've got to throw away all this stuff and use tag.bio,” it's, “Well tag.bio fits right here, and it fits right there and it could fit over there, but you're using that other thing. So we'll just wait on that one for a while.” Harry Glorikian: Okay. So somebody buys this and puts it in place, starts to utilize it. How do you guys measure, I don't know, a payback. How do you measure advancement? How do you measure impact? Because right. All of this is to make life easier, faster, and find that, billion dollar molecule, if you're looking at it that way faster or identifying a patient that would benefit from something faster, right. I'm assuming there are lots of use cases that you guys have. So how do you, measure the “Holy shit? I found it” moment. Tom Covington: Yeah, that's a great question because, so one of the things that the platform kind of inherently does is it keeps a history of every analysis that's been run. So when a user has a full history of their analysis, so, thinking back to, if you're thinking about an Excel model, any tweak you make to an Excel model, you may notate by just changing the file name. In our world, every analysis that's been run is annotatable, it's replayable, it is shareable. So you've got a user history, then you've got an organization's user history. So across all data nodes, all users so from an ROI perspective, the simplest metric is: how many more questions are you able to ask of your data than you previously could? The quick answer is it's about 1000x more. Just by short-circuiting the process to ask and answer your question, people ask a lot more questions, not surprisingly. The other is, we hear from the customers. Their direct feedback on like, how impactful it's been, how much has changed the culture of the organization, how people are now talking about data the same way. Whereas previously, the domain experts, the knowledge workers talked about data in a different way than the people who are actually practicing the arts of extracting information from data. So they, we see it on the cultural side, but then we also hear use cases, say, one of our large AMCs. They're using it right now for strategic financial recovery after COVID and they've been tasked with, how do we reduce costs, increase revenue still while maintaining or improving care. And, there are examples from that that are in, literally in the millions of dollars, just from one physician asking questions over the course of a couple of hours, able to identify opportunities and then, surface those and they implement them and sure enough, it's dramatic in terms of the impact to the organization.So those are the kinds of, that's the feedback that we get. And so that's why the use cases are so impactful when we engage with new customers, we can say, look, this is, this is what was possible at organization X. And this can be similarly possible with, for you and your organization. Harry Glorikian: Yeah. You almost want to publish all that to make sure that everybody gets the message because that's the goal, right?Tom Covington: Yeah. There will be publications that come out of this because some of the work they're doing and the impact it’s having on organizations are, is going to be replicable at other places. And it's there are novel ways of thinking about data, looking at data that they get to leverage via tag.bio that fundamentally is going to change these organizations for the better.Jesse Paquette: I'd like to bring up one thing and it kind of relates to what Tom was saying. And it sort of boils down to a bit of an ethos that we started with, which back in 2014 was sort of completely contrary to the hype of AI that was happening between say 2014 and 2016, we would talk to a lot of folks and they would say, are you AI? And we would have these debates about, Tom, do we actually say we're AI? And we think, okay, now we're going to say we're AI because everyone cares about it. And then we would think, no, we are definitively not AI. While we have machine learning algorithms under the hood, we are first and foremost focused on the knowledge and the discovery power of the knowledge worker, the physician who has 20 years of experience in the ER, the, the biochemist who's been working at a pharmaceutical company and in academia for, for 20, 30 years. They have so much information and their community of peers has so much information, detailed knowledge data inside their brains that is not being joined properly with the data that exists in these databases. And that's really what we're trying to do is bring those two together. And it's interesting to try to quantify as Tom was talking about we're working on those metrics. Harry Glorikian: So who do you guys see as your competitors? Because when I hear low-code and things like that, there's, I immediately go to the tech side. Right. Because they're all, the valuations are off the chart right now on some of these things, but who do you see as competitors and how do you differentiate from them? Tom Covington: That's a great question and it's one we've gotten a lot. So there are, we kind of tie three areas together, there's this data engineering aspect, there's the data science aspect, and then there's the end user experience. We have competitors in all three of those areas, but there are none that span those three areas. So we may have folks that are doing some really great work on the data engineering side, or maybe on the data science side, or even in the end user software side. But there are none that currently link those three together, those three legs together. So some of the competitors may start to approach us in certain avenues in certain areas, but there is not a kind of end to end solution that takes generally analysis-ready data, marries it with these data science capabilities, and then turns that into low-code application platform. So, for the time being, we're a bit unique. But I, obviously as we start to gain more traction, they're going to be people that are going to start trying to approximate what we're doing. And, we're anticipate that look forward, look forward to the competition. But realistically right now there's, there's no great solution that kind of packages up those three legs that we span. Jesse Paquette: We've encountered a lot of potential customers or customers of ours that had previously tried to stitch together a solution which didn't look like ours, but it was trying to solve the same problem. Really connects those three layers, the algorithms, the data engineering and the end user experience. And they're trying to stitch them together using open source components. They're basically trying to support a whole software environment within either a pharmaceutical or a healthcare organization. And it's really hard for them to sustain, the technical debt mounts, and the project eventually fails.So we, we do see that people, like a customer, for example, we would approach a large pharma or big healthcare institution. They are familiar with the problem. They probably have an in-house solution that they either built, or they had some consulting firm coming in and build for them. And some people in that organization feel rather proud of that thing that they've built. And other folks absolutely hate it because it doesn't solve 80% of their problems. And it's an interesting environment to get into, but it's usually not another vendor. It's an in-house self-built solution. Harry Glorikian: Yeah. Tough to get over some of those issues. I know if one of my partners was here, the first question he'd be like is, I'm sure you guys are filing IP on some of this. So hopefully you guys are able to protect it and create at least a moat around what you guys are building. Because it does sound like it was way ahead of a lot of the competitors. Tom Covington: We have filed for some patent protection, or some patents, yes.Harry Glorikian: So, COVID seems to have had an impact on, it seems like every organization I talk to these days and some of it has caused things to move a lot faster. Have you guys seen an acceleration of your business and, or are there places where people have said, yeah, your system is how I'm going to help find a solution from analyzing patients in COVID I'm looking at it from both sides, right? Where the telemedicine came whooshing in, because everybody needed it. And so I'm trying to figure out like, did it accelerate your business? And then through the acceleration, did it actually help identify opportunities in patient populations? Tom Covington: Yeah, so it hasn't been as dramatic as say telemedicine because that was, clearly everybody needed that right away. And so there was a big push in that effort. But it has accelerated certain aspects because, once you've got COVID patients, you want to understand that patient population and, understand you want to be able to do research on those patients. And so from that perspective, it has accelerated some business. Specifically there's a large AMC that wanted to be able to look at, do analyses on their COVID patient registry and they wanted to create a COVID patient registry.And we were able to get that up and running for them in about five days which allowed their researchers to do some pretty sophisticated analyses around survival, looking at what the makeup was, what was correlated with folks that ended up being, for example, intubated. So there was a clear need on their part to very rapidly be able to perform analysis on their COVID patients. And tag.bio was able to fill that need very quickly for them. And so I think there are other examples like that, that have been accelerated via COVID or the pressing need of COVID. But there's, it's also not as high a priority, say as telemedicine. So I think it's been good for us in general. But I also think it is not quite as bright and shiny as the, “Oh my God, we need a solution for how we can continue to see patients when they can't come into clinic.” Jesse Paquette: I would add that I think what we're doing is we're riding a much larger, but slower moving wave because of COVID, which has to do with cloud adoption. We are working with a number of cloud providers as channel partners and within the healthcare and life science space, there is a lagging surge in cloud adoption. And we're seeing more interest in our platform more, more meetings, more proof of concepts, more and more getting through the stages of the sales cycle, which, usually it's a really long sales cycle in healthcare and life sciences. You have to get a lot of people to approve. You have to go through the security approvals and, and the risk assessments and, and you get the right people to sign off at all levels. There's a lot of stakeholders within the organization. But being part of this cloud wave means that it's, that the organization has already decided we're going to pick one of the major cloud providers. We're going to build out more infrastructure, perhaps all of our infrastructure on that cloud. And it's this sort of new green field opportunity where applications useful applications like ours can come in and be easily adopted compared to the older model where there's more inertia.Tom Covington: Yeah, that's a, that's a great point. Yeah. Harry Glorikian: So what have I have I not asked you guys? I mean, I'm also thinking about like,  how does all this data, does the platform actually let you also visualize some of it? Cause I can see the things I like to see in certain ways, make it easier for me to tease things apart when I'm looking at it. But what have I not asked you about your platform that you think I missed?Tom Covington: It's a good question. I mean, I think one of the things that we are realizing is that there's a lot of value in having full provenance of analysis and have kind of a full history. It creates an additional essentially additional data source for how data are being used within an organization.So being able to understand which data nodes are of value, which analysis apps are of value. We talk about UDATs or useful data artifacts, and those could be gene signatures. That could be a particular cohort of patients. But those UDATs that get discovered via the platform and then get shared via the platform. And then the visibility on those is accessible to the kind of senior leaders within an organization. You start to understand the value of your data a lot better. And right now, particularly on the life sciences side, and even on the healthcare side, they may have immense volumes of data that are not being utilized. They're being stored because they believe there's value in them. But the time to extract that information is so high and the cost associated asking questions is so high that you don't have a good sense of like, what are valuable datasets, what are valuable analysis applications? And, we've, we provided this additional useful dataset of, for an organization around where the greatest value I, and there were organizational within their industry and within their infrastructure.Jesse Paquette: I'd like to extrapolate on that. If I could again, to quote our VP of customer Mark Mooney, we think about it this way. Even if you have the most useful data analysis application on top of your data right now, what happens is that people use it and you get information and you start to save it to your computer. You start to take it away from the system to be able to take action on it. Maybe for example, in health care, you might realize that if you do something in the ER, you're going to improve patient care and improve your bottom line. And it's a really useful thing. What Tom had just described the useful data artifacts means that there's a gravity in our system, that all of the useful things that are found and created in our system, right. They stay central to the system with attribution and provenance about who made them and who created them. They become shareable units of information and reusable, which is a very different paradigm than other analysis systems. Say, if you take your favorite visualization app, you're going to take something away. You're going to send it to somebody in an email. It goes away from the system. And ours is really trying to bring all of the useful things that were created from the system and keep them there so that they can be found and reused. Harry Glorikian: Yeah, I'm almost thinking like you would rank these, you would, at some point be able to rank them to let people know which ones are more or less useful and maybe why they were useful. Right. Which might generate more of that type of data. Tom Covington: Exactly. Harry Glorikian: Wow. So great learning about this. Because I have to admit, when I started reading about this, I'm like, I'm going to get in over my head really quickly, but this was incredibly useful. It sounds like something I almost wish was self-serve and I could use it for some of the stuff that I have, but it sounds like it's more, you have to deploy it within a certain network, as opposed to one individual like me utilizing it.Tom Covington: We are, we are coming for you though. It's going to be probably a year and a half or so, but yes, ultimately we want to empower people like yourself to be able to deploy these, set, set up a system like this for yourself relatively easily.Harry Glorikian: This was great. I look forward to keeping in touch and hearing how this evolves, and maybe one of these days I'll be your beta user to try my own data analytics and see how we can use it for our own organization.Tom Covington: That would be fantastic. We would love to help. Harry Glorikian: Thank you so much for joining me today. Tom Covington: Thank you very much for having us. We really appreciate it. And we enjoyed the conversation.Jesse Paquette: Thanks, Harry.Harry Glorikian:That’s it for this week’s show. We’ve made more than 50 episodes of MoneyBall Medicine, and you can find all of them at glorikian.com under the tab “Podcast.” You can follow me on Twitter at hglorikian. If you like the show, please do us a favor and leave a rating and review at Apple Podcasts. Thanks, and we’ll be back soon with our next interview.

Pek Lum, co-founder, and CEO of Auransa believes that a lot fewer drugs would fail in Phase 2 clinical trials if they were tested on patients predisposed to respond. The problem is finding the sub-populations of likely high-responders in advance and matching them up with promising drug compounds. That’s Auransa's specialty.The Palo Alto, CA-based drug discovery startup, formerly known as Capella Biosciences, has a pipeline of novel compounds for treating cancer and other conditions identified through machine learning analysis of genomic data and other kinds of data. It’s closest to the clinical trial stage with a DNA-binding drug for liver cancer (AU-409) and is also working on drugs for prostate cancer and for protecting the heart against chemotherapy drugs. The company says it discovered AU-409 as part of a broad evaluation of data sets on a range of close to 30 diseases. The company’s discovery process uses a platform called the SMarTR Engine that uses hypothesis-free machine learning to identify druggable targets and compounds as well as likely high-responder patients. Lum  calls it “interrogating gene expression profiles to identify patient sub-populations.” The company believes this approach can identify unexpected connections between diverse molecular pathways to disease, and that it will lead to progress in drug development for intractable conditions with poorly understood biology, including cancer and autoimmune, metabolic, infectious, and neurological diseases.Lum co-founded Auransa with Viwat Visuthikraisee in 2014 and is the chief architect behind its technology. Before Auransa, she was VP of Product, VP of Solutions, and Chief Data Scientist at Ayasdi (now SymphonyAyasdiAI), a Stanford spinout known for building hypothesis-free machine learning models to detect patterns in business data. Before that, she spent 10 years as a scientific director at Rosetta Inpharmatics, a microarray and genomics company that was acquired by Merck. She has bachelor's and master's of science degrees in biochemistry from Hokkaido University in Japan and a Ph.D. in molecular biology from the University of Washington, where she studied yeast genetics.Please rate and review MoneyBall Medicine on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:• Launch the “Podcasts” app on your device. If you can’t find this app, swipe all the way to the left on your home screen until you’re on the Search page. 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Thanks!TRANSCRIPTHarry Glorikian: I’m Harry Glorikian, and this is MoneyBall Medicine, the interview podcast where we meet researchers, entrepreneurs, and physicians who are using the power of data to improve patient health and make healthcare delivery more efficient. You can think of each episode as a new chapter in the never-ending audio version of my 2017 book, MoneyBall Medicine: Thriving in the New Data-Driven Healthcare Market. If you like the show, please do us a favor and leave a rating and review at Apple Podcasts.For every drug candidate that makes it all the way through the three phases of clinical trials to win FDA approval, there are about 20 others that fail along the way. Phase 2, where drug makers have to prove that a new drug is safer or more effective than existing treatments, is where a lot of drugs falter.But often, it’s not because the drugs don’t work. Sometimes it’s just because they weren’t tested on the right patients. Meaning, the people in the treatment group didn’t happen have the right genes or gene expression profiles to respond. If you could find enough patients who were likely high-responders and try your new drug just on them, your chances of approval might go way up. The tough part is identifying those subpopulations in advance and matching them up with promising drug compounds.That’s where a company like Auransa comes in. It’s a Palo Alto startup that has built an AI platform called the SMarTR Engine. The engine uses public datasets on gene expression to identify subtypes of molecular diseases and predict what kinds of compounds might work against specific subtypes. Auransa used the engine to discover a drug for liver cancer that’s about to enter clinical trials. And it’s licensing out other drugs it discovered for prostate cancer and for protecting the heart against the effects of cancer chemotherapy.Some of the ideas baked into the SMarTR Engine come from a sub-field of artificial intelligence called hypothesis-free machine learning. And joining us this week to explain exactly what that means is our guest Pek Lum. She’s a biochemist and molecular biologist who worked at the microarray maker Rosetta Inpharmatics and the software company Ayasdi before founding Auransa in 2014. And she says one of the real revolutions in drug development is that almost every disease can be divided up into molecular subtypes that can best be treated using targeted drugs.Harry Glorikian: Pek, welcome to the show.Pek Lum: Thank you. Pleasure to be here.Harry Glorikian: You know, I always try to ask this opening question when I start the show to give the listeners a good idea of of what your company does. But you guys are in in drug discovery. What tell us how people understand what is the basic approach that you guys have. And I'll get into the special sauce later. But what do you guys do in the drug discovery space?Pek Lum: No, that's a really great question in the sense that when we first started in about five years ago, we... I've always been in the drug discovery field in the sense that I worked for over 20 years ago at that time in a company called Rosetta Inpharmatics, which is really pushing the cutting edge of thinking about using molecular data. Right. And to solve the mysteries of biology. And I was extremely lucky to be one of the core members in when we were very small. And then that really kind of put me in the sense put me in the stage where I could think about more than just one gene. Right. Because the technology was just kind of getting really kind of I would say not rolling forward, like propelling forward, with microarrays.Harry Glorikian: Yes.Pek Lum: So I was part of the whole movement and it was really amazing to be kind of like, you know, in the show as it runs, so to speak. And so and then Merck bought us after we went public and worked for Merck and Co. for another eight years, really learning how technology, how we should apply technology, how we can apply technology, molecular data, RNA data, DNA data to a drug discovery pipeline. And really kind of figured out that there are many things that the pharmaceutical world does very well, but there are many things that it also fails in and that how can we do it better? So I've always been in the mindset of, when starting Auransa with my co-founder, How do we do it better? And not only just do it better, but do it very differently so that we can address the most, I would say critical problems. So Auransa is really a company started by us to address the problem of why drugs actually fail a lot when we go into a Phase II efficacy trial. Right. Is not like the drug is bad or toxic. And most of the time is you can find enough responders to make your clinical trial a success.Pek Lum: And that cause, I guess, drugs actually made to maybe against one target. You don't really think about the biology that much at the beginning or the biology responders. So Auransa was really created to think about first, the heterogeneity of the disease and the heterogeneity of patient response. So we start from looking at molecular data of the disease from the get go. We take RNA, is really the RNA world is coming back with the vaccines.Harry Glorikian: Right.Pek Lum: And the RNA has always been fascinating because it tells you about the activity of the cell, of a normal cell versus a disease cell. So we use RNA transcriptomes right, transcriptomics to study the biology and the heterogeneity. So our algorithms, there are many algorithms, one of the first algorithms of the engine is really to look at the biology of heterogeneity, whether we can subdivide a disease into more homogeneous categories before doing anything.Harry Glorikian: Right. Yeah, I remember when, because when I was at Applied Biosystems, I remember Applied Biosystems, Affymetrix and then Stephen Friend starting this and like, you know, it was all starting back then. And I want to say we sort of had an idea of what we were doing, but compared to now, it's like, wow, how naive we were back then compared to how much this whole space has evolved. And it's interesting you mention, you know, RNA and its activity because in a couple of weeks, I'm actually going to be talking to a spatial genomics company so that you get a better idea from a visual standpoint of which cells are actually activating and which aren't.Harry Glorikian: But so, you've got an interesting professional career, and I say that because you were working at a big data analytics company for a while that was utilizing an approach that was hypothesis-free machine learning, where the machine was sort of identifying unique or aspects that you should be paying attention to. Maybe that it was seeing that instead of you going in there saying, let's just look over here, you could see what the machine was seeing for you. How much can you tell us a little bit about that experience? And then how did that influence what you're doing now? Because I have to believe that they superimpose at some level.Pek Lum: Right. I think, you know, ever since my first job at Rosetta and then my subsequent jobs really kind of culminated into this into this tech, as you see today. Right. All this experience and certainly experience while being a founding member of a small team at that time of Ayasdi, which is the software company, has been also an eye-opening experience for me because we were trying to create, using a very old mathematical idea called topology, or TDA, really start to figure out whether there's maybe there's some things that can't be learned. Right. And so typical machine learning methods need a training set or a test. But there are just some things where you don't really know what the ground truth is. So how do you do that? So that's the idea of like I say, the hypothesis-free approach. And the approach that that that the tech company, the software company that we built is really around the idea that not everything can be learned. But you can actually adapt some very interesting ideas around a hypothesis-free approach and then use it in a machine learning AI framework. So I definitely have been influenced by that thinking, you know, as I as we built the software.Harry Glorikian: Right.Pek Lum: And also, when we were Rosetta, we were generating in parallel, data on thousands of genes. And often at that time we were called, "Oh, you're just going fishing," you know, but fishing is not a bad idea because you don't really know which part of the ocean you need to go to catch your Blue Marlin, for example, right?Harry Glorikian: Yeah, no, no, absolutely.Pek Lum: Fish a little bit, not the whole ocean, but, you know, to get some, I would say, boundaries. Right. So in that sense, to me, a hypothesis-free approach gives you the boundaries where you can look. So, you know, so the experience, definitely the idea that you can use methods or thinking, algorithms, that could help you in a field where you do not know the ground truth. Like patient heterogeneity, I would say nobody really can pinpoint and say, OK, I can say that, oh, this is THE subtype, these are THE markers. And therefore, I'm going to go after this. And there are many. I guess, for example, you can think of a Herceptin as a great example, right, but when you first started, you know, it was like, wow, OK, you're going to go after a target. And then the idea of really kind of subtyping breast cancer, you know, I don't know, 20, 30 years ago. Right. And we're still learning about, you know, in a patient heterogeneity and we're just beginning to scratch the surface. So for Auransa, we wanted to use a method very much like the thinking that and the idea that we had, you know, when we were when I was at Ayasdi, is that you could search with some parameters, you know, a very complex space without needing to say, this is my hypothesis. This is that one gene, because we all know that if you have a target, you know ... to have to respond you need the target. But if you have the target, it doesn't mean you're going to respond. Because things below the target or above the target are much more complex than that.Harry Glorikian: Correct. And I always feel that there's, you know, I always call them low hanging fruit. Like the first one is, OK, well, it's either luck or skill, but I got to one level. But then you start to see people that are not responding. So that means something else is going on and there's subtypes. Right. So it's funny how we always also call it "rare diseases" in these smaller population. I'm pretty convinced that at some point everything is going to be a rare disease. Right. Because of the subtypes that we're going to start to see. I mean, even we're seeing in a neurological now, or Alzheimer's. There's subtypes of Alzheimer’s. No! Really? Shocking. Amazing to me that there's subtypes. Right. We've been dealing with this for ages. And I do believe that these technologies are so good at highlighting something where a human might not have seen it, might not have understood it. You know, I was I was interviewing actually I just posted it today on imaging and agriculture. And they were saying that sometimes the machine sees things that we don't fully understand how it sees it, but it sees it and points it out, which allows us now to dig into it and be able to sort of identify what that unique feature is that the machine has pulled out. I'm not sure I want drug discovery and drugs being based on something we don't fully understand, but the machine highlighting something for us that then we can go dig into, I think is an interesting greenfield space that that we need to explore more.Pek Lum: Right. I think you're absolutely right. You know, when we first started Auransa, that was the idea that we had. And then my co-founder and I thought, what if we find like hundreds of subtypes? We're never going to be able to make a drug again a hundred subtypes. So let's hope we find a small enough number of buckets that we can say this is approximately what it looks like, to be able to be practical to find drugs against those subtypes. So when we talk about subtypes, we are talking about you're absolutely right, it's like a leaf on a tree and that we have to cut it off at one point. Enough that things that, OK, this is homogeneous enough that actually makes sense out of it. And that's where the engine, that's what the engine does. Basically, it takes data, very, very complex data, things that we could never figure that out ourselves and say this approximately five, six buckets. So we've actually not found hundreds of subtypes, otherwise we probably would not have started Auransan, because it would have been impossible. But instead, we find n of one, but maybe a five to seven subtypes at most. That is enough for us to say, the machine says, OK, it is homogeneous enough, go for this. So that's kind of where we are, where we start at Auransa. And I think that's an important concept because people often thought about precision medicine as being, oh, I'm going to make a medicine for you and you only. But actually you could learn from, say, breast cancer, and that's approximately people with estrogen-receptor-positive tumors. And then you will likely respond to a drug like Tamoxifen. And even though we know that the response rate is only about, I think maybe 30, 40 percent. Right. But that's really good. At least at this poibt. So that's where we how we think about the engine as a shining light on a homogeneous enough population that we can actually make a drug against that.Harry Glorikian: Yeah. So that sort of leads us into you have this technology that you've termed SMarTR, S-M-A-R-T-R engine. Right. What does that stand for?Pek Lum: You know, that's my one of my rare occasion where I put my marketing hat on. I don't like marketing all. And we so and you notice the Mar is big-M, little-a-r. So S is for Subpopulation. Markers. Targets. And Redefining. Because I needed it to be Smartr.Harry Glorikian: Ok, ok. So and when you like when you've described this in the papers that I've looked at it, it's a machine learning mathematical statistical approaches, highly automated and totally runs in the cloud. So can you give us a little more color on the sort of the highly automated, and why is that so important?Pek Lum: Right. It's important because it comes from my own experience of working with, like, amazingly talented implementations and data scientist at the at Merck or I know how it goes where biologists will often ask them for something and they would run their magic and they'd give us an Excel sheet or a PowerPoint. Right. It's always a one-off one of those and one of that because you know, biologists are kind of one-off. So the idea of of us building this engine is not just equipping it with algorithms. So first of all, we don't have one algorithm, a hammer looking for a nail. We have a problem to solve. The problem is how to find novel drugs, drugs that people have never thought about, for patient populations that will respond.Pek Lum: So with that in mind, we built a pipeline of algorithms that starting from thinking about heterogeneity, to understanding preclinical models that reflect the biology of human subtypes, to predicting drugs and targets for those, and getting biomarkers for the patients when we go to the clinic. And we have different algorithms for each step of the pathway. So instead of having my team do a one-off thing, we know that if we don't do good software engineering it's going to be problematic because first it's going to take a really long time. This will be kind of higgledy piggledy in Excel sheets and we might be able to solve one thing. But to do this as a platform and as a pipeline builder, it would be impossible without good engineering practices. So we wanted to put this in, like I say, in a framework where everything is connected, so where it gets to run faster and faster through better algorithms, through better software engineering. And this really kind of came from my experience to at Ayasdi, a software engineering, a software firm. And also my co-founder who is a physicist and a software engineer, that we need to have good software practices. So what we did was we built first. We don't want any servers. Everything is done on AWS and is done in modules. So we create algorithms for each part of the pipeline, of the in silico pipeline. And then we have in such a way that when we take data in, when we ingest data, that we also automate it, and then by the time it ingest data and it spits out, I would say, what subtypes of disease, what biomarkers could be used in the clinic, what targets are interesting to you, what compounds from our digital library of compounds may be effective for that. Everything is more or less connected and could be done up in the cloud and now it finishes in about 24 hours.Harry Glorikian: When do humans look at it to say hmmm, makes sense. Or maybe we need to tweak the model a little. Right. Because it's not making sense. When does that happen?Pek Lum: So we, it happens at several steps. So within our engine we actually have benchmarks in there that we run periodically. You know, for example we have about about eight to ten data sets that we have for breast cancer, thousands of patient tumors. And we know approximately that it should be discovering, and it has discovered ER+ flavored subtypes, ERBB2, HER2+ subtypes, triple negative subtypes. So that is kind of like the rails that we put into our engine as well to make sure that when we actually do tweak an algorithm, it still has its wheels. But what we do is at this point, we generate out all the in-between data, but it's kept on the cloud. And once it's up, when it outputs the the list of things, the biologists actually, I would say the biologists with a knack for computation, we look at it and I myself look at it. I love to do data analysis in my spare time when I'm not doing CEO stuff. And we can see that we will look at once it's done that it also allows you...Ok, so this is an interesting one. The engine on the cloud outputs all of this. And right now, let's say my CSO, who is not a computational person, or me, or whoever really would be kind of a big pain to kind of go up and install the stuff and look at the things, some things you can't see. So what we did as a company is to build another kind of software, which is the visualization software on top of that.Pek Lum: So we have on our other end a visualization software that we call Polo because it's exploring that basically connects everything the SMarTR engine has done into something that's visualizable. It has a URL, we go to it and let's say, for example, my CSO wants to know, OK, the last one you did on head and neck cancer, you know, how many subtypes did you find? What is the biology, what's the pathway? And it could do all of that by him just going then looking at things. Or he can actually type in his favorite gene and then see what the favorite gene actually is predicted for how it behaves across over 30 diseases, and you can do that all at his fingertips, so we have that part of the engine as well, which is not the engine. We call it Polo, which is our visualization platform.Harry Glorikian: Right. It's funny because one of the first times I interviewed Berg Pharma and they were talking about their system, I was like, if you put on a pair of VR glasses, could you see the interconnectivity and be able to look in a spatial.... I was on another planet at the time, but it was a lot of fun sort of thinking about how you could visualize how these things interact to make it easy. Because human beings I mean, you see a picture. Somehow we're able to process a picture a lot faster than all this individual data. I think it... I just slow down. I rather look at a visual if it's possible.Pek Lum: It is so important because, you know, even though the engine is extremely powerful now, takes it 24 hours to finish from data input to kind of spitting out this information that we need. Visualization and also like the interpretation and just kind of making sure kind of like the human intelligence. Can I keep an eye on things. The visualization platform is so, so important. That's why I feel like that we did the right thing in making and taking time, putting a bit of resources to make this visualization platform for our preclinical team who actually then needs to look at it and go, OK, these are the drugs that are that are predicted by the engine. Can we actually have an analog of it or does it have development legs? Does it make sense? Does the biology makes sense. And so now we're basically connected everything. So you can click on a, you can find a drug in a database and it will pop up, you know, the structure and then it will tell you, hey, this one has a furan ring. So maybe you might want to be careful about that. This one has a reactive oxygen moiety. You might want to be careful about that. As we grew the visualization platform, we got feedback from the users. So we put more and more things in there, such that now it has a little visualization module that you can go to. And if you ever want to know something, I can just, I don't have to email my data scientist at 1:00 am in the morning saying, hey, can you send me that Excel sheet that has that that particular thing on it that I want to know from two weeks ago? I can just go to Auransa's Polo, right? As long as I have wi-fi. Right. And be able to be self-sufficient and look at things and then ask them questions if things look weird or, you know, talk to my CEO and say, hey, look at this. This is actually pretty interesting. And this one gets accessed by anybody in Auransa as long as you have Wi-Fi.Harry Glorikian: So so it's software development and drug development at the same time. Right. It's interesting because I always think to myself, if we ever, like, went back and thought about how to redo pharma, you'd probably tear apart the existing big pharma. Other than maybe the marketing group, right, marketing and sales group, you tear apart the rest of it and build it completely differently from the ground up? It was funny, I was talking to someone yesterday at a financial firm, a good friend of mine, and it's her new job and she's like, my job is to fully automate the back to the back end and the middle and go from 200 people down to 30 people because we're fully automating it. I'm like, well, that sounds really cool. I'm not really thrilled about losing the other 170 people. But with today's technology, you can make some of these processes much more automated and efficient. So where do you get your data sets that you feed your programs?Pek Lum: Yeah, let me tell you this. We are asked this a lot of times. And just kind of coming back again for my background as an RNA person. Right. One thing that I think NIH and CBI did really well over 20 years ago is to say, guys, now we no longer doing a one gene thing. We have microarrays and we're going to have sequencing. There's going to be a ton of data. We need to start a national database. Right. And it will enable, for anybody that publishes, to put the data into a coherent place. And even with big projects like TCGA, they need things that could be accessed. Right. So I think it is really cool that we have this kind of, I would say, repository. That unfortunately is not used by a lot of people because, you know, everything goes in. That's a ton of heterogeneity. So when we first started the company, before we even started the company, we thought about, OK, where is it that we can get data? We could spend billions of dollars generating data on cells, pristine data, but then it would never represent what's in the clinical trials without what's out there in the human the human world, which is the wild, wild west. Right. Heterogeneity is abundant. So we thought, aha, a repository like, you know, like GEO, the Gene Expression Omnibus, right, and ANBO or TCGA allows this kind of heterogeneity to come in and allows us the opportunity to actually use the algorithms which actually have algorithms that we look for. We actually use to look for heterogeneity and put them into homogeneity. These kind of data sets. So we love the public data sets. So because it's free, is generated by a ton of money. It is just sitting there and it's got heterogeneity like nobody's business. Like you could find a cohort of patients that came from India, a cohort of patients that came from North Carolina, and group of patients that came from Singapore and from different places in the US and different platforms. So because the algorithms at first that studied heterogeneity is actually, I would say, platform independent, platform agnostic, we don't use things that are done 20 years ago. They were done yesterday. And what we do is we look at each one of them individually and then we look for recurrent biological signals. So that's the idea behind looking for true signals, because people always say, you go fishing, you may be getting junk out. Right?Pek Lum: So let's say, for example, we go to, the engine points to a spot in the sea, in the ocean, and five people go, then you're always fishing out the same thing, the Blue Marlin, then you know that there is something there. So what we do is we take each data set, runs it through an engine and say these are the subtypes that I find. It does the same thing again in another data set and say these are the things that I find. And then it looks for recurrence signals, which is if you are a artifact that came from this one lab over here, or some kind of something that is unique to this other code over there, you can never find it to be recurrent. And that's a very weird, systematic bias, you know, so so because of that, we are able to then very quickly, I would say, get the wheat and throw away the chaff. Right. And basically by just looking by the engine, looking at looking for recurring signals. So public data sets is like a a treasure trove for Auransa because we can use it.Harry Glorikian: So you guys use your engine to I think you identified something unexpected, a correlation between plant-derived flavonoid compound and the heart. I think it was, you found that it helps mitigate toxic effects in a chemotherapy drug, you know. Can you say more about how the system figured that out, because that sounds not necessarily like a brand-new opportunity, but identifying something that works in a different way than what we thought originally.Pek Lum: Right, exactly. So in our digital library, let me explain a little bit about that. We have collected probably close to half a million gene expression profiles. So it's all RNA gene expression based, representing about 22,000 unique compounds. And these are things that we might generate ourselves or they are in the public domain. So any compound that has seen a live cell is fair game to our algorithms. So basically you put a compound, could be Merck's compound, could be a tool compound, could be a natural compound, could be a compound from somewhere. And it's put on a cell and gene expression was captured. And those are the profiles or the signatures that we gather. And then the idea is that, because remember, we have this part of the engine where we say we're going to take the biology and study it and then we're going to match it or we're going to look for compounds or targets. When you knock it down, who's gene expression actually goes the opposite way of the the disease. Now, this is a concept that is not new, right. In the sense that over 20 years ago, I think Rosetta probably was one of the first companies that say, look, if you have a compound that affects the living cell and it affects biology in a way that is the opposite of your disease, it's a good thing. Right thing. So that's the concept. But, you know, the idea then is to do this in such a way that you don't have to test thousands of compounds.Harry Glorikian: Right.Pek Lum: That is accurate enough for you to test a handful. And that's what we do. And by putting the heterogeneity concept together with this is something extremely novel and extremely important for the engine. And so with this kind of toxicity is actually an interesting story. We have a bunch of friends who are spun off a company from Stanford and they were building cardiomyocytes from IPS cells to print stem cells. And they wanted to do work with us, saying that why do we work together on a cool project? We were just starting out together and we thought about this project where it is a highly unmet medical need, even though chemotherapy works extremely well. Anthracyclines, it actually takes heart, takes a toll. There is toxicity and is it's a known fact. And there's only one drug in the market and a very old drug in the market today. And there is not much attention paid to this very critical aspect. So we thought we can marry the engine. At that time were starting up with oncology. We still we still are in oncology, and they were in cardiomyocytes. So we decided to tackle this extremely difficult biology where we say, what is a how does chemotherapy affect heart cells and what does the toxicity look like? So the engine took all kinds of data sets, heart failure data sets, its key stroke and cells that's been treated with anthracyclines. So a ton of data and look for homogeneity and signals of the of the toxicity.Pek Lum: So this is a little bit different from the disease biology, but it is studying toxicity. And we then ask the engine to find compounds that we have in our digital library, that says that what is the, I would say the biology of these compounds when they hit a living cell that goes the opposite way of the toxicity. And that's how we found, actually we gave the company probably about seven, I forget, maybe seven to 10 compounds to test. The one thing that's really great about our engine is that you don't have to test thousands of compounds and it's not a screen because you screened it in silico. And then it would choose a small number of compounds, usually not usually fewer than 30. And then we able to test and get at least a handful of those that are worth looking into and have what they call development legs. So this I would say this IPSC cardiomyocyte system is actually quite complex. You can imagine that to screen a drug that protects against, say, doxorubicin is going to be a pretty complicated screen that can probably very, very hard to do in a high throughput screen because you have to hit it with docs and then you have to hit it with the compounds you want to test and see whether it protects against a readout that is quite complex, like the beating heart.Pek Lum: And so we give them about, I think, seven to 10 and actually four of them came out to be positive. Pretty amazing. Out of the four, one of them, the engine, noticed that it belonged to a family of other compounds that looked like it. So so that was really another hint for the the developers to say, oh, the developers I mean, drug developers to say, this is interesting. So we tested then a whole bunch of compounds that look like it. And then one of them became the lead compound that we actually licensed to a a pharma company in China to develop it for the Chinese market first. We still have the worldwide rights to that. So that's how we tackled toxicity. And I think you might have read about another project with Genentech, actually, Roche. We have a poster together. And that is also the same idea, that if you can do that for cardio tox, perhaps you can do it for other kinds of toxicity. And one of them is actually GI tox, which is a very common toxicity. Some of them are rate limiting, you might have to pull a drug from clinical trials because there's too much GI tox or it could be rate limiting to that. So we are tackling the idea that you can use to use machine, our engine, to create drugs for an adjuvant for a disease, a life-saving drug that otherwise could not be used properly, for example. So that's kind of one way that we have to use the engine just starting from this little project that we did with the spin out, basically.Pek Lum: So basically, you're sort of, the engine is going in two directions. One is to identify new things, but one is to, I dare say, repurpose something for something that wasn't expected or wasn't known.Pek Lum: That is right. Because it doesn't really know. It doesn't read papers and know is it's a repurposed drug or something. You just put in it basically, you know, the gene expression profiles or patterns of all kinds of drugs. And then from there, as a company, we decided on two things. We want to be practical, right. And then we want to find novel things, things that, and it doesn't matter where that comes from, as long as the drug could be used to do something novel or something that nobody has ever thought of or it could help save lives, we go for it. However, you know, we could find something. We were lucky to find something like this flavanol that has never been in humans before. So it still qualifies as an NCE, actually, and because it's just a natural compound. So so in that sense, I would say maybe is not repurposing, but it's repositioning. I don't know from it being a natural compound to being something maybe useful for heart protection. Pek Lum: Now for our liver cancer compound, it is a total, totally brand-new compound. The initial compound that the engine found is actually a very, very old drug. But it was just a completely different thing and definitely not suitable for cancer patients the way it is delivered.Harry Glorikian: This is the AU 409?Pek Lum: Correct? Entirely new entity. New composition of matter. But the engine gave us the first lead, the first hit, and told us that we analyzed over a thousand liver tumors and probably over a thousand normal controls, found actually three subtypes, two of them the main subtypes and very interesting biology. And the engine predicted this compound that it thinks will work on both big subtypes. We thought this is interesting. But we look at the compound. You know, it's been in humans. It's been used. It's an old drug. But it could never be given to a cancer patient. And so and so our team, our preclinical development team basically took that and say, can we actually make this into a cancer drug? So we evaluated that and thought, yes, we can. So we can basically, we analogged it. It becomes a new chemical. Now it's water-soluble. We want to be given as a pill once a day for liver cancer patients. So so that's how we kind of, as each of the drug programs move forward, we make a decision, the humans make a decision, after the leadds us to that and say can we make it into a drug that can be given to patients?Harry Glorikian: So where does that program stand now? I mean, where is it in its process or its in its lifecycle?Pek Lum: Yeah, it's actually we are GMP manufacturing right now. It's already gone through a pre-IND meeting, so it's very exciting for us and it's got a superior toxicity profile. We think it's very well tolerated, let's put it that way. It could be very well tolerated. And it's it's at the the stage where we are in the GMP manufacturing phase, thinking about how to make that product and so on.Harry Glorikian: So that that begs the question of do you see the company as a standalone pharma company? Do you see it as a drug discovery partner that that works with somebody else? I'm you know, it's interesting because I've talked to other groups and they start out one place and then they they migrate someplace else. Right. Because they want the bigger opportunities. And so I'm wondering where you guys are.Pek Lum: Yeah, we've always wanted to be, I say we describe ourselves as a technology company, deep tech company with the killer app. And the killer app is drug discovery and development especially. And we've always thought about our company as a platform company, and we were never shy about partnering with others from the get go. So with our O18 our team, which is a cardioprotection drug, we out-licensed that really early, and it's found a home and now is being developed. And then we moved on to our liver cancer product, which we brought a little bit further. Now it's in GMP manufacturing. And we're actually looking for partners for that. And we have a prostate cancer compound in lead optimization that will probably pan out as well. So we see ourselves as being partners. Either we co-develop, or we out-license it and maybe one day, hopefully not too far in the future, we might bring one or two of our favorite ones into later stage clinical trials. But we are not shy about partnering at different stages. So we are going to be opportunistic because we really have a lot to offer. And also one thing that we've been talking to other partners, entrepreneurs, is that using our engine to form actually other companies, to really make sure the engine gets used and properly leveraged for other things that Auransa may not do because we just can't do everything.Harry Glorikian: No, that's impossible. And the conversation I have with entrepreneurs all the time, yes, I know you can do it all, but can we just pick one thing and get it across the finish line? And it also dramatically changes valuation, being able to get what I have people that tell me, you know, one of these days I have to see one of these A.I. systems get something out. And I always tell them, like, if you wait that long, you'll be too late.Harry Glorikian: So here's an interesting question, though. And jumping back to almost the beginning. The company was named Capella. And you change the name to Auransa.Pek Lum: That's right.Harry Glorikian: And so what's the story behind that? Gosh, you know.Harry Glorikian: When somebody woke up one morning and said, I don't like that name.Pek Lum: It's actually pretty funny. So we so we like to go to the Palo Alto foothills and watch the stars with the kids. And then one day we saw Capella. From afar, you look at it, it's actually one star. You look at closer, it's two stars. Then closer, it's four stars. It's pretty remarkable. And I thought, OK, we should name it Capella Biosciences. Thinking we are the only ones on the planet that are named. So we got Capella Biosciences and then probably, we never actually had a website yet. So we were just kind of chugging along early days and then we realized that there was a Capella Bioscience across the pond in the U.K. We said what? How can somebody be named Capella Bioscience without an S? So I actually called up the company and said, “Hey, we are like your twin across the pond. We're doing something a little different, actually completely different. But you are Capella Bioscience and I am Capella Biosciences. What should we do?” And they're like, “Well, we like the name.” We're like, “Well, we like it too.” So we kind of waited for a while. And but in the meantime, I started to think about a new name in case we need to change it. And then we realized that one day we were trying to buy a table, one of those cool tables that you can use as a ping pong table that also doubles as a as a conference room table. So we called up this New York City company and they said, oh, yeah, when are you going to launch the rockets into space. We're like what? So apparently, there's a Capella Space.Harry Glorikian: Yeah, OK.Pek Lum: Well, that's the last straw, because we get people tweeting about using our Twitter handle for something else. And so it's just a mess. So we've been thinking about this other name, and I thought this is a good name. Au means gold. And ansa is actually Latin for opportunity, which we found out. So we're like oh, golden opportunity. Golden answer. That kind of fits into the platform idea. Auransa sounds feminine. I like it. I'm female CEO. And I can get auransa.com. Nobody has Auransa. So that is how Auransa came to be.Harry Glorikian: Well, you got to love the…I love the Latin dictionary when I'm going through there and when I'm looking for names for a company, I've done that a number of times, so. Well, I can only wish you incredible success in your journey and what you're doing, it's such a fascinating area. I mean, I always have this dream that one day everybody is going to share all this data and we're going to move even faster. But I'm not holding my breath on that one when it comes to private companies. But it was great to talk to you. And I hope that we can continue the conversation in the future and watch the watch the progression of the company.Pek Lum: Thank you, Harry. This has been really fun.Harry Glorikian: That’s it for this week’s show. We’ve made more than 50 episodes of MoneyBall Medicine, and you can find all of them at glorikian.com under the tab “Podcast.” You can follow me on Twitter at hglorikian. If you like the show, please do us a favor and leave a rating and review at Apple Podcasts. Thanks, and we’ll be back soon with our next interview.  

Marian Guthrie is both a mama to her three children,  and also a MAMA - a Mature Aged Ministry Apprentice.  In this conversation with host Tori Walker, Marian talks about what led her to being a MAMA and studying at Moore Theological College, the effects of feminism in her life, and ministry through COVID times.   EPISODE NOTES: Marian Guthrie is both a mama to her three children,  and also a MAMA - a Mature Aged Ministry Apprentice.  In this conversation with host Tori Walker, Marian talks about what led her to being a MAMA and studying at Moore Theological College, the effects of feminism in her life, and ministry through COVID times.   *** I live in Sydney with my husband, Stephen, and have three adult children. Christ Church, Gladesville, has been my church for over thirty years and is where I'm currently working part-time as a Ministry Apprentice. I'm also a part-time student at Moore Theological College, enrolled in the Advanced Diploma of Bible, Mission and Ministry. Early morning walks are one of my favourite pastimes as well as pottering in the garden or reading a good novel. Since COVID I've also started jigsaw-puzzling and binge-watching. I enjoy catching up with women one to one, usually walking and talking, but I also enjoy gathering members of my church family to join us for breakfast before watching livestream church on Sunday mornings.   LINKS MENTIONED IN THIS EPISODE: Katoomba Christian Conventions Moore Theological College  Marian's TCGA articles (book recommendation) Deeper Still by Linda Allcock      SHOW SNIPPETS: “Maybe I'm just one of those stepping stones” (along the way to people coming to faith in Christ)   “I look back and I think, yep, God was at work even in those times before I became a Christian.”   “I think God's been doing a lot of work on me this year, in terms of the ways He has been teaching me.”   “I've had to learn (and still in the process of it) humility and being teachable.”   This episode also introduces a book club idea that we would love to share with you. Stay posted for more!    Can't see clickable links? Copy and paste this into your browser: tlpcwcw.podbean.com . . The Lydia Project: Conversations with Christian Women is a podcast co-hosted by Tori Walker and Taryn Hayes. It features informal chats with Christian women around faith, life, ministry and the ways in which God is shaping their thinking and their lives. The views of TLP guests are their own and do not necessarily reflect the views of the hosts.  

The cover for issue 28 of Oncotarget features Figure 5, "TMEM165 expression levels alters N-linked glycosylation," by Murali, et al., and reported that the TMEM165 protein was not detected in non-malignant matched breast tissues and was detected in invasive ductal breast carcinoma tissues by mass spectrometry. The hypothesis is that the TMEM165 protein confers a growth advantage to breast cancer. The authors created a CRISPR/Cas9 knockout of TMEM165 in the human invasive breast cancer cell line MDAMB231. Furthermore, they find that TMEM165 expression alters the glycosylation of breast cancer cells and these changes promote the invasion and growth of breast cancer by altering the expression levels of key glycoproteins involved in the regulation of the epithelial to mesenchymal transition such as E-cadherin. These studies illustrate new potential functions for this Golgi membrane protein in the control of breast cancer growth and invasion. Dr. Karen L. Abbott from The University of Oklahoma Health Sciences Center, Department of Biochemistry and Molecular Biology said "Breast cancer is the most commonly diagnosed cancer in women." The TMEM165 protein was identified by mass spectrometry in invasive breast carcinoma tissue with no detection in patient-matched adjacent normal breast tissues. The authors analyzed TCGA breast cancer cases to examine TMEM165 expression levels in all molecular types of human breast cancer using UALCAN. They found that TMEM165 is amplified across all types of breast cancer compared to normal breast tissue with IDC cases having the highest levels of TMEM165 expression. In the present study, the authors report that TMEM165 is upregulated in human breast cancer cell lines and patient tumor tissues and increased expression of TMEM165 correlates with poor prognosis in breast cancer patients. Collectively, the data demonstrate that overexpression of TMEM165 promotes EMT in breast cancer suggesting a novel role for TMEM165 as a driver of tumor invasion making it a prognostic marker and potential therapeutic target for breast cancer. The Abbott Research Team concluded in their Oncotarget Research Paper, "we have expanded on our initial 2012 glycoproteomic study that was the first to identify TMEM165 protein as a potential biomarker for breast carcinoma. In this study, we have provided initial mechanistic studies that indicate that TMEM165 expression drives the growth and invasion of breast cancer. TMEM165 expression levels could be a potential prognostic marker for predicting DCIS cases that may progress to invasive disease. Larger prospective cohorts will need to be analyzed to determine the link between TMEM165 levels and the progression to IDC. We find that IDC patients with higher TMEM165 expression levels have reduced overall survival making this protein a target for the development of new therapeutic strategies to limit the progression of breast cancer." Sign up for free Altmetric alerts about this article DOI - https://doi.org/10.18632/oncotarget.27668 Full text - https://www.oncotarget.com/article/27668/text/ Correspondence to - Karen L. Abbott - Karen-Abbott@ouhsc.edu Keywords - TMEM165, migration, invasion, breast cancer, glycosylation About Oncotarget Oncotarget is a weekly, peer-reviewed, open access biomedical journal covering research on all aspects of oncology. To learn more about Oncotarget, please visit https://www.oncotarget.com or connect with: SoundCloud - https://soundcloud.com/oncotarget Facebook - https://www.facebook.com/Oncotarget/ Twitter - https://twitter.com/oncotarget LinkedIn - https://www.linkedin.com/company/oncotarget Pinterest - https://www.pinterest.com/oncotarget/ Reddit - https://www.reddit.com/user/Oncotarget/ Oncotarget is published by Impact Journals, LLC please visit http://www.ImpactJournals.com or connect with @ImpactJrnls Media Contact MEDIA@IMPACTJOURNALS.COM 18009220957x105

This episode's guest, Andrew Janowczyk, is a computer scientist who has been active in the field of digital pathology since 2008. Before turning to the field of digital pathology he worked across the globe and across industries. He was a salmon fisherman in Alaska. In Austria at the United Nations (UN) International Atomic Energy (IAE) Agency, he significantly contributed to the work that won the UN IAEA a Nobel Peace Prize.He taught English in China. He helped build an oil facility in the Nigerian jungle and lived in Nigeria for a while. Then he lived in Germany...A close family member diagnosed with cancer made him aware of the field of pathology and he decided to switch gears and put his energy and brainpower into advancing this discipline. He moved to Mumbai, India to get his Ph.D. and started his digital pathology research. Currently, he is working at the Case Western Reserve University (OH, USA) and Lausanne University (Switzerland).Fast forward to 2018, after 10 years in the field and after overcoming many challenges, he encountered another one: the suboptimal quality of the whole slides from the TCGA data set. To solve this he writes software that excludes all the non-usable regions of the slides and makes it open-source. Why? Why not commercialize such a useful tool?Andrew's answer: "I wanted to release it open source just to fundamentally change the world. I wanted to change the way that we enacted digital pathology as a science, and one of the problems with digital pathology science versus other sciences is that we don't take measurements. And as soon as we start taking measurements, we have the ability to do better."In addition to his main work, Andrew also runs a blog with resources for computer scientists working in the field of digital pathology. Other resources from this episode include:Publication: HistoQC: An Open-Source Quality Control Tool for Digital Pathology SlidesPublication: Assessment of computerized quantitative quality control tool for kidney whole slide image biopsiesHistoQC download pageAn article on Andrews blog about how to download TCGA digital pathology images

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.11.20.391045v1?rss=1 Authors: Planell, N., Lagani, V., Sebastian-Leon, P., van der Kloet, F., Ewing, E., Karathanasis, N., Urdangarin, A., Arozarena, I., Jagodic, M., Tsamardinos, I., Tarazona, S., Conesa, A., tegner, j., Gomez-Cabrero, D. Abstract: Technologies for profiling samples using different omics platforms have been at the forefront since the human genome project. Large-scale multi-omics data hold the promise of deciphering different regulatory layers. Yet, while there is a myriad of bioinformatics tools, each multi-omics analysis appears to start from scratch with an arbitrary decision over which tools to use and how to combine them. It is therefore an unmet need to conceptualize how to integrate such data and to implement and validate pipelines in different cases. We have designed a conceptual framework (STATegra), aiming it to be as generic as possible for multi-omics analysis, combining machine learning component analysis, non-parametric data combination and a multi-omics exploratory analysis in a step-wise manner. While in several studies we have previously combined those integrative tools, here we provide a systematic description of the STATegra framework and its validation using two TCGA case studies. For both, the Glioblastoma and the Skin Cutaneous Melanoma cases, we demonstrate an enhanced capacity to identify features in comparison to single-omics analysis. Such an integrative multi-omics analysis framework for the identification of features and components facilitates the discovery of new biology. Finally, we provide several options for applying the STATegra framework when parametric assumptions are fulfilled, and for the case when not all the samples are profiled for all omics. The STATegra framework is built using several tools, which are being integrated step-by-step as OpenSource in the STATegRa Bioconductor package https://bioconductor.org/packages/release/bioc/html/STATegra.html. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.11.17.387860v1?rss=1 Authors: Gao, B., Luo, Y., Ma, J., Wang, S. Abstract: Tumor stratification, which aims at clustering tumors into biologically meaningful subtypes, is the key step towards personalized treatment. Large-scale profiled cancer genomics data enables us to develop computational methods for tumor stratification. However, most of the existing approaches only considered tumors from an individual cancer type during clustering, leading to the overlook of common patterns across cancer types and the vulnerability to the noise within that cancer type. To address these challenges, we proposed cancerAlign to map tumors of the target cancer type into latent spaces of other source cancer types. These tumors were then clustered in each latent space rather than the original space in order to exploit shared patterns across cancer types. Due to the lack of aligned tumor samples across cancer types, cancerAlign used adversarial learning to learn the mapping at the population level. It then used consensus clustering to integrate cluster labels from different source cancer types. We evaluated cancerAlign on 7,134 tumors spanning 24 cancer types from TCGA and observed substantial improvement on tumor stratification and cancer gene prioritization. We further revealed the transferability across cancer types, which reflected the similarity among them based on the somatic mutation profile. cancerAlign is an unsupervised approach that provides deeper insights into the heterogeneous and rapidly accumulating somatic mutation profile and can be also applied to other genome-scale molecular information. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.11.13.381442v1?rss=1 Authors: Nagy, A., Munkacsy, G., Gyorffy, B. Abstract: Cancer hallmark genes are responsible for the most essential phenotypic characteristics of malignant transformation and progression. In this study, our aim was to estimate the prognostic effect of the established cancer hallmark genes in multiple distinct cancer types. RNA-seq HTSeq counts and survival data from 26 different tumor types were acquired from the TCGA repository. DESeq was used for normalization. Correlations between gene expression and survival were computed using the Cox proportional hazards regression and by plotting Kaplan-Meier survival plots. The false discovery rate was calculated to correct for multiple hypothesis testing. Signatures based on genes involved in genome instability and invasion reached significance in most individual cancer types. Thyroid and glioblastoma were independent of hallmark genes (61 and 54 genes significant, respectively), while renal clear cell cancer and low grade gliomas harbored the most prognostic changes (403 and 419 genes significant, respectively). The eight genes with the highest significance included BRCA1 (genome instability, HR=4.26, p

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.11.10.376228v1?rss=1 Authors: Bartha, A., Gyorffy, B. Abstract: Genes showing higher expression in either tumor or metastatic tissues can help in better understanding tumor formation, and can serve as biomarkers of progression or as therapy targets with minimal off-target effects. Our goal was to establish an integrated database using available transcriptome-level datasets and to create a web-platform enabling mining of this database by comparing normal, tumor and metastatic data across all genes in real time. We utilized data generated by either gene arrays or RNA-seq. Gene array data were manually selected from NCBI-GEO. RNA sequencing data was downloaded from the TCGA, TARGET, and GTEx repositories. TCGA and TARGET contain predominantly tumor and metastatic samples from adult and pediatric patients, while GTEx samples are from healthy tissues. Statistical significance was computed using Mann-Whitney or Kruskall-Wallis tests. The entire database contains 56,938 samples including 33,520 samples from 3,180 gene chip-based studies (453 metastatic, 29,376 tumorous and 3,691 normal samples), 11,010 samples from TCGA (394 metastatic, 9,886 tumorous and 730 normal), 1,193 samples from TARGET (1 metastatic, 1,180 tumor, 12 normal) and 11,215 normal samples from GTEx. The most consistently up-regulated genes across multiple tumor types were TOP2A (mean FC=7.8), SPP1 (FC=7.0) and CENPA (FC=6.03) and the most consistently down-regulated gene was ADH1B (mean FC=0.15). Validation of differential expression using equally sized training and test sets confirmed reliability of the database in breast, colon, and lung cancer (p

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.11.09.375204v1?rss=1 Authors: Kosaloglu-Yalcin, Z., Lee, J., Nielsen, M., Greenbaum, J., Schoenberger, S. P., Miller, A., Kim, Y. J., Sette, A., Peters, B. Abstract: MHC class I antigen processing consists of multiple steps that result in the presentation of MHC bound peptides that can be recognized as T cell epitopes. Many of the pathway steps can be predicted using computational methods, but one is often neglected: mRNA expression of the epitope source proteins. In this study, we improve epitope prediction by taking into account both peptide-MHC binding affinities and expression levels of the peptide's source protein. Specifically, we utilized biophysical principles and existing MHC binding prediction tools in concert with RNA expression to derive a function that estimates the likelihood of a peptide being presented on a given MHC class I molecule. Our combined model of Antigen eXpression based Epitope Likelihood-Function (AXEL-F) outperformed predictions based only on binding or based only on antigen expression for discriminating eluted ligands from random background peptides as well as in predicting neoantigens that are recognized by T cells. We also showed that in cases where cancer patient-specific RNA-Seq data is not available, cancer-type matched expression data from TCGA can be used to accurately estimate patient-specific gene expression. Using AXEL-F together with TGCA expression data we were able to more accurately predict neoantigens that are recognized by T cells. The method is available in the IEDB Analysis Resource and free to use for the academic community. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.10.19.345389v1?rss=1 Authors: BRIERE, G., Darbo, E., Thebault, P., Uricaru, R. Abstract: Background Facing the diversity of omic data and the difficulty of selecting one result over all those produced by several methods, consensus strategies have the potential to reconcile multiple inputs and to produce robust results. Results Here, we introduce ClustOmics, a generic consensus clustering tool that we use in the context of cancer subtyping. ClustOmics relies on a non-relational graph database, which allows for the simultaneous integration of both multiple omic data and results from various clustering methods. This new tool conciliates input clusterings, regardless of their origin, their number, their size or their shape. ClustOmics implements an intuitive and flexible strategy, based upon the idea of evidence accumulation clustering. It computes co-occurrences of pairs of samples in input clusters and uses this score as a similarity measure to reorganise data into consensus clusters. Conclusion We applied ClustOmics to multi-omic disease subtyping on real TCGA cancer data from ten different cancer types. We showed that ClustOmics is robust to heterogeneous qualities of input partitions, smoothing and reconciling preliminary predictions into high quality consensus clusters, both from a computational and a biological point of view. In this regard, ClustOmics is not meant to compete with other integrative tools, but rather to make profit from their various predictions when no gold-standard metric is available to assess their significance. Availability ClustOmics' source code, released under MIT licence, as well as the results obtained on TCGA cancer data are available on Github: https://github.com/galadrielbriere/ClustOmics. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.10.04.325670v1?rss=1 Authors: Li, R., Qu, H., Wang, S., Wang, X., Cui, Y., Yu, L., Chater, J. M., Zhou, R., Jia, Q., Traband, R., Yuan, D., Zhu, J., Zhong, W.-D., Jia, Z. Abstract: MicroRNAs (miRNAs), which play critical roles in gene regulatory networks, have emerged as promising biomarkers for a variety of human diseases, including cancer. In particular, circulating miRNAs that are secreted into circulation exist in remarkably stable forms, and have enormous potential to be leveraged as non-invasive diagnostic biomarkers for early cancer detection. The vast amount of miRNA expression data from tens of thousands of samples in various types of cancers generated by The Cancer Genome Atlas (TCGA) and circulating miRNA data produced by many large-scale circulating miRNA profiling studies provide extraordinary opportunities for the discovery and validation of miRNA signatures in cancer. Novel and user-friendly tools are desperately needed to facilitate the data mining of such valuable cancer miRNome datasets. To fill this void, we developed CancerMIRNome, a web server for interactive analysis and visualization of cancer miRNome data based on TCGA and public circulating miRNome datasets. A series of cutting-edge bioinformatics tools and functions have been packaged in CancerMIRNome, allowing for a pan-cancer analysis of a miRNA of interest across multiple cancer types and a comprehensive analysis of cancer miRNome at the dataset level. The CancerMIRNome web server is freely available at http://bioinfo.jialab-ucr.org/CancerMIRNome. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.10.01.322164v1?rss=1 Authors: Karasikov, M., Mustafa, H., Danciu, D., Zimmermann, M., Barber, C., Ratsch, G., Kahles, A. Abstract: The amount of biological sequencing data available in public repositories is growing exponentially, forming an invaluable biomedical research resource. Yet, making all this sequencing data searchable and easily accessible to life science and data science researchers is an unsolved problem. We present MetaGraph, a versatile framework for the scalable analysis of extensive sequence repositories. MetaGraph efficiently indexes vast collections of sequences to enable fast search and comprehensive analysis. A wide range of underlying data structures offer different practically relevant trade-offs between the space taken by an index and its query performance. Achieving compression ratios of up to 1,000-fold over the already compressed raw input data, MetaGraph indexes can represent the content of large sequencing archives in the working memory of a single compute server. We demonstrate our framework's scalability by indexing over 1.4 million whole genome sequencing (WGS) records from NCBI's Sequence Read Archive, representing a total input of more than three petabases. MetaGraph provides a flexible methodological framework allowing for index construction to be scaled from consumer laptops to distribution onto a cloud compute cluster for processing terabases to petabases of input data. Notably, processing of data sets ranging from 1 TB of raw WGS reads to 20 TB of human RNA-sequencing data results in indexes whose memory footprints are small enough to host on standard desktop workstations. Besides demonstrating the utility of MetaGraph indexes on key applications, such as experiment discovery, sequence alignment, error correction, and differential assembly, we make a wide range of indexes available as a community resource, including indexes of over 450,000 microbial WGS records, more than 110,000 fungi WGS records, and more than 40,000 whole metagenome sequencing records. A subset of these indexes is made available online for interactive queries. All indexes will be available for download and in the cloud. In total, indexes comprising more than 1 million sequencing records are available for download. As an example of our indexes' integrative analysis capabilities, we introduce the concept of differential assembly, which allows for the extraction of sequences present in a foreground set of samples but absent in a given background set. We apply this technique to differentially assemble contigs to identify pathogenic agents transfected via human kidney transplants. In a second example, we indexed more than 20,000 human RNA-Seq records from the TCGA and GTEx cohorts and use them to extract transcriptome features that are hard to characterize using a classical linear reference. We discovered over 200 trans-splicing events in GTEx and found broad evidence for tissue-specific non-A-to-I RNA-editing in GTEx and TCGA. Copy rights belong to original authors. Visit the link for more info

Classification of endometrial tumors based strictly on morphology can be challenging due to overlapping histologic features and often requires the assistance of ancillary testing. Recently proposed algorithms, such as the ProMisE Proactive Molecular Risk Classifier for Endometrial Cancer, coupled with TCGA data, aim to further subclassify endometrial carcinomas into prognostically significant groups based on molecular genetics, explains Dr. Laura Tafe in this CAPcast interview. Dr. Tafe will be joining Dr. Jessica Dillon to teach a course on this topic at CAP20 this year, which will be held virtually on Oct. 10-14. CAP20 will be held virtually this year from Oct. 10-14 (www.capannualmeeting.org/). Follow #cap20virtual on social for the latest on the event.

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.09.07.286583v1?rss=1 Authors: Mostavi, M., Chiu, Y.-C., Chen, Y., Huang, Y. Abstract: We consider cancer classification based on one single gene expression profile. We proposed CancerSiamese, a new one-shot learning model, to predict the cancer type of a query primary or metastatic tumor sample based on a support set that contains only one known sample for each cancer type. CancerSiamese receives pairs of gene expression profiles and learns a representation of similar or dissimilar cancer types through two parallel Convolutional Neural Networks joined by a similarity function. We trained CancerSiamese for both primary and metastatic cancer type predictions using samples from TCGA and MET500. Test results for different N-way predictions yielded an average accuracy improvement of 8% and 4% over the benchmark 1-Nearest Neighbor (1-NN) classifier for primary and metastatic tumors, respectively. Moreover, we applied the guided gradient saliency map and feature selection to CancerSiamese to identify and analyze the marker-gene candidates for primary and metastatic cancers. Our work demonstrated, for the first time, the feasibility of applying one-shot learning for expression-based cancer type prediction when gene expression data of cancer types are limited and could inspire new and ingenious applications of one-shot and few-shot learning solutions for improving cancer diagnosis, treatment planning, and our understanding of cancer. Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.08.10.245142v1?rss=1 Authors: Mandera, D., Ritz, A. Abstract: Predicting the response to a particular drug for specific cancer, despite known genetic mutations, still remains a huge challenge in modern oncology and precision medicine. Today, prescribing a drug for a cancer patient is based on a doctor's analysis of various articles and previous clinical trials; it is an extremely time-consuming process. We developed a machine learning classifier to automatically predict a drug given a carcinogenic gene mutation profile. Using the Breast Invasive Carcinoma Dataset from The Cancer Genome Atlas (TCGA), the method first selects features from mutated genes and then applies K-Fold, Decision Tree, Random Forest and Ensemble Learning classifiers to predict best drugs. Ensemble Learning yielded prediction accuracy of 66% on the test set in predicting the correct drug. To validate that the model is general-purpose, Lung Adenocarcinoma (LUAD) data and Colorectal Adenocarcinoma (COADREAD) data from TCGA was trained and tested, yielding prediction accuracies 50% and 66% respectively. The resulting accuracy indicates a direct correlation between prediction accuracy and cancer data size. More importantly, the results of LUAD and COADREAD show that the implemented model is general purpose as it is able to achieve similar results across multiple cancer types. We further verified the validity of the model by implementing it on patients with unclear recovery status from the COADREAD dataset. In every case, the model predicted a drug that was administered to each patient. This method will offer oncologists significant time-saving compared to their current approach of extensive background research, and offers personalized patient care for cancer patients. Copy rights belong to original authors. Visit the link for more info

Oncotarget Volume 11, Issue 27 published "Clonality and antigen-specific responses shape the prognostic effects of tumor-infiltrating T cells in ovarian cancer" by Tsuji et al. which reported that to delineate the complexity of anti-tumor T-cell responses, the author's utilized a computational method for de novo assembly of sequences from CDR3 regions of 369 high-grade serous ovarian cancers from TCGA, and then applied deep TCR-sequencing for analyses of paired tumor and peripheral blood specimens from an independent cohort of 99 ovarian cancer patients. In the validation cohort, the authors' discovered that patients with low T-cell infiltration but low diversity or focused repertoires had clinical outcomes almost indistinguishable from highly-infiltrated tumors. They also found that the degree of divergence of the peripheral repertoire from the TIL repertoire, and the presence of detectable spontaneous anti-tumor immune responses are important determinants of clinical outcome. Also that the prognostic significance of TILs in ovarian cancer is dictated by T-cell clonality, degree of overlap with peripheral repertoire, and the presence of detectable spontaneous anti-tumor immune response in the patients. These immunological phenotypes defined by the TCR repertoire may provide useful insights for identifying “TIL-low” ovarian cancer patients that may respond to immunotherapy. Dr. Kunle Odunsi from The Center for Immunotherapy as well as The Department of Gynecologic Oncology at Roswell Park Comprehensive Cancer Center said, "The presence of tumor-infiltrating lymphocytes (TILs) is a key determinant of clinical outcome in a wide range of solid tumors including ovarian cancer." High-throughput next-generation sequencing has made it possible to read the entire CDR3 to uniquely identify specific T cell clones and to estimate the absolute frequency of T cell clones in tumor tissue from the copy number of TCR sequences. The importance of TCR repertoire in shaping anti-tumor immunity in ovarian cancer was recently demonstrated using unbiased functional analysis of TCR repertoires from TILs derived from two patients. Tumor reactivity was revealed in 0–5% of tested TCRs indicating that the vast majority of T cells infiltrating ovarian tumors were irrelevant for tumor recognition. To determine how the TCR repertoire of TILs shapes the prognosis of ovarian cancer patients, the authors' utilized a new computational method for de novo assembly of sequences from CDR3 regions using paired-end RNA-seq data from the Cancer Genome Atlas study of high-grade serous ovarian cancers. The author's examined TCR repertoire in the context of the degree of tumor infiltration by T cells, spontaneous immune responses against bona fide TAAs, and clinical outcome. The Odunsi Research Team concluded in their Oncotarget Research Paper that despite these limitations, this study highlights the extraordinary diversity of the T-cell repertoire in ovarian cancer patients, and demonstrates that pre-existing immunity against cancer antigen is a critical prerequisite to correctly understand the prognostic significance of the T-cell repertoire in the tumor and peripheral blood of patients with ovarian cancer. They have distilled TCR repertoire information into candidate biomarkers that may critically influence the prognosis of ovarian cancer patients. Conceptually, ovarian cancers may not fit into the classic paradigm of ?cold' and ?hot' based on the number of T cells they contain, but also by the TCR repertoire information, which serves as a surrogate for tumor recognition. The latest technologies put these prognostic features in clinical reach not only for predicting prognosis but potentially for determining the best immunotherapeutic strategy for each patient. Full text - https://www.oncotarget.com/article/27666/text/ Correspondence to - Kunle Odunsi - kunle.odunsi@roswellpark.org. Keywords - T-cell repertoire, ovarian cancer, tumor immunity

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.06.01.126375v1?rss=1 Authors: Nalawade, S., Yu, F. F., Bangalore Yogananda, C. G., Murugesan, G. K., Shah, B. R., Pinho, M. C., Wagner, B. C., Mickey, B., Patel, T. R., Fei, B., Madhuranthakam, A. J., Maldjian, J. A. Abstract: Deep learning has shown promise for predicting glioma molecular profiles using MR images. Before clinical implementation, ensuring robustness to real-world problems, such as patient motion, is crucial. We sought to evaluate the effects of motion artifact on glioma marker classifier performance and develop a deep learning motion correction network to restore classification accuracies. T2w images and molecular information were retrieved from the TCIA and TCGA databases. Three-fold cross-validation was used to train and test the motion correction network on artifact-corrupted images. We then compared the performance of three glioma marker classifiers (IDH mutation, 1p/19q codeletion, and MGMT methylation) using motion-corrupted and motion-corrected images. Glioma marker classifier performance decreased markedly with increasing motion corruption. Applying motion correction effectively restored classification accuracy for even the most motion-corrupted images. Robust motion correction can enable high accuracy in deep learning MRI-based molecular marker classification rivaling tissue-based characterization. Copy rights belong to original authors. Visit the link for more info

In this episode, Bob & Kevin talk all things CRISPR - basically the REGEX of DNA editing... and guess what? Apparently you can do it at home... soak in the transcript below (from our friends at https://otter.ai/) and feel free to ping us on social media with your thoughts on this episode or any of our others - Follow us on twitter at https://twitter.com/bobandkevinshow.  Kevin 0:00 So Bob, is this a safe place for discourse? Bob 0:06 I hope so Kevin 0:07 good. Because Bob 0:09 I've been pretending it is for quite some Kevin 0:11 as we have the normal show disclaimer, but rather than disclaim, I mean, we'll do the normal disclaimer. But rather than just do the normal disclaimer, I'm going to go ahead and say it right now. I'm probably going to piss off the science folks. And I'm probably going to piss off the religious folks. Bob 0:27 Why are you going to piss off the science folks? That doesn't seem like something you would do? Kevin 0:31 2020 outrage culture was born. Not that long ago and people disliked we pissed. Bob 0:38 So what do scientists play that game? Because they're pretty factual. Kevin 0:42 There's there's different kinds of scientists, right? There's, there's Yes, there's just I mean, they're humans, right? So it depends. Bob 0:51 I guess I just don't see Neil deGrasse Tyson getting outraged. Kevin 0:54 So something number scientists so something happened in 1984 Bob, do you know what happened? Bob 1:02 George Orwell wrote a book. Kevin 1:05 Now he actually wrote that in 1948 but you know about the year 1984 I probably got the year you write it wrong, but as a long time ago anyway, know what you meant to say, but you didn't say is gozer that goes arion asks the Ghostbusters if they're gods. And Bob, do you remember what they say? Bob 1:26 Oh, how did I How did I misquote that? Right? Unknown Speaker 1:31 You? Kevin 1:33 So do you remember what the Ghostbusters replied? Who you gonna call? Oh, in fact, they said no. I said, you know when gozer says are you a god? And they're like, no. And then she like tries to destroy them. So how does this okay, I am I'm a bastion of useless pop culture references. Okay, so Bob, would you like to play God unearth, Bob 2:01 Kevin. I think I have since I have children, doesn't that qualify? Kevin 2:06 Yeah, maybe I guess he kind of brought life into this world and the common thing is I brought you in this world. take you out, take you out. At least that's what I was told when I was a child. Okay, so playing God, Bob 2:21 I don't really know if I have God like playing God like tendencies. Like I'm a floater. I just kind of go where the wind takes me for the most part. Kevin 2:29 All right, so Unknown Speaker 2:32 would you Kevin 2:33 consider saving or manipuri manipulating populations of species of animals on earth a bit of kind of messing with how things work kind of kind of godlike a little bit. Bob 2:51 I know where this is going. I know where you're taking me. Oh boy. Um all right no but we do well let's naturally or not Kevin 3:06 we let's develop it a little further. So there is like an article and I think his name was Diego or something the turtle maybe maybe you know his actual name. He He's been tasked he's a giant turtle has been tasked with making babies making more giant turtles. Bob 3:22 I think he succeeded and they set him free. Yeah, he made like 2000 Kevin 3:25 babies so there is at once you know a very small number. Now there's 2000 thanks to his sexual prowess as a giant turtle right? Did they I didn't read the article. Did they do it naturally or did they do it extraction and implantation? I'm pretty sure he did it the old fashioned way, Bob. Good. No, yeah. So if you listen to Joe Rogan, which I know we both do, sometimes I'll mention, wolves are being reintroduced to curb like elk populations, or do population and things like that. And other things that we kind of play God as humans is genetically modified organisms. And you and I have talked a little like three sentences, maybe on the pod and maybe a little more off about where we are with genetically modified organisms. And for the lay user, a GMO is basically vegetables that produce bigger fruit. It's going to be chickens. Well, actually, before we get the chickens, Bob 4:32 it's gonna be basically but since the dawn of, but hang on since the dawn of time we've been genetically modifying just by breeding. Kevin 4:41 Yeah, and like, just like the turtle the good old I was trying to come up with what what do we call that as humans because we took corn because like the original corn was like really nice. You know, small Meeker looking and we use I guess, expedited natural selection to make the corn super fat and feed population. Bob 5:01 Well, you brought up apparently something similar has happened to bananas as well, because apparently the bananas of old don't taste like the bananas today. Well, Kevin 5:09 how long have bananas tastes? They don't exist? Bob 5:12 What? I don't think the bananas of old actually exist anymore. Kevin 5:17 All right. I mean, there's those things called playing pains or whatever they kind of look like. But I Bob 5:22 think that there's I'll have to find the article and maybe put it in the show notes. But there's definitely some discussions about how bananas have been genetically re engineered, you know, but through breed, you know, Kevin 5:34 bananas are like the number one selling thing at Walmart. I believe. Yeah. And I used to work at a Walmart distribution center, grocery one. And bananas were like the first class citizen and products in the warehouse. I mean, you want to get in trouble. Go mess with the bananas. There's a whole team of people that will like take you down, if you can try to mess with the bananas. It's amazing. Bob 5:58 All right. So we've we've crossbred corn Well, it's just it's very popular in the plant world to cross pollinate species to create a new plant, whether it'd be more suited to feed more people or visual appearance plants, you know, like flowering plants are pretty common there. Kevin 6:20 So there's a lot of people who are against GMOs because I guess they ignore the idea of natural expedite and natural selection because that's air quotes nature. There's also the laboratory version where they're kind of doing gene editing and then you there's no shortage of labeling. If you go you'll see gluten free and then you also see non GMO on the on the box too. Bob 6:42 But do you think so, as far as that classification goes, they're talking about laboratory genetically modified, not classic, just cross pollinate, and I saw a tweet and I'm not gonna be able to give the person credit, Kevin 6:55 but basically, it came down to the difference between laborat And natural selection is basically this human emotional laden burden that you put upon yourself because at the end, they both won't kill you. They both taste good, and they both will feed you. So who cares whether it was in the lab or natural selection, right. Bob 7:16 But I think in this is probably getting to the crux of we're going to anyway, I think that the general fear is if we're doing this artificially, in even so far as the artificially not encouraging the crossbreeding of species, those kind of things. But if we're doing it in a scientific lab, underneath microscopes with, you know, syringes and centrifuges, and things like that, I think the inherent fear is that that's going to cause a domino effect of negative consequence. Right. Kevin 7:49 And if we stick with food for a moment, and I told you, you know, what, here's corn, I'm not going to tell you whether it was modified by the laboratory whether it was not modified at all or was we just had many, many, many generations of expedited natural selection. Here you go. Would you eat it or not? Or would you care? Bob 8:10 I probably wouldn't care. I definitely wouldn't know. Although like supernatural stuffs tends to be a little bit different flavor, profile, texture, all that kind of stuff. But I mean, yeah, the, it really makes no difference to that the Kevin 8:24 other buzzword in food is organic, which if you look at the rules, basically there's as long as you check back to two or three of these boxes, you can use the word organic, but it's totally in my opinion, non GMO and organic is totally a marketing term. It's totally just some hipster way of saying we're better than you and it's just the new marketing. What do you think? Yeah, Bob 8:46 organic is more of a organic is definitely more of a an encouragement to follow a specific set of standards is outlined by some organization where the GMO is almost like a confession or not confession, like you know, so we're just letting you know this product has been genetically modified and I probably should have looked up for that. So what the true definition of genetic genetically modified is while I news on this next topic, maybe if you want to ask Mr. Google or so the next part is and so we talked about plants, and you mentioned the word breeding. So we breed plants we also breed animals so we have dogs right we I own two labradoodles that's unnatural for the most part, unless you have a Labrador now poodle, who are like friends lab thinking uh, you know, tootles can keep asking rato day Yeah, they use Kevin 9:42 like doggy Tinder, it's really weird, but you know, to, to labradoodles. So bark, left bark. And then we also have so we don't eat dogs. Well, unfortunately. But we have cattle, pig, sheep, you know, that sort of thing. We do have like the Bacon's and the stakes of the world. And we've also done a an expedited natural selection of those. We also have things like this is antibiotic free, we have free range chickens, things like that. So we definitely, I don't know that that rises to the level of God, but it definitely rises to the level of manipulation. And hopefully I've got enough time for you to tell us what GMO is defined as, Bob 10:28 Oh, totally GMO, or genetically modified organism is a plant, animal micro or micro organism or other organism whose genetic makeup has been modified in a laboratory using genetic engineering or transgenic technology. Kevin 10:46 A couple couple $5 word. That's right. We'll just go with it. Some science involved, right? Bob 10:51 Well, I think the I think the important part is laboratory. Okay. So if there's two different crops in a field and they cross pollinate, and it makes a crop that more suitable for fill in the blank that is not genetically modified that is just good old fashioned farming. Unknown Speaker 11:08 You are listening to the Bob and Kevin show with Bob Baty bar and Kevin chesky. Each week we cover relevant tech and social issues related to technology. Our website is Bob and Kevin dot show. And our episodes can be found virtually on any Podcast Network. Be sure to follow us on Twitter, Instagram and Facebook. Just search for Bob and Kevin show. Kevin 11:46 Just to take it to the extreme because that's what we do here. So if I now build four walls and a roof around your field, and they're making babies under the roof, and I stick a sign on that says, Kevin's laboratory farm is that now GMO Bob 12:03 Would you be using genetic engineering or transgenic technology? I think the operative words in their engineering and technology Kevin 12:15 i don't know i don't think Bob 12:16 i don't think you would. I think if you put a house over your plants that we're doing it with people don't the plants that weren't there species I think Kevin 12:25 you're I was gonna ask you then if the tomatoes that are growing in a window at grandma's house is she, you know, practicing GMO so I guess we're saying no, Bob 12:34 so well, it's funny that you bring that up because I'm sure we'll touch on this later. Well, I'll save that. Okay, where are you taking this next? Kevin 12:43 Alright, so manipulating, breeding, growth of vegetables laboratory, things like that takes me to, you know, like, when when when an animal is going to be extinct like the turtle or loner like the black rhino or pick pick some sort of an Dangerous species as humans, and this is where I'm going to piss off probably, I don't know, maybe everybody I look at that and go, maybe we maybe we should let them all die. You know, please don't ask me. But I'm Bob 13:14 asking No, but I think there's a definite, I don't think it's just you. I think there's a whole camp of people who believe that the natural consequences of all of our actions are those natural consequences, and we should let those play out. I think there's obviously another group camp of people who believe that we should do everything within our power. I'm doing everything in air quotes, by the way, right, everything in our power to stop that destruction based on our natural consequences. But then, however, some people in that camp would be appalled if quote unquote, unnatural methods were used to course correct, even though unnatural methods probably put us on the course in the first place. Kevin 13:56 Right. So to recap, I on one hand, we introduced Wolves to bounce an ecosystem. On the other hand, we take an ecosystem that is favoring the extinction of obsolete potentially animals. And we we artificially prop them up as Bob 14:13 well. But I think in a lot of those cases, those are reintroductions. So, let's say especially as it relates to the wolf, the wolf used to roam free across many a continent. And then due to expansion, technology, and probably very specific measures to remove wolf populations from an area. Now, we're finding that they did serve a purpose in the conservation effort and management of wild animals. So now we're reintroducing species back into areas where they used to be but aren't any longer Kevin 14:57 and I get the whole idea that ecosystems can collapse and you have to possibly recognize it and make adjustments. But let's take it to the extreme of Bob and Kevin show favorite. So the Tyrannosaurus Rex was not completely extinct by a meteor. In fact, they lasted until the early 20th century. And the final ones were placing the captivity. Do we need to keep them alive? Is what I'm getting. Bob 15:25 Yeah, I'm sorry. Wait, wait, this is fake. Oh, Kevin 15:29 totally. Yeah. So okay. Bob 15:32 I was like, what books are you reading? Kevin 15:34 No, I am definitely at risk of sounding like weird flat earther. guy. I completely hypothetical. What if the T rex lived to modern times? Would there be people out there going we need to save the T Rex. Meanwhile, we haven't heard from that person in a while after the after they tried to feed it, you know? Bob 15:52 Oh, without a doubt. There would be the save the T rex people. All right. Kevin 15:58 All right. So now we've kind of set the table here. So, imagine me, Bob 16:03 I'm smoking. Kevin 16:04 Where is this going? So imagine you had the ability to bring back the T Rex, kind of like Jurassic Park. But your scientists were so preoccupied with whether or not they could they didn't stop to think of a shot. Or more likely, you had the ability to prevent or cure people of genetic diseases such as I don't know. I don't know, bad stuff. There's a whole list of bad things. Bob 16:30 Hold tight hold total list and imagine Kevin 16:33 that there's a technology allows you to cut and paste DNA like it. We're a frickin Word document, and just change the genetic code. And the recap. DNA is the double helix thing and it's got four sets of possible letters, I forget the four letters, it's like TCGA or something like that. And those are the only depending on how you combine these that's that's basically what defines Bob you as person me as person. And then everyone else. So sometimes there's errors in those. And those errors give rise to diseases. So there's a technology called CRISPR. Bob, do you know what CRISPR stands for? It's an acronym. Bob 17:15 I do have a tab open somewhere that tells me what CRISPR means. All right. Kevin 17:23 clustered regularly interspaced short palindromic repeats, wow, say that 10 times fast. So we're just going to call it CRISPR moving forward, Bob 17:34 and it's also typically grouped with a Unknown Speaker 17:39 protein called cast Kevin 17:41 nine, but there are more than one. So there's technology and it's I don't want to call old technology, but it's emerging technology, but it's been around for at least five years or so. And it allows this cut and paste of somebody's DNA. So the first question I had for myself was this Wait a second, you can cut and paste so DNA but DNA is per cell, right? So each cell has the same DNA, but if I want to cut and paste, you know, a skin cell, great, but what about the other cells in my body? How do I, you know, make it propagate through my body or whatnot. So I looked this up. And there's two ways to do this. They can either take cells out of your body, cut and paste and then put them back in. Or they can add at at the sperm egg level at the embryo stage, you know, right when it combined get the first cell they can edit that one cell, and they subdivide, now it's in every cell in your body sounds awesome, doesn't about we can just cure every disease, the end. Bob 18:44 Highly unlikely that that's the short. All right, well, Kevin 18:46 let's let's look some more at kind of how it works. So I watched the, I guess, the equivalent of TED talks and stuff on this in different videos on YouTube. So essentially, what CRISPR is is a molecular scalpel. It's actually according to them, Well, actually, it's very accurate, easy, quick and inexpensive. Those are usually things you don't associate with like new modern cutting edge tech technology, right? Bob 19:13 Yeah, I think like legit machine is only like Kevin 19:16 10 grand. And so there's a you can you can buy kits online for about $200 and you can do biohacking on yourself. And the way it works is in your cells, you have RNA, and they they program these proteins to look for a certain sequence of genes or nucleate proteins, if you will, in your body. And once it finds a match, it will then cut and paste the take the old one out, or I guess it'll be cut and recut. Yeah, it'd be cut and paste. Bob 19:50 Yeah, it can remove completely, it can replace or it can repair and repair would just be realigning the CGS T's and the is to put it in the desired sequence. Kevin 20:04 So the programmer amigos, ah, so this is pretty much a regex. So based on this pattern and kind Bob 20:11 of it kind of is Kevin 20:12 and Bob, when you if you have 99 problems totally. Okay. Yeah. So, because imagine what would happen and this happens all the time programming, hey, I think I got the the pattern to match and then you apply in it, either a doesn't match or B. What's worse is it matches a bunch of stuff you didn't intend it to match. So that's, that's kind of, Bob 20:36 if I have trouble with a regex for a phone number, for instance, right? You're telling me that scientists have figured out the regex for all DNA sequences or Well, I guess I haven't figured out for all of them. But they the literature says it's relatively simple. I believe that's a quote to pretty much decode anywhere they want to Kevin 20:59 so We can't so phone numbers, tough emails even more like controversial because there's like, hey, I need to read it. If you Google the regex for email, you will get so many answers that all of them say are right. And then there's the well, actually guy who always puts a little comments that go for it. Well, actually, it doesn't cover this cares. Okay, whatever. I got it. Okay. So, what's what's this best for? Well, apparently, if you have a single gene that's jacked up, this is the best thing so far that they've got. And one other delivery method that they have for this. And the idea here is this is going to cure some disease, that one delivery method that they're trying to use is create a virus that attacks every cell in your body, and it performs the cut and paste everywhere in your body. So the virus becomes Yeah, the transport mechanism, if you will. Bob 21:53 That's basically the process they were outlining in NPR. episode of radiolab that I listened to a couple years ago. I think they actually just revisited it not too long ago so Kevin 22:08 one of my kids has to get monthly infusions at the hospital and we have to do that for his foreseeable future for life as far as you know cuz he got a jacked up immune system. So there's a bit of an appeal here that hey, you know, we can do this here. If you know somebody with cancer, I guess that's one application to they can program. Things to, you know, attack cancer. But what, what is kind of weird when people kind of go to the Black Mirror episodes are you know, could you create a super soldier? Could you create a designer child or whatnot, right, so that's where it starts getting weird. Bob 22:46 Well, I think that's the slippery slope for most technology. Even that Netflix, limited series, I guess is what they're calling it that you had recommended even in episode one. That the practical uses versus the I would say cosmetic uses that Joe user biohacking in their basement. Like it was almost three to one cosmetic type. Kevin 23:14 Wow, that's an awesome transition because I got a list of things here that well, if this were possible, what what could people use this for? Well, obesity would be one of them, right? Hey, I want to be able to eat whatever I want. And I want my body to be able to just whatever, you know, that's, that's what I want Oreos every day and I still look amazing. So that's, that's one thing that people would use for vanity, right? Bob 23:39 Yes, so you got that metabolic and then also, like the muscle tissue, like the ability to multiply muscle tissue at a faster and Kevin 23:47 just to make things weird. Breast augmentation, right. Right now, it's very popular for women to do breast augmentation or reduction, if you will, some sort of body modification there and for men I don't have to lead you very far to tell you what men might want to do. Bob 24:05 If we say male enhancement, can we put that in the show title and get that click Yes. Kevin 24:10 Let's say one more time male, enhance male and there we go. So it'll definitely be in the transcript. Now, the SEC to me would be the next thing. Wouldn't it be great if you didn't have to do anything other than take an injection and now you're just Bob 24:25 shooting blanks. They almost have that down to an injection at this point. Anyway, it's such microsurgery so Kevin 24:32 and along these lines, what what if we perfect this so much, and I'm just going I like going to the black near end of the spectrum, sometimes what if we no longer procreate the old way air quotes, everyone becomes sterile through an injection, and then you just order your baby online Bob 24:51 you from the central repository. Kevin 24:53 You've heard this story before, except this time, you're going to take a swab of your mouth and then your potential mate And he sent both q tips in and Eddie that sounds high and for Box Tops from your favorite cereal and it goes to the central repository they engineer your child from an embryo they basically go the freezer and you know do their thing and you know drop in your whatever it is they do and then your kid shows up the woman doesn't even have to go through the pain of childbirth anymore they everything is custom ordered. It's your kid there's hopefully knows no swapping of DNA with the wrong donor you know? Yeah, whatever. What do you think? Bob 25:37 Let's let's take it back a half stuck, because I think the one of the original like sales jobs part of this would be we can eliminate x and x in this instance the sales job part of it would be something awful that is taking lives at a at a high rate at this time. current state in our you know our our global community of people so let's say that that works like without without a large dollar payment because it seems like this would be in the best interest of mankind so why would it be expensive first of all that would never go down like that it would be super expensive to start but let's say that they could fix something that that affected a lot of people the the immediate cascade effect is that of that is we have people starving all over the world now anyway, they do. So why would we want to artificially inflate our population that's already growing at an extreme rate? Kevin 26:40 And oh, so one of the specials that I was watching brought up immortality sounds great, right? We can live forever that's a shitty idea. It is a shitty idea and be even if you double the life of humans 200 years imagine the food requirements and the just the Annette air the the The actual cleaning of the human population numbers won't happen at the same rate. I mean, you want to talk about overpopulation now, Bob 27:08 right? The only thing I could think of that would be worse than being 100 would be being 200. Ah, Kevin 27:14 well, okay because of aging, but did you know that the common lobster does not age? Bob 27:22 Okay, by what standard? Kevin 27:25 I don't know I they were talking about how lobsters don't age and I guess squids, there's like different things that don't they still die. They're not immortal. But they don't age. They're the same, like, age wise. I don't know how you determine age of a lobster. But Bob 27:40 you asked him, Hey, Kevin 27:44 I'm 32 but I feel like I'm six months or whatever. Bob 27:49 I'm calling so much bullshit on that statement. The lobster doesn't Google it. Kevin 27:54 Check it, look into it, look into it. Bob 27:57 Alright, but the simplest Like ourselves age, like that's a known thing, cells age and die, right? And they're not they're not the same as the day that they were formed. So how do we have organisms swimming around our oceans don't age? Well, Kevin 28:13 I we'd have to look up the definition of aging. But the implication here is if humans can take lobster DNA and put it into humans, perhaps we don't age either or we can even reverse the effects of aging. Bob 28:26 Or we end up with a hard outer shell and well, okay, Kevin 28:31 see, I love I love your we didn't plan this. But you have another bullet point I have on here. Bob 28:37 Also, we never plan anything just in case. Well, we don't we do plan Kevin 28:42 separately, just not collaboratively. So right. But imagine, we're like, Hey, we got this figured out. You can go on Amazon. Order your crisper kit. You're like, Hey, you know what, instead of those tacky wild eyes for Halloween, I want real red eyes this year. So you go on Amazon you buy a crisper kit suddenly have red eyes You're like I'm getting tired of the red eyes. Let me go back to blue. Oh, you should make them like glow bioluminescent eyes when that be cool, you know kind of like the night King from Game of Thrones. Yeah, yeah. Add that to cart to Yeah, get one for me get one for me right what could go wrong? Bob 29:17 Yeah, but have you ever cut a piece of paper and then pasted it back together and then cut it again and pasted it back together. Eventually, eventually the ship gets shorter. And eventually it just breaks Kevin 29:28 kind of like if you take a JPEG and just keep receiving 1000 times it turns into this. So what I'm getting at here is, are we risking creating bio disasters by making this super easy and convenient? I would say yes, we're going to have that exoskeleton that we didn't mean to get from the lobster because we thought we're getting a no aging but now we have these freaks of nature, right? Bob 29:53 Yes. And did you see that panel is like the Silicon Valley Comic Con, which I imagine that's probably like the biggest freak shows of everything. But in the first episode of that Netflix special, they had that panel. And the one guy, I get what he was lobbying for, you know, this CRISPR technology is being highly regulated with good cause. But we won't know what the implications of it are if we can't test it on healthy people. So I get with the guys coming from but I just feel like you're just asking for shit ton of negative consequences. Kevin 30:35 Yeah, so like anything. So okay, let's let's take the invention of the atom bomb, which was preceded by the splitting of the atom. So when they split the atom, they probably will that the Manhattan Project was built for war purposes. And but I think it's Oppenheimer who ran the project was like, Fuck, you know, basically the cats out of the bag at this point, you know, we A new era is Dawn. And that's absolutely happened. And so the atomic bomb had some noble consequences, which is energy, nuclear submarines more military. So we had nuclear power and things like that. But it came at the consequence of having created the most destructive weapon ever. And so I look at this crisper stuff is going, Hey, this is cool. It has some good here, but it also has the consequence of having potential bio disasters, we could create some sort of biological weapon that can actually annihilate all of us overnight, you know, some virus or something Bob 31:35 almost seamlessly and silently, right. And I think you brought up you brought up Game of Thrones, and I was just thinking about, you know, the, what was that army? The unfallen Kevin 31:48 was this insanely, I believe, on Unix. Bob 31:54 Right, so they could in theory, speaking of the bioengineering Not just like chemical warfare but they could bio engineer people without remorse without you know, appendages that aren't necessarily needed or, or appendages that would tempt them. So or make it so Kevin 32:14 tons or make it so humans are not hermaphrodites and you can actually reproduce asexually and you don't need a mate anymore. He just divide or whatever boy, Bob 32:25 I'm pretty sure the republicans will never let that like I said, we're gonna piss off everybody here today. So Kevin 32:33 hey, if we really want to get this off people abortion, I'm gonna bring up a portion. So you're probably wondering, how does this mix in Well, in a lot of countries and a lot of places, there's pre, there's early pregnancy testing, and some people choose to abort their pregnancies or terminate the pregnancies, whatever the PC version of that is, based on that and so you could look at this as go hey, we Prevent abortions of the that variety. If we can fix it, you know, hey, we say there's a hole in the heart. Let's fix that. We don't have to abort the fetus, right. So there is, you know, I could see people coming. Where am I going with this Can I can see people from both sides of the aisle, you took my Bob 33:19 line. Kevin 33:21 Damn it, I could see people on both sides arguing for and against this is what I'm saying when I say both sides it's the tip. It's the two sides in America people spoiler alert. So I'm not sure how this will come down. Do you see this going any particular way. Bob 33:38 I think that the in womb, genetic defect repair is definitely another one of their sales tactics for this type of technology. But also to go back to your blue I read I you know, cutting that piece of paper so many times that it just shreds I could see that same technique being used for fertility like an on and off bit for fertility. Oh yeah. So you mentioned it with the vasectomy, you know, it could obviously be used for women's reproduction as well. So you basically could go in and instead of being on birth control pills get your DNA edited over a six week course or whatever it is to turn off your reproductive organs and then when you get to a point in your life where you think you're ready to settle down and have a family just go back and get it turned back on the new birth control right hey, I'm young I don't need It's the new everything control unfortunately I that gives rise Kevin 34:35 to Hey, I'm going to take my children down to the clinic turn hit the off button because they get born with it on so I'm gonna hit the off button. Hey kids, you just go be promiscuous as you want to learn about everything. Hey, we've even taken care of HIV. That's not a thing. We turn that off too. So you resistant just go for it. Man. This is getting really weird. Bob 34:56 But I think when you like in going back to the doctor Come again or whatever that is the limited series. It's called Netflix Kevin 35:03 called unnatural selection because I don't think we've set on Bob 35:06 natural selection. Okay, sorry, I meant to name it the first time. But you can go back to unnatural selection. This whole discussion of putting these kits are the ability for these kids to exist in a quote unquote, home lab. I mean, basically, you could start to, you know, there's the people there that were biohacking themselves, but you could most certainly biohack your kids without their consent, though. Kevin 35:31 Really. That'll be the next thing because we already have let's see, it's the HPV virus vaccine that you can give to a tweener. 12 1314 ish. And that's controversial. I mean, heck people by getting the measles vaccine controversy. So, you know, I have Yeah, I have. I have a hard time thinking that this will get very far. However. I think some of the aims of these biohackers is to make it so simple that if it's not legalized There'll be a black market of biohacking out there, kids. You're worried about marijuana worry about people getting genetically modified at this point? Bob 36:09 No, I think that the biohacking movement is well underway. And I mean, body modifications have really transitioned into biohacking to be more permanent in nature. And it comes up and sci fi shows all the time. Oh, so I mean, it's definitely part of culture. And this technology, like I said that one article I found, I think the base regular machine that scientists uses only 10 grand for crisper, and now they've found a way to replicate it for literally pennies. So do you ever watch crime shows like the last 48 or? I try really hard not to Kevin 36:47 so I'll binge watch some of those and just think less of humanity. But a common theme during those shows is the DNA match the results? Well, guess what I do. You know, let's say you murder someone I didn't wanna say I murder someone. Let's say someone murdered somebody. And I bought one of these kippy if you're listening, Bob 37:08 I'm sure Kevin murdered someone. Kevin 37:12 Oh, man, it's weird. Bob 37:13 Let me finish my thought took off his game. Kevin 37:16 So, could you modify your DNA after the crime, and it suddenly no longer match? And now I am able to say, Well, I'm only an 8% match versus the 99.99% match. I didn't By the way, you're not the father. Well, Maury Povich for you. Bob 37:38 Yeah, it's totally gonna mess up the Maury show. Um, but no, I, first of all, huge personal disclaimer here. I've never killed anyone unlike my co Hey, hey, wait, wait, wait, wait. That's not what I meant to say. I don't know from the research that I've read. So I think it's very, very realistic to say, yeah, you could tweak something in be that not 99, nine match. But I don't know how long the course is to make a genetic correction to that. Kevin 38:10 I don't either. Of course, that's during a lot of these talks. And one of the the, I don't know, she's a co founder, but she's one of the names. Her name's Jennifer Doudna. Bob 38:23 Yeah, that actually sounds right. She was, Kevin 38:26 you know, she was on this panel. And she was very upfront and said, hey, look, we can change one gene, we can maybe change a few sequences. And then when the panelists were being asked about these black media type things, well, not yet, but she didn't rule it out. She's like in the future, maybe. But she kind of threw a dose of reality. I think on some of that. I did have another sort of use for this. Imagine your Russia, North Korea, or even maybe United States is they're known to do some door during our Let's say no let's say you had a detainee or a dissident political dissident like China, you know, if you're not part of the party, you're against the party. Could you modify he send them to a, let's just say, we'll just Bob 39:15 go China? Okay. It if Kevin 39:18 could you modify them in a way that would mark them or alter their behavior or some sort of or even like, you know, your truth serum? Hey, turn on these jeans and they will tell a lie, you know, during the interrogation. I mean, there's so many bad things. I think they're gonna come out of this. Bob 39:39 Oh, no doubt. No. Kevin 39:42 All right. I have a question for you, Bob. Okay, if it were safe. And if you had some sort of debilitating thing, hypothetically, would you do it to yourself or would you consult a professional who could perform so Sort of modification to you. Bob 40:03 I think that begs such bigger questions Kevin 40:09 you're so responsible by that's such a responsible answer. Bob 40:14 I think about this all the time, because, you know, I think you and I both do because we're parents, you know, our kids, they're unique and ourselves personally, we're unique because of who we are and in what our circumstances and how we deal with that circumstance. And just like you wouldn't want to say you're defined by fill in the blank. It's still part of who you are. Um, Kevin 40:41 I just don't know. I think I would fear the downside. Like the unknown downside. Yeah. Try to undo something that's already been done for whatever reason, or no, really so but it's done if I were to have surgery, and I've had minor surgery, but you know, I've known people have major surgeries. That's a physical manipulation of the layers above the stack, if you will of DNA, that's a higher order modification, getting your ears pierced getting a tattoo, those are all body modifications to different, you know, extense. So I could see a rash now that DNA modification is just a modification of the body at a different stage of the the diagram, if you will. And if I were dying, and there were a certain cure, and there was, I guess the chances of it working or not, or high or low, but you're saying there's a chance, you know, I might consider it Bob 41:43 right, but that's life or death. I think that there's so many situations where in betweens, right or out of convenience, Kevin 41:52 like being paralyzed, or Bob 41:55 Well, maybe parallel because I think parallel ization definitely contributes to a life or death situation. So there's a saying an injury Kevin 42:04 that is based on life, limb or eyesight. So I think one of those three would definitely rise the level of Hell yeah. Let's try it. However, vanity things such as I have got this freckle right here on my butt or whatever. Can you make it go away but don't use a knife but can you just make it no longer part of my genome and you know stuff like that? I don't think obviously it's worth the risk. But I'm I know people who have you know, those giant where they call it gaged earrings a man. He got frickin coffee cups saucers, his gauges, there you go, man. You go. I'm not that but you that guy would get his freckle removed off his ass. Bob 42:54 Yeah, I wonder if that would work. I don't know. There's just so much you don't know about but he There's one thing that I wanted to make sure that I got to before too long. One of the things that freaked me out in doing this research and I don't know if you saw it, or notice it if you're doing any of the research online, but there are freaking ads. Like I'm staring at one right now. It's in the middle of one of the articles that I pulled up. It's about the the lab that found a way to basically make crisper tools for pennies. There's an ad on the page, it says stop doing crisper for yourself. Order your knockout cell lines. So there are companies that you can pay to fabricate your knockout cells for you. Because apparently that many people are doing at home. There's advertising for it. There's a Kevin 43:46 there's a market for this. Wow. Yeah, well, I wonder if this is kind of looked at by the FDA as like a supplement. This hasn't been tested. This statement has not been tested by the FDA. Good luck Bob 43:58 but I think one of the channels Is that they talk about all the time, is that China? Or maybe it's because it's only regulated at the upper levels and not at the personal level? I don't know. But, you know, one of the arguments is, well, China's way ahead of us in this technology, because it's not as regulated as it is here. The old we gotta get ahead of them. The bad guys, right? Seems to be a very common theme. Unknown Speaker 44:23 So Kevin 44:25 I just want to ponder another potential use of this. So life is very fragile. As far as we know, you need oxygen, you need water. You need you know, place here or you need a space suit. Imagine going to Mars without a space suit. We can genetically modify our astronauts to breathe a very thin atmosphere or we could genetically modify them to breathe methane on one of the moons of Jupiter or Saturn. Of course, there's other problems like pressure suits. I get that don't don't that me but man In, we can modify, you know, humans to travel through the cosmos. Bob 45:06 But all that would require an extreme amount of experimentation, which is the problem right now, Kevin 45:12 like takes me relax. All right click the aliens. What? What? Why? So they're aliens. Okay, so imagine aliens have already had this problem. They want to go visit Earth, but we can't get there because of these biological issues. So what if they've evolved to the point where they now can do their own DNA splicing and editing and they've modified their bodies so that they got big, black guys and their small bodies and they're gray and they're very smart, and they can travel across the cosmos? Maybe maybe aliens already doing this Bob? What do you think? Bob 46:00 I'm going to guess they probably have better genetic code to start out with. That's what I'm going to guess. All right, so they live in a society where the genetic modification of their organism is an accepted norm. And they bioengineer themselves to suit whatever Next, you know, Global Mission they have. Kevin 46:27 All right, so we've always heard of things like intelligent design versus evolution, right, tell Joe is gonna piss off everyone. Right? So, so intelligent design is basically the idea that humans started out as humans, they did not evolve to humans were evolutions. Basically. We started it in a petri dish, you know, a little swamp and then eventually became humans over millennia. So what if humans were actually designed by a And I keep bringing this back. I'm sounding really like tinfoil hat. I apologize to everyone. But what if humans were dropped here as an experiment, or monkeys, or apes or chimps, if you will, we're here. And then they're like, hey, let's take our own DNA and take these bipedal organisms with arms, and let's combine it with our own DNA and just leave them here. And we're some lab experiment that they're checking in on us every now and then. Bob 47:28 Well, I think that's a definitely a common argument for a lot of technological advance. It's either the simulation argument or that, you know, we're the experiment of an alien race. But I don't know if that addresses or solves just because can does that mean we should kind of I think, which is the original premise of you know why we're talking about this, right? Kevin 47:56 Right. Oh, for sure. Because for me, this sounds like a James Bond. Movie plot at this point where you got the evil you know, the the antagonist is totally like building this genetically enhanced army or building a bio weapon, or God knows what the thing is, is I think this crisper thing is flirting with the hubris of humanity. And I think it's this innate thing that we have, especially as parents that we want to create life and you know, programmers want to create artificial intelligence, and you got the whole gamut of things. And it always comes back to what you said which is just because we can should we and I am on the fence at this point because I can see the potential good for it but I am right now the bad luck so overwhelming. How do you feel about it? Bob 48:48 Well, I think the bad looks so unknown, which makes it overwhelming, but I'm also super intrigued that I didn't think of this and you brought it up with the the AI the machine learning And then we can even tie this back to bad bias to, I can see it going toward analysis of data, ai determining what the perfect gene sequence looks like. And then the system of splice is done with crisper to get some one person to that point. And, you know, depending on the garbage that goes into that, ai analysis, the output could be terrible. Yeah, Kevin 49:33 we can end up with nothing but zombies, a bio weapon that just inadvertently kills everyone. Or we could end up with a great future. But the thing is, it's like, it's like counter terrorism, you have to account for the 99 things that they can do to hurt you and they only have to be successful once, right? So I look at this as going for the one thing or the few things that it looks like it could be good for. I also see that Listen, the downside of this and that I don't know if it really scares me yet because it's it's more academic papers and there's a couple cases out there at this point. But as they mentioned in one documentary in the 70s, they had okay computers, but they knew they would have better computers one day. Well, right. I think the same thing applies here. They have pretty okay technology with gene editing. Now, we know in the future, it'll probably only get better. Bob 50:31 Okay. Unknown Speaker 50:33 What do we forget? Bob 50:35 Well, I think it'd probably be a little bit remiss to not mention that I think we're both in agreement here that regardless of the outcome, I think the the technology itself, in the research that's, you know, been put into this, this problem that needs to be solved, apparently of, you know, splicing genes in figuring out that you could use a Protein x is a virus to basically do the work. It's pretty impressive. I mean, it's a very impressive Oh, Unknown Speaker 51:07 it like who who thinks of this shit. Kevin 51:08 I was reading some of the Wikipedia articles and trying to make sense of it. And I just said, I'm glad I'm a programmer. I'm not a biologist, because I don't know what's going on here. Bob 51:19 It feels like it crosses over into programming a little bit, though, with the, you know, the sequence and the wirings. And the knowing how do I like developing a technique for identification of the bad sequence, and then the replacement or the reorganization or removal of that sequence? It's just crazy. Kevin 51:38 Yeah, it's kind of like the chemical version of Find and Replace. Bob 51:43 Yeah, it's, Kevin 51:45 like you said, and that's and and for non programmers, programmers out there. regex is stands for regular expressions, and there's nothing regular about them. It's just a fancy way of pattern matching. Bob 51:55 No way. You could totally find the pattern in anything. So Kevin 52:00 I'm going to watch more of unnatural selection on Netflix because it's a little short series I haven't made it through all the way there's some good videos on YouTube regarding crisper and whatnot. I've probably pissed off science people can conservationist religious types, but I just wanted to do a kind of throw out there all the angles and definitely got in some chops for aliens are probably Bob 52:28 probably not putting that in the title but hey, and for anyone that's still listening, if you've got thoughts on this, interestingly, we got well Kevin because of course he puts all of his good tweets on his own Twitter account. Got some pretty good engagement on talking about that we were going to be talking about this on the show today. So if you did listen, obviously we gave this a 30,000 foot cuz Hey, we're not biologist be we're not scientists, but it's a technology topic that I think is There's gonna be more and more Unknown Speaker 53:02 brought to the forefront as these days tick by so let us know what you think on it, for sure. Kevin 53:09 All right, Bob, I think we're good to go. This is good, good, good stuff. I'm sure we'll have more to follow as the technology develops Unknown Speaker 53:17 and or as zombies approach my front door. Bob 53:22 Just Just remember kids don't give your DNA to any of the online stores for DNA and maybe don't modify it with it at home crisper kit. Unknown Speaker 53:33 Yeah, just say no kids. Unknown Speaker 53:38 Hey, have you ever wondered how you can get in touch with us at the Bob and Kevin show? Well, first, you can try us via email and comments at Bob and Kevin show calm or are you more into social? If so you can find us on Twitter at Bob and Kevin show or on Instagram, as Bob e Kevin show. That's Bob. The letter M Show Unknown Speaker 54:00 and if you're still on Facebook, you can even find Unknown Speaker 54:03 us@facebook.com slash Bob and Kevin show and for the serious business fans, you can even find us on linkedin@linkedin.com slash company slash the dash Bob dash Kevin dash show. How's that for a handle? Let's connect Transcribed by https://otter.ai

Subscribe to the podcast through iTunes and Google Play.    Dr. Clifford A. Hudis: Welcome to this ASCO in Action podcast. This is ASCO's podcast series where we explore policy and practice issues that impact oncologists, the entire cancer care delivery team, and the individuals we care for-- people with cancer. My name is Clifford Hudis, and I'm the CEO of ASCO as well as the host of the ASCO in Action podcast series. For today's podcast, I am delighted to have as my guest Dr. Ned Sharpless, the director of the National Cancer Institute. The NCI is the largest funder of cancer research in the world, and it has helped to drive many of the major prevention and treatment advances we've seen over the past 50 years. This includes things like HPV vaccination and the identification of the link between HER2 status and breast cancer outcomes and treatment, as well as new discoveries that have dramatically improved outcomes for childhood cancer. Dr. Sharpless, welcome, and thank you for joining me today. Now, we really have a whole lot to discuss, but before we get to our planned topics, I have to jump ahead and start with the president's State of the Union address, when President Trump mentioned that he wants to see $500 million appropriated for childhood cancers over the next decade. Can you talk a little bit about how you expect that, specifically, to play out? What will the NCI be able to do with those new specified funds for pediatric research? Dr. Ned Sharpless: Sure. I think childhood cancer-- childhood cancer is an area where the National Cancer Institute has had a long interest and a robust portfolio of research. And I think it is an area where we've made some progress, in terms of mortality, over the last few decades. But you have to say two things about childhood cancer. While progress has been good, and we're making-- more kids are surviving cancer therapy today than ever-- there's still a long way to go. Too many kids dying of cancer in the United States, and even the kids that we're able to cure have these significant lifelong survivorship challenges, in some cases. So the therapy that is curative may leave patients with side effects of surgery and chemotherapy and radiation for the rest of their lives. So better treatments for kids and less toxic treatments for kids are what we are really looking for. And with that amount of money, I think a good-- the thing that it appears to me that one could do to most quickly move the needle in childhood cancer-- which, as you know, is a collection of less common cancers, even rare cancers-- is really a more intentional effort at aggregating and using and linking clinical data with molecular data and other sorts of patient data, so that we can really learn from every child with cancer in the United States, so that we can really figure out what's working in certain populations and then disseminate that information as rapidly as possible-- without having, in all cases, to rely on slower clinical trial structures that are challenged for certain populations where accrual can be difficult. So I think that is the vision for the president's initiative, is to, with additional funding, allow for very aggressive, intentional, and organized data linkages and data aggregation so that we can learn from every trial and therefore treat every child's cancer in a better, more effective way. Dr. Clifford A. Hudis: You know, I think that's great. And that actually provides two different segues-- one I'm going to pick up right now, and one I want to come back to. The first is about data-- big data, specifically. We'll come back to that. The second is about the way the pediatric oncology community for years has really led in designing studies that could accrue the majority of children diagnosed with various specific diseases. And that leads me, that idea of eligibility and the structure of research, to ask about the way that you're thinking about modernizing clinical trials. This is something I know you wrote about in JAMA Viewpoint in the last couple of months. You addressed financial pressures, the need to increase overall rates of accrual to the trials, especially representing patients from underserved populations. Can you expand a little bit on that effort and what kind of progress you see as possible in the coming months and years? Dr. Ned Sharpless: Yeah, I think everything we do successfully in cancer today is in some ways the results of a clinical trial. And this is clearly one of the most important things the NCI does, in terms of moving basic science into patient care through experimental clinical trials. And it's an area where we-- frankly, a lot's changed in the last couple of decades. When I was a wee fellow, the clinical trials apparatus was very different from the way it is 20 years later today. And we need to make sure that we modernize the clinical trials process to keep up with the changes in our understanding of the biology and the new kinds of therapy we have for cancer. So that brings up a bunch of items that are areas where the NCI is really doing a lot of things. So, for example, one of the first problems I noticed when coming to the National Cancer Institute was that the clinical trials infrastructure, the big networks that we have for doing these kinds of trials, were under-resourced, that they had a funding problem. And they were becoming non-competitive with the trials sponsored by industry. And this showed itself in many ways, in accrual fees for patients, or the wait times to get the trial open, or the slow accrual once the trial was open. And so they were laboring under a number of problems. And so we decided we had to invest in the Clinical Trials Network and have been doing that and will be continuing to doing that in a number of ways-- through direct funding to attempt something like the National Clinical Trials Network or the NCORP, for example, the NCORP organization, but also by additional funding for biobanks and data aggregation initiatives, targeted clinical trials, et cetera. I think we've also-- there are some structural problems with the clinical trials that you alluded to. For example, eligibility criteria, I think, hadn't really kept pace with modern clinical trials. And I think ASCO and other groups have played a really important leadership role in identifying what are good eligibility criteria and which ones are not as necessary anymore. And then, do we have to have the same criteria in all the trials, and be more thoughtful about how those are used as a way to enhance accrual, because often we have a-- superfluous eligibility criteria can limit accrual. And increasing accrual by a variety of measures is really important. And we've thought a lot about how to do this through novel ways of clinical [? house ?] matching. I think one of the more successful efforts we've had in clinical trials accrual recently has been the MATCH trial, the NCI MATCH trial, which was able to accrue 6,000 patients at 1,100 sites in the United States, filling a targeted accrual two years ahead of schedule. It's the fastest-accruing trial in the history of the NCI. And I think one of the things MATCH teaches you is that if you have an interesting trial that's written in a nimble way that is open in the community-- that patients don't have to drive six hours to a cancer center, but can go to a local NCORP site, for example-- then those trials will accrue. We can accrue quickly, and we can accrue underserved populations, and we can accrue rare cancers. And that framework is more nimble than, say, the large phase III randomized trial run only at cancer centers that we had 10 years ago. There is still a role for large, randomized, phase III trials. The NCI is not backing away from that, or where we will support those. But I think, as we discussed in the JAMA piece, we really have to be thoughtful about where the NCI needs to be involved with those kinds of trials, compared to which of those should be supported by industry, for example. Dr. Clifford A. Hudis: It sounds like you're alluding to something I think you and I discussed even when you first got into your current role, which is the identification of those trials that industry should run, essentially, itself, and those trials that the NCI should support as complementary to industry trials. Can you expand a little bit on how you see that distinction and where you draw that line? Dr. Ned Sharpless: Yeah, the thing to know about clinical trials in oncology in the United States right now is most are actually paid for by industry. There's a huge pharmaceutical industry spend on clinical trials, and from my point of view, that's great. The fact that industry is paying for trials to develop therapies for cancer patients-- that's less money the NCI has to spend on those same questions. So we think that's a wonderful development and healthy for cancer research. But if that's the way it's going to be, then the NCI has to ask itself-- for the precious moneys that we have to spend on clinical trials, we need to use those in a way that's maximally effective and, in particular, not duplicative with what industry sponsors are doing. It's important to say, we do a lot of work with industry. So it's not just us either-or. Many of our trials, through these agreement processes called CRADAs, allow us to do trials with pharma sponsors and use their compounds in our trials. And that's a real boon to our research effort, as well. But there are certain kinds of trials that are very important where we really want to know the answer, but they're a bad fit for what industry is going to fund. For example, a de-escalation trial-- that's a trial where there's a standard of care that's pretty good, but the therapy is toxic. And so we'd like to see if we can get the same good outcome in a population using less aggressive therapy. A very important example of this was the TAILORx trial recently, where we showed that based on a genetic risk score, an RNA-based risk score of the breast cancer, women with estrogen receptor positive breast cancer-- many of them could forego cytotoxic chemotherapy and just take anti-hormonal agents and have the same good outcome in terms of their long-term survival. So that's a trial that is not going to be industry-led, for a variety of reasons. But I think it is the kind of question that's really important for patients. It's important, also, to say that de-escalation trials are hard to do. They require a lot of thought. They don't always work. And so they require these comprehensive thoughtfulness and infrastructure that the National Clinical Trials Network can provide. So an additional example is these multi-modality trials we have, where maybe two different agents come from two different pharmaceutical companies, and then there's some surgery and some radiation. There's very complex, multi-integrated care. And those can be very hard for a single sponsor to run, but, again, can be a very good fit for the NCI. And there are many other examples like this. But I think the real question we have to ask is, if our budget is limited and finite, what are the trials that the NCI really should do and lead on? Dr. Clifford A. Hudis: Yeah. And I think one of the points there is you need to conduct-- we need to conduct-- trials as efficiently as possible, getting the most so-called bang for the buck. You alluded to the fact that the NCI, along with ASCO, has been working on making trials essentially more efficient by making them more representative of the actual cancer population we end up treating. And a specific area of focus for us at ASCO, in this collaboration and also in our TAPUR trial, has been driving the eligibility age down below 18. My understanding is that this is something that you're adopting as a recommendation across the NCI, as well. I guess my question is, how broad and how quickly do you expect to see this implemented? Dr. Ned Sharpless: We have a number of efforts related to these barriers to accrual. You mentioned age as one of them and other sorts of exclusion criteria. And we've looked deeply and thought about this sort of care across the continuum of life-- both age limits on the less than 18 side, but also at the greater than 65-year-old side, where we see, often, eligibility criteria structured around a maximum age that don't often make a lot of sense. So that is one of several topics that we are addressing. As you know, we have a variety of networks and programs, and we fund a variety of kinds of trials. Some are led predominantly by the academic institution. Some are led through NCI networks. And so we are rolling out these policies, not in a one shot fits all way, but across these networks at different scales. They often require scientific buy-in from the other participants, and you know how that process works. I think this is an area, fortunately, where there is a lot of buy-in, where we're not having lengthy debates about whether or not we should do this. Really, the question is how we operationalize it and make it happen as quickly as possible. Dr. Clifford A. Hudis: That's great. And you know how strongly supportive we are, on lots of levels, for this effort and the related ones, in terms of barriers to accrual. I want to pivot, though, back to something that you introduced earlier about the big data. And my understanding is, in the annual plan and your bypass budget for 2020, you specifically called out the need to harness big data to speed up all of our work across the cancer research enterprise. And there are many companies, organizations-- we ourselves at ASCO have CancerLinQ-- that are involved in trying to collect data, share it, analyze it, and advance science and clinical care. But what exactly do you see as the NCI's role in facilitating this, and what do you think is our biggest challenge going forward? Dr. Ned Sharpless: Yeah, it's an interesting topic. I think the-- it's maybe two things to say off the top about big data in cancer research. The first is the NCI already has one very important example of how big data can transform a field, and that's The Cancer Genome Atlas, which later became the Genomic Data Commons. This is petabytes of genomic data that we make available in the cloud now to any researcher, basically, who is interested in cancer. And that set of data has led to thousands of papers and just a fundamental reorganization of how we think about cancer biology in many ways. And it's been a huge success, I would argue, and well worth the investment of the NCI to do it. And the data has been used in ways we never envisioned. We never thought of some of the papers and applications that would come out of the analysis of the Cancer Genome Atlas, for example. But the problem, then, one quickly sees, is that while that data set is great, it's limited. It doesn't have the clinical data, it doesn't have radiology and histology, it doesn't have-- we don't really have a way of binning big epidemiologic cohort data, for example. So the GDC, the TCGA, the Genomic Data Commons, proves how useful these kinds of data aggregation efforts can be, but also makes very clear what the shortcomings of our modern efforts are. The second thing to say is that this is a problem where the NCI is well-poised to be a leader, right? There are a number of issues around data sharing and data aggregation that really benefit from a Switzerland-like federal entity, a non-conflicted, dispassionate entity like the NCI that just wants to create the data structure in a way that's maximally beneficial for everyone, so that there are-- this is an area where the imprimatur of the federal government really allows us to play a role that would be hard for other groups to take on directly. And so I think this is a reason why so many groups have been looking to the NCI for leadership on this topic. So what are the challenges to big data? Well, I think that one challenge that has been spoken about a lot publicly is this issue of data hoarding by scientists and physicians and people who have these sets of data they don't want to share for academic competitive reasons. That is a problem. I'm not going to say that doesn't exist. But I don't actually think that's the biggest problem. I think a bigger problem around data sharing is just it turns out to be really hard to do. And by hard, I mean expensive. It turns out to be-- these various data sets were not created, initially, with the intent of sharing them. They're often in different formats. They're often governed by different kinds of data use agreements, which are governed by the consent form that the patient signed to have their data included. And so linking them can be both very technically difficult, from just a computer science point of view, and can also provide a lot of administrative and logistical hassles from the data sharing, data use agreement point of view. And so each one of these things is just something the NCI has got to work through, or someone like the NCI-- is figuring out how to link disparate data sets, how to get the right kind of data abstracted from charts that we want, how to develop the right work force to study big data with big data analytics, and then that is a big problem. So there are a number of areas where the NCI can address the challenges. And I think we'll make progress. I mean, the good news is that we understand these problems. This is not like we need to-- there's some fundamental problem of biology that we need to figure out. The bad news is that the problems are weedy, complex, and many, many layered, and require us working through them. But that's what we can do. We have support from the government for this. The moonshot had a lot of funding for data initiatives, which we've been employing to get these structures going. And now the Childhood Cancer Data Initiative, for example, I think could really-- that's a nice demonstration project, if you will, because it's the right size. Childhood cancer is about 16,000 cases a year. And so I think we can show what this radical data sharing, if you will, this data liberation project can do-- you know, that population and how useful it could be to larger groups of patients like lung cancer, breast cancer, things like that. So I think that these are the kinds of things the NCI can do with help from other federal agencies and academic partners and groups like ASCO. This is certainly not an area where we plan to go it alone. There are a lot of stakeholders and a lot of great ideas. And I think that by organizing and convening these initiatives, we'll make progress. Dr. Clifford A. Hudis: Well, I really, first of all, appreciate your calling out the fact that data hoarding in isolation is not the single biggest problem, because I think that's a frequently-cited limit. And I agree with you that it's less of an issue than all of the other ones that you highlighted. In that regard, I understand that you just announced a new office. I think it's the Office of Data Sharing? Can you expand on or explain how that relates to these challenges and what it's going to, hopefully, accomplish for us? Dr. Ned Sharpless: Sure. The Office of Data Sharing is something within our Center for Bioinformatics and Information Technology. It's getting stood up now. It's been around for about a year, even less than that. It has a new leader and a few FTs, and it has a number of jobs intended for it. I mean, there are a number of ways that we would like the Office of Data sharing to-- a number of problems that we think that the ODS can help serve with the external community in terms of data sharing, like these issues around consent and data privacy that I mentioned. But right now, an intense focus of that office, because it's something we really need to solve, are related, really, to the issue of accepting data and allowing access to NCI data at present. So we have this complex structure whereby academic investigators can give data sets to the NCI. That's harder than it sounds, because we have to make sure the data are of good quality and they're properly consented, and we understand the data usage agreements and that kind of stuff. And then we have a means to allow access to those data to accredentialed investigators. And there are a bunch of issues with that that are more complicated than you and I would want to go into right now. But I think that's consuming a lot of the bandwidth at that office right now, is the problems around, for example, the dbGaP entity, whereby different investigators give data to the NCI and the rest of the NIH. That has caused a bit of a bottleneck, and so we're trying to work through some of those issues. One thing, for example, that I think the ODS can do and is doing already is this sort of concierge-like function. For people who have large, valuable data sets that they'd like to give to the NCI, we should be able to take those data sets as quickly as possible. But something that's happened in the transmission of those data is that we've realized the quality isn't quite what we wanted or the format isn't exactly right, and so we have these questions, and they go back to the investigator. And there's this sort of cyclical loop that can take months and really substantially delay the process. And so the ODS is jumping in there early on and intervening on that loop and making sure the data are the right format and the right quality at the time of initial submission, so that we don't have this back and forth that wastes a lot of time. So I think those data access and data transmission issues are a prime focus for the office right now, although it has a much larger mission as it gets stood up. Dr. Clifford A. Hudis: Yeah, a little bit like CENTRA that Rich Schilsky runs for us here at ASCO, in terms of access. But at any rate, I want to take the remaining time we have, and maybe this is a speed round on the cancer research workforce. So a couple of quick questions, perhaps-- first of all, has the Cancer Moonshot Initiative had an impact directly on the kinds of awards that you're making available to researchers? And if so, how do you think that might evolve in the next couple of years? Dr. Ned Sharpless: I think the moonshot, as you know, was intended to focus on these 10 areas identified by a blue-ribbon panel that were thought to be ripe for clinical translation, just about ready to go into clinic and to benefit patients in a very direct, immediate way. So the moonshot per se didn't include funds for things like really hardcore basic science or training, although certainly moonshot moneys are being used to some extent in both those areas, as necessary, as part of these translational efforts. So I think that what the moonshot has done-- it's done a couple of things. So first of all, that most of the awards granted by the moonshot mechanisms are more these-- are not the traditional R01, but are more of these consortia and network grants. And I think we've built a lot of infrastructure for research efforts, say, in immuno-oncology or in pediatric cancer or in survivorship. And those networks will both-- well, they will live on beyond the moonshot in some cases, I'm sure. And those networks will provide integrated research efforts, but also some training opportunities. So most of those include junior scientists and junior clinical investigators, and so there will be some opportunity for the moonshot both to drive the scientific area of study and also provide some training opportunity for the new people coming up. Dr. Clifford A. Hudis: Well, speaking of junior and new, I listened to your conference call, I guess, about a week or two ago talking about the pay line. Can you expand on your plans to support young investigators right now, given the always-present constraints in funding? Dr. Ned Sharpless: Right. This is a particular problem for the National Cancer Institute, because we've seen this relatively-- there's no other word than "massive" influx in the number of applications for the so-called R01 grants, the independent investigator-initiated award at the NCI. And this is-- our award number is something up like 60% over the last nine years or so. So this rapid increase-- which is, in most ways, a very good thing. I mean, that says that new scientists are coming to our field with new ideas and new ways to treat cancer, and the NCI can pick among these many applications and fund the very best ones. But it has this pernicious bad effect for the academic investigator community, and that is that their individual chances of getting a grant are lower. If paylines are really the number of funded awards divided by the number of applications, and the denominator goes up faster than the numerator-- both are going up, but the denominator goes up faster-- then the paylines are going to go down. And we think this is particularly a problem for junior scientists, because established scientists have seen paylines come and go and funding realities change. But new scientists aren't as used to the life of the independent researcher and, we think, are most likely to either leave science or move out of cancer research to another area of science. And we'll have to try and minimize that from happening, to the extent possible. So one of the things we've done at the behest, in fact, of 21st Century Cures, which included language asking the NCI in the United States to do this, was really focused on these so-called early stage investigator, the ESI. So the ESI is faculty. That's someone who's gotten a job, generally in an academic institution, and is now writing their first R01 grant, their first independent scientist grant. And we've done a few things for this population. One thing that's really important is we give them a special payline. We give them, effectively, a higher chance of getting funding. So if, say, paylines are on the order of 8% now for all Comer grants, for ESIs they'll be more like 14%, right? So a significant-- or 12%, in that range. So, significantly higher than what the general community is. I want to point out, also, that paylines are lower than the actual success rates of the NCI, which is a better number. The reason success rates are higher is because we do fund a lot of grants outside of the score. It's a little bit of inside baseball. But generally, if you write a grant to the NCI, your chance of getting it is more like 12%. And if you're an early stage investigator, it's more like 16%. Dr. Clifford A. Hudis: Thanks, Ned. To switch gears a bit, I know you've worked with the NCI throughout your career. But now you've been at the Institute's helm for nearly a year and a half. Has your understanding of the NCI and its role in cancer research changed or evolved in this newest assignment? Dr. Ned Sharpless: I think it has to be said that I was an NCI watcher my entire research career, and I thought I knew the National Cancer Institute and the National Institutes of Health pretty well-- as well as one can know these organizations from the external perspective. But since starting at the NCI, I've really learned that this amazing organization is much larger than even I realized, and that the scale and scope of the NCI is truly both awe-inspiring and, in some ways, daunting. I had a series of meetings as I started as NCI director where I would learn about these sprawling comprehensive cancer prevention and control efforts or new areas of basic research or clinical trials. And I just really had had no idea that the NCI was involved in some of these activities. So it was very illuminating. In some ways, it's thrilling, the things the NCI is doing. But I think it also made very clear to me another thing that I think I knew at some level, but didn't really appreciate the full scale of this until becoming NCI director, and that's the issue of-- although the NCI is huge and has this great reach and comprehensive nature, we are limited in scale. Our resources are finite, and the NCI, therefore, is really forced to make these difficult choices about which areas of cancer research to fund and how best to address our mission of reducing cancer suffering. So I think I was surprised both by the scale and scope of the NCI, but also by the fact that, despite how big the NCI is, it still has significant limitations on what it's able to do and has to make these difficult choices. Dr. Clifford A. Hudis: ASCO recently launched the "I lived to conquer cancer" awareness campaign that spotlights federally-funded cancer researchers and the patients who inspire them. I want to close out our conversation today by asking you, why do you live to conquer cancer? Dr. Ned Sharpless: Yeah, I think like just about everybody in the United States, my life has been personally touched by cancer. I've had friends and family members get cancer, and my father even died from cancer. Both of my sisters are cancer survivors. So I think I have a real personal stake-- like everyone in the United States, almost-- in seeing the reduction of cancer suffering and conquering cancer, if you will. I also find the problem fascinating from an academic point of view. I was drawn to cancer research because I found the biological questions of cancer research so fascinating. So I live to conquer cancer from this intellectual point of view, as well. And lastly, I have the experience of being a doctor, of being a medical oncologist taking care of patients with cancer. And I've had the frustrating experience of having patients not do well who I thought, I wish we could have done more for-- as well as the experience of taking someone who has a pretty terrible cancer but yet driving it into remission with therapy and then watching that person effectively survive the disease and become cured of it over years. And that is so special and so thrilling to be a part of that as a physician. So I live to cure cancer because it's personally touched my life, because I am a scientist who is fascinated by the biology of cancer, and as a doctor I've had the experience of helping people survive their cancer. And once you do that once, you just want to do that over and over again. Dr. Clifford A. Hudis: That's really great, Ned. It's fascinating to hear why progress against cancer is personally so important to you. And I'm sure all of our listeners enjoy hearing that, as well. I want to thank you again for joining me for this ASCO in Action podcast and for all the work you do at the NCI and across the entire cancer care community. Well, thank you for having me. As you know, one of NCI's most important partners in this effort against cancer is really ASCO. And so it's great to speak to you today. And thanks for all the things that you guys do for patients with cancer. Again, thanks to all of you for listening today. Those of you who want to follow Dr. Sharpless on Twitter, he's @NCIDirector. And you can always follow me @CliffordHudis, as well as ASCO @cancer. If you do that, you can stay connected to our work, of course, on social media. You can also go to the NCI's website, which is NCI.gov. With that, again, I want to thank Dr. Sharpless for joining me today. And thanks to all of you for tuning in.    

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Prof Perou talks to ecancer reporter Peter Goodwin about the Cancer Genome Atlas; working to target drugs and improve treatment by forming a comprehensive picture of breast cancer biology. They also discuss an overview of biomarkers, pathways and inhibitors, drugs in the pipeline for PI3 Kinase and looking at ER positive, HER2 negative cancers. Potential therapeutic advances in triple negative breast cancers are also covered.

Background: We analyzed prospectively whether MGMT (O(6)-methylguanine-DNA methyltransferase) mRNA expression gains prognostic/predictive impact independent of MGMT promoter methylation in malignant glioma patients undergoing radiotherapy with concomitant and adjuvant temozolomide or temozolomide alone. As DNA-methyltransferases (DNMTs) are the enzymes responsible for setting up and maintaining DNA methylation patterns in eukaryotic cells, we analyzed further, whether MGMT promoter methylation is associated with upregulation of DNMT expression. 12 Hide Figures Abstract Introduction Methods Results Discussion Acknowledgments Author Contributions References Reader Comments (0) Figures Abstract Background We analyzed prospectively whether MGMT (O6-methylguanine-DNA methyltransferase) mRNA expression gains prognostic/predictive impact independent of MGMT promoter methylation in malignant glioma patients undergoing radiotherapy with concomitant and adjuvant temozolomide or temozolomide alone. As DNA-methyltransferases (DNMTs) are the enzymes responsible for setting up and maintaining DNA methylation patterns in eukaryotic cells, we analyzed further, whether MGMT promoter methylation is associated with upregulation of DNMT expression. Methodology/Principal Findings: Adult patients with a histologically proven malignant astrocytoma (glioblastoma: N = 53, anaplastic astrocytoma: N = 10) were included. MGMT promoter methylation was determined by methylation-specific PCR (MSP) and sequencing analysis. Expression of MGMT and DNMTs mRNA were analysed by real-time qPCR. Prognostic factors were obtained from proportional hazards models. Correlation between MGMT mRNA expression and MGMT methylation status was validated using data from the Cancer Genome Atlas (TCGA) database (N = 229 glioblastomas). Low MGMT mRNA expression was strongly predictive for prolonged time to progression, treatment response, and length of survival in univariate and multivariate models (p

Guest: Stephen B. Baylin, MD Host: Leslie P. Lundt, MD The Cancer Genome Atlas (TCGA) has recently reported results from its first comprehensive study focusing on glioblastoma multiforme. How might this new research help us develop new therapies? Dr. Stephen Baylin, professor of oncology and medicine at the Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, joins host Dr. Leslie Lundt to explain how the TCGA may guide future cancer treatment.