Podcasts about Affymetrix

In this episode of Molecule to Market, you'll go inside the outsourcing space of the global drug development sector with Wayne Woodard, CEO at Argonaut Manufacturing Services. Your host, Raman Sehgal, discusses the pharmaceutical and biotechnology supply chain with Wayne, covering: How a career in the famously outsourced semiconductor industry benefited his route into the outsourced life science space The basic thesis that led to the start-up of Argonaut and the deliberate intention to ‘skate to where the puck will be' Wayne's aim (and achievement) of creating something special that would also help people and pay forward Key considerations and benefits of being located on the West Coast of the USA... including wine With 35 years of management experience in operations, supply chain and general management, Wayne Woodard's primary focus (in three different industries) has been on building global operations capabilities with particular emphasis on working with external manufacturing services companies. Before founding Argonaut, Wayne most recently worked at Thermo Fisher/Life Technologies through its Ion Torrent acquisition. He previously worked at Affymetrix, Electroglas and Ridge Technologies. Wayne began his career in manufacturing at Sun Microsystems. Wayne focuses intensely on execution and detail, which is why he also bottles his own wine. Please subscribe, tell your industry colleagues and join us in celebrating and promoting the value and importance of the global life science outsourcing space. We'd also appreciate a positive rating! Molecule to Market is sponsored and funded by ramarketing, an international marketing, design, digital and content agency helping companies differentiate, get noticed and grow in life sciences.

Today’s guest is Tracy Ting, CHRO of Encore Capital Group, a global specialty finance company with operations and investments across North America, Europe, Asia and Latin America. Tracy brings almost three decades of combined finance and HR expertise to her role, and is an award-winning CHRO who enhances shareholder value through strategic business growth and cultural transformation at multi-national companies. At Encore, Ms. Ting is responsible for people, culture, communications and ESG, providing strategic vision and enterprise leadership. Prior to Encore, Ms. Ting was the Chief People Officer at Avanir Pharmaceuticals and SVP & CHRO at Affymetrix. In this episode, you’ll hear how Tracy spearheaded the creation of a global set of values for Encore, written by employees, that distilled ten different sets of values down to just three. She shares the metrics by which all these values are measured, and explains how a company’s core values should hit the heart more than the head. Tracy talks about the challenges and successes Encore faces post-pandemic regarding the future of work. She addresses how they make ESG initiatives their standard operating procedure rather than just a "special project.” Leaders Playbook is a podcast hosted by Dr. Diane Hamilton and powered by the Global Mentor Network. We share stories about how to drive transformational impact in your organization. We talk with innovative thinkers across various industry sectors to hear about the best tools, resources, practices, and strategies to help you and your team reach the top of their game. Register for free on GMN.net to have access to our full library of content and resources on professional development. Discussion Points: Tracy Ting background Involving all voices in the co-creation process Distilling ten sets of company values down to three: We care, we find a better way, we are inclusive and collaborative Feeling values in your heart vs. head The impact on global employee engagement A constant search for incremental improvements Thinking through the future of work, and making ESG the new normal Metrics for measuring ESG and who wants to see them ESG has been a multi-year journey of incremental progress Five pillars of ESG: Consumers, people, operating responsibly, environmental, and community The biggest challenge in global collaboration is getting the right people at the table Advice- Tracy has a lot of it! Be open Navigate the new Don’t be afraid of mistakes “I don’t know what I don’t know.” Be strategic in your career progression, think two steps ahead, not one. Figure out your superpower and market it Never compromise your core values Resources/Links: Tracy Ting LinkedIn Encore Capital Global Mentor Network Website Global Mentor Network on Twitter Global Mentor Network on LinkedIn Dr. Diane Hamilton LinkedIn

Melinda Thomas, COO & Co-Founder of Octave Bioscience, shared the story behind her title with us on August 14, 2022.Melinda has over two decades of experience starting and leading health companies. Using her expertise, she wraps a company around a technical/scientific team so they can focus on meeting the company's milestones. Melinda built CardioDx, a molecular diagnostics company specializing in cardiovascular genomics, and ParAllele, high-throughput sequencing and SNP discovery company, from the foundation point. ParAllele was acquired by Affymetrix in what one investor said was their best investment of the decade. Previously, she served as the Inaugural Entrepreneur in Residence for New York City, solving problems for aspiring entrepreneurs, and as Chair of the Board for the Save the Redwoods League. Before ParAllele, she guided Molecular Dynamics Manufacturing, building it from an 8-person to an 85-person manufacturing organization. Melinda holds an MBA from Harvard and a BS from UC Berkeley.SUE SAYS"Melinda Thomas' life and career journey was one of saying yes to opportunities that presented themselves to her. From when she was seven and said sure, I'll help the neighbor build that cinderblock grease pit to saying yes to the multiple opportunities to launch new companies. It's always been a matter of following her gut and wanting to learn new things. Learn why she and her co-Founder decided to tackle the most complex neurological disease first when launching Octave."Support this podcast at — https://redcircle.com/women-to-watch-r/donationsAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Even though Brian McKelligon calls himself a rookie CEO, he comes to the top position at Akoya Biosciences with a veteran's resume. His path to one of spatial biology's hottest companies in 2022 worked him up the ranks of some of the top names in life science tools: Affymetrix, Ingenuity, Ion Torrent, and 10X Genomics. Last year Brian led Akoya through an IPO and this year the company has launched a new integrated product line called the Phenocycler-Fusion which they are calling the fastest single-cell spatial biology system on the market.

Alan Frazier, a pioneer in the healthcare venture industry with $7.1 billion in capital raised, discusses starting his firm in 1991 with a $5 million fund, turning down the role of CFO of Microsoft, going from a CPA to CFO of a fast-growing biotech startup, how his childhood prepared him for life as an investor, the importance of hiring people with the right values, traits of great entrepreneurs, how storytelling allows venture capitalists to help build companies, and reflections on his professional career and community involvement. Listen to the end to gain insight into spending more time with his foundation and giving back to the community in recent years, as well as his advice to find a group of mentors to support you on your journey. Top Three Takeaways: #1 Early in his career, Alan chose culture over money when he turned down an offer to become CFO of the 80-employee Microsoft. Although he had a feeling he might make a lot of money, he didn't think Microsoft was a good culture fit or that he would enjoy his life if he worked there. This is a great lesson in the importance of finding an organization that fits your values and sticking to that even when facing a tough decision. #2 In recent years, Alan has realized the importance of going beyond business and has gotten much more involved in giving back to his community through his foundation and nonprofit board roles. The things you do outside of work can bring a lot of joy, fulfillment, inspiration and energy in other parts of your life. In short, it's good for yourself, good for your career, and of course good for your community to step away from work and be involved in your community. #3 Alan stressed the importance of finding mentors who truly care for you to help guide you. This show is meant to help introduce the careers and advice of a couple of successful individuals, but it's only meant as a starting point. Find mentors who have been where you want to go, who know you, care for you, and can share their specific experiences and feedback to support you on your journey. Instagram: www.instagram.com/Voyager.Talks Twitter: https://twitter.com/ZevCarlyle LinkedIn: https://www.linkedin.com/in/zevcarlyle/ Podcast Home: https://bit.ly/VoyagerTalksHome Guest bio: Alan Frazier is the chairman of Frazier Healthcare Partners, a leading provider of growth and venture capital to healthcare companies, which he founded in 1991. With over $7.1 billion total capital raised, Frazier has invested in more than 200 companies, with investment types ranging from company creation and venture capital to publicly traded companies and buyouts of profitable lower-middle market companies. Since 2005, 61 Frazier Life Sciences portfolio companies, many of which were created or seeded by Frazier, have completed IPOs or M&As. Prior to forming Frazier Healthcare Ventures, Alan was the executive vice president and chief financial officer of Immunex Corporation. Later, he served as the senior financial advisor and chief financial officer of Affymax and worked on the spinout of Affymetrix. Prior to Immunex, Alan was head of the Emerging Business Practice and co-head of the Technology Practice for the Seattle office of Arthur Young Company (now, Ernst & Young). Alan holds a B.A. in Economics from the University of Washington. He has served on numerous boards, including Calypso Medical Technologies, Portola Pharmaceuticals, and TridentUSA Health Services. Alan is active in his community, having served on the boards of UW Medicine and Fred Hutch Cancer Research Center. Alan's continuing drive and passion come from his “love to create” and his “love to build.” --- Send in a voice message: https://anchor.fm/zev-carlyle/message

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. 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.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.  

Ron is a serial Biotech entrepreneur and the co-founder and Chief Science Officer at Wild Earth. Together with Ryan Bethencourt, Ron has funded over 70 startups that have redefined food, manufacturing, and consumer products through biotechnology. Ron did his doctoral training at Princeton University studying protein structure and biophysics. His post-doctoral training at Stanford and Harvard Medical School focused on antibiotic biosynthesis and diabetes. He is also a 15 year veteran of commercial biotechnology, having led a bioinformatics and functional genomics group at Affymetrix.

Dr. Colón brings over 25 years of experience in biopharma, genomics, healthcare and industrial biotechnology. In addition to her role at InCarda, she is Executive Chairman (formerly CEO) of ProterixBio, and serves on the boards of CareDx (NASDAQ:CDNA) and Cocoon Biotech and on the Advisory Board of the Miller Center for Social Entrepreneurship at Santa Clara University. Formerly, she was a partner at New Science Ventures, a New York based venture capital firm with over $700M under management, and served on the boards of Paradigm Diagnostics and PerceptiMed. Previously, she co-founded Pyranose Biotherapeutics, a biologics discovery platform company. She was also founding President of the Industrial Products Division at Intrexon Corporation, where she established a new division focused on leveraging synthetic biology for bioindustrial applications such as biofuels and renewable chemicals. Prior to Intrexon, she was head of Clinical Operations for Gilead Sciences, where she was responsible for global execution of clinical trials. She also created and led both the Alliance Management and Commercial Strategic Planning groups. Prior to Gilead, she was VP, Corporate Planning at Affymetrix, where she was responsible for strategic planning and project management and where she also served as COO for the International Genomics Consortium, a non-profit medical research organization focused on cancer genomics. Earlier in her career she was a consultant with McKinsey & Co., where she served clients in healthcare, biotech, high tech and venture capital. She was also an engineer with Merck & Co. in France and in Rahway, NJ. Dr. Colón received her Ph.D. in chemical engineering from the Massachusetts Institute of Technology, where she was an NSF Fellow. She also holds a B.S. degree in chemical engineering from the University of Pennsylvania, where she was a Benjamin Franklin Scholar.

Thane Kreiner is one of my heroes. As executive director of Miller Center for Social Entrepreneurship, Dr. Kreiner sets the vision and trajectory for programs that empower students and entrepreneurs to impact hundreds of millions of people around the globe. Prior to diving into leading the center, Dr. Kreiner was an entrepreneur himself, starting four biotech companies in the span of just three years. Before starting his own companies, he worked for 14 years at pioneering DNA-sequencing company Affymetrix. After doing his undergrad at U.T. Austin, Dr. Kreiner got both a Ph.D. in Neuroscience and an MBA from Stanford. No big deal. Thane has just written a new book called "Composition of Life: A Memoir of Science and Spirituality," which you can check out here. In this conversation, we dive into everything from deep sea creatures to formative startup stories, big life decisions, work-life balance and more. Read a shortened transcript article on my website. See acast.com/privacy for privacy and opt-out information.

Edwin Chau Senior Sales Manager Affymetrix eBioscience was exhibiting at the Festival of Genomics California and took the time to talk about their ground-breaking microarray solutions and other tools that are helping researchers increase their efficiency and get a better picture of disease.  @ebioscience #FestivalofGenomics ebioscience.affymetrix.com  

Go about anywhere in the life science industry, and you’ll run into someone who once worked at Affymetrix. Since the founding of Affymetrix and the development of what’s come to be known simply as the Affy chip, the company’s history has been intertwined with that of biotech and the genomics revolution. But what has become of the company today?

Background: Udder infections with environmental pathogens like Escherichia coli are a serious problem for the dairy industry. Reduction of incidence and severity of mastitis is desirable and mild priming of the immune system either through vaccination or with low doses of immune stimulants such as lipopolysaccharide LPS was previously found to dampen detrimental effects of a subsequent infection. Monocytes/macrophages are known to develop tolerance towards the endotoxin LPS (endotoxin tolerance, ET) as adaptation strategy to prevent exuberant inflammation. We have recently observed that infusion of 1 mu g of LPS into the quarter of an udder effectively protected for several days against an experimentally elicited mastitis. We have modelled this process in primary cultures of mammary epithelial cells (MEC) from the cow. MEC are by far the most abundant cells in the healthy udder coming into contact with invading pathogens and little is known about their role in establishing ET. Results: We primed primary MEC cultures for 12 h with LPS (100 ng/ml) and stimulated three cultures either 12 h or 42 h later with 10(7)/ml particles of heat inactivated E. coli bacteria for six hours. Priming-related alterations in the global transcriptome of those cells were quantified with Affymetrix microarrays. LPS priming alone caused differential expression of 40 genes and mediated significantly different response to a subsequent E. coli challenge of 226 genes. Expression of 38 genes was enhanced while that of 188 was decreased. Higher expressed were antimicrobial factors (beta-defensin LAP, SLPI), cell and tissue protecting factors (DAF, MUC1, TGM1, TGM3) as well as mediators of the sentinel function of MEC (CCL5, CXCL8). Dampened was the expression of potentially harmful pro-inflammatory master cytokines (IL1B, IL6, TNF-alpha) and immune effectors (NOS2, matrix metalloproteases). Functional network analysis highlighted the reduced expression of IL1B and of IRF7 as key to this modulation. Conclusion: LPS-primed MEC are fitter to repel pathogens and better protected against misguided attacks of the immune response. Attenuated is the exuberant expression of factors potentially promoting immunopathological processes. MEC therefore recapitulate many aspects of ET known so far from professional immune cells.

Background: A recent large-scale analysis of Gene Expression Omnibus (GEO) data found frequent evidence for spatial defects in a substantial fraction of Affymetrix microarrays in the GEO. Nevertheless, in contrast to quality assessment, artefact detection is not widely used in standard gene expression analysis pipelines. Furthermore, although approaches have been proposed to detect diverse types of spatial noise on arrays, the correction of these artefacts is mostly left to either summarization methods or the corresponding arrays are completely discarded. Results: We show that state-of-the-art robust summarization procedures are vulnerable to artefacts on arrays and cannot appropriately correct for these. To address this problem, we present a simple approach to detect artefacts with high recall and precision, which we further improve by taking into account the spatial layout of arrays. Finally, we propose two correction methods for these artefacts that either substitute values of defective probes using probeset information or filter corrupted probes. We show that our approach can identify and correct defective probe measurements appropriately and outperforms existing tools. Conclusions: While summarization is insufficient to correct for defective probes, this problem can be addressed in a straightforward way by the methods we present for identification and correction of defective probes. As these methods output CEL files with corrected probe values that serve as input to standard normalization and summarization procedures, they can be easily integrated into existing microarray analysis pipelines as an additional pre-processing step. An R package is freely available from http://www.bio.ifi.lmu.de/artefact-correction.

The PR interval on the electrocardiogram reflects atrial and atrioventricular nodal conduction time. The PR interval is heritable, provides important information about arrhythmia risk, and has been suggested to differ among human races. Genome-wide association (GWA) studies have identified common genetic determinants of the PR interval in individuals of European and Asian ancestry, but there is a general paucity of GWA studies in individuals of African ancestry. We performed GWA studies in African American individuals from four cohorts (n = 6,247) to identify genetic variants associated with PR interval duration. Genotyping was performed using the Affymetrix 6.0 microarray. Imputation was performed for 2.8 million single nucleotide polymorphisms (SNPs) using combined YRI and CEU HapMap phase II panels. We observed a strong signal (rs3922844) within the gene encoding the cardiac sodium channel (SCN5A) with genome-wide significant association (p < 2.5 > 10(-8)) in two of the four cohorts and in the meta-analysis. The signal explained 2% of PR interval variability in African Americans (beta = 5.1 msec per minor allele, 95% CI = 4.1-6.1, p = 3 x 10(-23)). This SNP was also associated with PR interval (beta = 2.4 msec per minor allele, 95% CI = 1.8-3.0, p = 3 x 10(-16)) in individuals of European ancestry (n = 14,042), but with a smaller effect size (p for heterogeneity < 0.001) and variability explained (0.5%). Further meta-analysis of the four cohorts identified genome-wide significant associations with SNPs in SCN10A (rs6798015), MEIS1 (rs10865355), and TBX5 (rs7312625) that were highly correlated with SNPs identified in European and Asian GWA studies. African ancestry was associated with increased PR duration (13.3 msec, p = 0.009) in one but not the other three cohorts. Our findings demonstrate the relevance of common variants to African Americans at four loci previously associated with PR interval in European and Asian samples and identify an association signal at one of these loci that is more strongly associated with PR interval in African Americans than in Europeans.

Introduction: Current prognostic gene expression profiles for breast cancer mainly reflect proliferation status and are most useful in ER-positive cancers. Triple negative breast cancers (TNBC) are clinically heterogeneous and prognostic markers and biology-based therapies are needed to better treat this disease. Methods: We assembled Affymetrix gene expression data for 579 TNBC and performed unsupervised analysis to define metagenes that distinguish molecular subsets within TNBC. We used n = 394 cases for discovery and n = 185 cases for validation. Sixteen metagenes emerged that identified basal-like, apocrine and claudin-low molecular subtypes, or reflected various non-neoplastic cell populations, including immune cells, blood, adipocytes, stroma, angiogenesis and inflammation within the cancer. The expressions of these metagenes were correlated with survival and multivariate analysis was performed, including routine clinical and pathological variables. Results: Seventy-three percent of TNBC displayed basal-like molecular subtype that correlated with high histological grade and younger age. Survival of basal-like TNBC was not different from non basal-like TNBC. High expression of immune cell metagenes was associated with good and high expression of inflammation and angiogenesis-related metagenes were associated with poor prognosis. A ratio of high B-cell and low IL-8 metagenes identified 32% of TNBC with good prognosis (hazard ratio (HR) 0.37, 95% CI 0.22 to 0.61; P < 0.001) and was the only significant predictor in multivariate analysis including routine clinicopathological variables. Conclusions: We describe a ratio of high B-cell presence and low IL-8 activity as a powerful new prognostic marker for TNBC. Inhibition of the IL-8 pathway also represents an attractive novel therapeutic target for this disease.

Background: Due to its strong water binding potential, hyaluronic acid (HA) is a well-known active ingredient for cosmetic applications. However, based on its varying molecular size, skin penetration of HA may be limited. Recent studies have demonstrated that low-molecular-weight HA (LMW HA) may show a certain proinflammatory activity. We thus aimed to characterize an LMW-sized HA molecule that combines strong anti-aging abilities with efficient skin penetration but lacks potential proinflammatory effects. Methods: Total RNA and total protein were isolated from reconstituted human epidermis following incubation with HAs of various molecular weights (20, 50, 130, 300, 800 and 1,500 kDa). Tumor necrosis factor-alpha expression was determined using quantitative PCR. Genonnic and proteomic expression of various junctional proteins was determined using Affymetrix and common Western blotting techniques. Results: LMW HA of approximately 50 kDa did not significantly alter tumor necrosis factor-alpha expression compared to 20-kDa HA, but revealed significantly higher skin penetration rates than larger sized HA associated with increased expression of genes and proteins known to be involved in tight junction formation and keratinocyte cohesion. Conclusion: LMW HA of approximately 50 kDa shows better penetration abilities than larger-sized HA. In addition, LMW HA influences the expression of various genes including those contributing to keratinocyte differentiation and formation of intercellular tight junction complexes without showing proinflammatory activity. These observations contribute to current knowledge on the effects of LMW HA on keratinocyte biology and cutaneous physiology. Copyright (C) 2011 S. Karger AG, Basel

Background: Chromatin immunoprecipitation combined with DNA microarrays (ChIP-chip) is an assay used for investigating DNA-protein-binding or post-translational chromatin/histone modifications. As with all high-throughput technologies, it requires thorough bioinformatic processing of the data for which there is no standard yet. The primary goal is to reliably identify and localize genomic regions that bind a specific protein. Further investigation compares binding profiles of functionally related proteins, or binding profiles of the same proteins in different genetic backgrounds or experimental conditions. Ultimately, the goal is to gain a mechanistic understanding of the effects of DNA binding events on gene expression. Results: We present a free, open-source R/Bioconductor package Starr that facilitates comparative analysis of ChIP-chip data across experiments and across different microarray platforms. The package provides functions for data import, quality assessment, data visualization and exploration. Starr includes high-level analysis tools such as the alignment of ChIP signals along annotated features, correlation analysis of ChIP signals with complementary genomic data, peak-finding and comparative display of multiple clusters of binding profiles. It uses standard Bioconductor classes for maximum compatibility with other software. Moreover, Starr automatically updates microarray probe annotation files by a highly efficient remapping of microarray probe sequences to an arbitrary genome. Conclusion: Starr is an R package that covers the complete ChIP-chip workflow from data processing to binding pattern detection. It focuses on the high-level data analysis, e. g., it provides methods for the integration and combined statistical analysis of binding profiles and complementary functional genomics data. Starr enables systematic assessment of binding behaviour for groups of genes that are alingned along arbitrary genomic features.

Alternative mRNA processing mechanisms lead to multiple transcripts (i.e. splice isoforms) of a given gene which may have distinct biological functions. Microarrays like Affymetrix GeneChips measure mRNA expression of genes using sets of nucleotide probes. Until recently probe sets were not designed for transcript specificity. Nevertheless, the re-analysis of established microarray data using newly defined transcript-specific probe sets may provide information about expression levels of specific transcripts. In the present study alignment of probe sequences of the Affymetrix microarray HG-U133A with Ensembl transcript sequences was performed to define transcript-specific probe sets. Out of a total of 247,965 perfect match probes, 95,008 were designated “transcript-specific”, i.e. showing complete sequence alignment, no cross-hybridization, and transcript-, not only gene-specificity. These probes were grouped into 7,941 transcript-specific probe sets and 15,619 gene-specific probe sets, respectively. The former were used to differentiate 445 alternative transcripts of 215 genes. For selected transcripts, predicted by this analysis to be differentially expressed in the human kidney, confirmatory real-time RT-PCR experiments were performed. First, the expression of two specific transcripts of the genes PPM1A (PP2CA_HUMAN and P35813) and PLG (PLMN_HUMAN and Q5TEH5) in human kidneys was determined by the transcript-specific array analysis and confirmed by real-time RT-PCR. Secondly, disease-specific differential expression of single transcripts of PLG and ABCA1 (ABCA1_HUMAN and Q5VYS0_HUMAN) was computed from the available array data sets and confirmed by transcript-specific real-time RT-PCR. Transcript-specific analysis of microarray experiments can be employed to study gene-regulation on the transcript level using conventional microarray data. In this study, predictions based on sufficient probe set size and fold-change are confirmed by independent means.

Diabetic nephropathy (DN) is a complex and chronic metabolic disease that evolves into a progressive fibrosing renal disorder. Effective transcriptomic profiling of slowly evolving disease processes such as DN can be problematic. The changes that occur are often subtle and can escape detection by conventional oligonucleotide DNA array analyses. We examined microdissected human renal tissue with or without DN using Affymetrix oligonucleotide microarrays (HG-U133A) by standard Robust Multi-array Analysis (RMA). Subsequent gene ontology analysis by Database for Annotation, Visualization and Integrated Discovery (DAVID) showed limited detection of biological processes previously identified as central mechanisms in the development of DN (e.g. inflammation and angiogenesis). This apparent lack of sensitivity may be associated with the gene-oriented averaging of oligonucleotide probe signals, as this includes signals from cross-hybridizing probes and gene annotation that is based on out of date genomic data. We then examined the same CEL file data using a different methodology to determine how well it could correlate transcriptomic data with observed biology. ChipInspector (CI) is based on single probe analysis and de novo gene annotation that bypasses probe set definitions. Both methods, RMA and CI, used at default settings yielded comparable numbers of differentially regulated genes. However, when verified by RT-PCR, the single probe based analysis demonstrated reduced background noise with enhanced sensitivity and fewer false positives. Using a single probe based analysis approach with de novo gene annotation allowed an improved representation of the biological processes linked to the development and progression of DN. The improved analysis was exemplified by the detection of Wnt signaling pathway activation in DN, a process not previously reported to be involved in this disease.

The t(10;11)(p13;q14) is a recurring translocation resulting in the fusion of the CALM and AF10 genes. The leukemogenic CALM/AF10 fusion genes codes for a 1595 amino acids protein. This translocation was first identified in a patient with hystiocytic lymphoma and was subsequently found in patients with AML, T-ALL and malignant lymphoma. This translocation is found in younger patients and is associated with a poor prognosis. The CALM/AF10-associated leukemias can exhibit myeloid, lymphoid or mixed lymphoid-meyloid features, indicating a stem cell or an early commited progenitor as the target cell of leukemic transformation. At the present time the target cells in CALM/AF10-associated leukemogenesis are unknown. It is also not known which target genes are up or downregulated by the presence of the CALM/AF10 fusion protein. To answer these questions, the following experiments were performed: 1) Five transgenic mouse lines, two expressing CALM/AF10 under the control of the immunoglobulin heavy chain enhancer promoter and three under the control of the murine proximal Lck promoter were generated. Although the CALM/AF10 expression was confirmed to be present and specific to the cells targeted by the promoters used (B- and T-cell progenitors for IgH and Lck promoters, respectively), the transgenic animals did not show a phenotype that could be detected after meticulous clinical, haematological, immunological, flow cytometrical and immunohistopatological analysis . 2) We performed molecular characterization of several CALM/AF10 patient samples: A group of 13 patients with different types of leukemia: case 1 (AML M2), case 2 (Acute Biphetnotypic leukemia), case 3 (Pre T-ALL), case 4 (Acute Undifferentiated Leukemia), case 5 (PreT-ALL), cases 6 and 7 (ProT-ALL), case 8 (T-ALL), case 9 (AML), case 14 (T-ALL), case 15, 16 and 17 (AML) with a t(10;11) translocation detected by cytogenetic analysis suggesting a CALM/AF10-rearrangement. The samples were analyzed for the presence of the CALM/AF10 and AF10/CALM fusion transcripts by RT-PCR and sequence analysis. All these patients were found to be positive for the CALM/AF10 fusion. In addition, we analyzed a series of twenty-nine patients with T-ALL with T-cell receptor ≥¥ rearrangement. Among these patients, four (case 10 to 13) were positive for the CALM/AF10 fusion transcript, indicating a high incidence of CALM/AF10 fusions in this group of leukemia. Three different breakpoints in CALM at nucleotide 1926, 2091 and a new exon, with 106 bases inserted after nt 2064 of CALM in patient 4 were found. In AF10 four breakpoints were identified: at nucleotide position 424, 589, 883 and 979. In patient 16 we found an extra exon before nt 424 of AF10. In seven patients it was also possible to amplify the reciprocal AF10/CALM fusion transcript (case 1, 3, 4, 8, 9, 10 and 14). There was no correlation between disease phenotype and breakpoint location. Ten CALM/AF10 positive patients were analyzed using oligonucleotide microarrays representing 33,000 different genes (U133 set, Affymetrix). Analysis of microarray gene expression signatures of these patients revealed high expression levels of the polycomb group gene BMI1, the homeobox gene MEIS1 and the HOXA cluster genes HOXA1, HOXA4, HOXA5, HOXA7, HOXA9, and HOXA10. The overexpression of HOX genes seen in these CALM/AF10 positive leukemias is reminiscent to the pattern seen in leukemias with rearrangements of the MLL gene, normal karyotypes and complex aberrant karyotypes suggesting a common effector pathway (i.e. HOX gene deregulation) for these diverse leukemias. In addition, the general pattern of gene expression of CALM/AF10 patients when compared to other leukemia subtypes and to normal bone marrow was dominated by a global downregulation of genes some of them with function identified as related to important molecular mechanisms, such as membrane trafficking, cell growth regulation, proliferation, differentiation and tumor suppression. 3) We cloned CALM/AF10 fusion gene into a vector that allowed us to induce the expression of CALM/AF10 using doxycycline in transiently and stably-transfected NIH3T3 and HEK293 cells. This system will be an important tool to identify direct CALM/AF10 target genes and to answer the question whether continued CALM/AF10 expression is necessary to maintain the CALM/AF10-associated expression pattern.