On today’s podcast, Cofactor Genomics COO, Dr. David Messina discusses how his company is working to make personalized medicine a reality by focusing on the potential of RNA as a diagnostic tool.
Unlike DNA, which is set in stone at birth, our RNA changes on a minute-by-minute basis and acts as a barometer of our health.
Led by 3 former researchers from The Human Genome Project, Cofactor Genomics is developing a patent-pending RNA-based technology that will allow medical researchers to detect specific RNA molecular markers in even small, low-quality tissue samples. This will make 100 times more patient specimens available for analysis and will open the door to massive, Big Data-style databases for use in both drug and treatment development.
Carter Williams: So, David, the way we normally do this, is we sort of walk through background.
David Messina: Great.
CW: Sort of just, you know … Cofactor is doing some really exciting things and so we’ll spend some time talking about that, but I also love technology and try and understand what’s happening next, I think. In iSelect, we have this opportunity to see cool emerging technology, and when I tell other people, that’s … No, idea, what’s … And, so it’s normal stuff for us –
DM: Right, but, not everybody knows what’s going on.
CW: Not everyone knows what’s going on and it sort of gives them some excitement, so … But, I’m really intrigued about you’re now at Cofactor, right? Part of the founding team. And, I’m sort of intrigued about how you got here, so, how did you become a computational biologist?
DM: You know, I never expected to be, actually, I … When I went into college, I was thinking I would do international law, or maybe political science, and after trying to do that for a couple of years, I realized it wasn’t for me, and I switched gears completely.
And, I was really looking for something 180 degrees different. And I ended up thinking about biology, and I had the opportunity to work with a very talented guy at Argon National Laboratory, a national laboratory just outside of Chicago, who is a mathematician and a computer scientist, originally, and he had met Carl Woese who, kind of one of the pioneers of computational biology.
In 1977, he, Carl Woese, had discovered, or postulated, that there is an entire third domain of life called the Archaea. And, so, you have to think about eukaryotes being multicellular organisms, prokaryotes being bacteria, and he was saying that there’s actually a whole third domain that’s kind of similar, some parts that are multicellular, some parts that are like single-celled organisms.
He predicted this using … looking at RNA molecules, the very early sequencing, that was available at that time. And he looked at enough of them to be able to figure out that there was, in fact, this whole third branch of life. Nobody believed him for a long time. And, it turned out to be true. And, now we know this. This is accepted many years later.
So, Ross Overbeek met Carl and became enamored of applying computational techniques to biology. So, when I met Ross, this was just after a team had published the first free-living … The genome of the first … The first genome sequence of a free-living organism. So, Methanococcus jannaschii, this was done by Craig Venter’s group.
CW: What year was this?
DM: This would have been 1996.
DM: And, so, Ross was a unique individual, in that, even at that time, this is when we have one genome … He started thinking about, and we talked about, “Well, what do you do when you have ten genomes, or a hundred genomes, or a thousand? What can you do, in terms of comparative genomics, to better understand the world? So, in a nutshell, you can think about it, where, once you have the whole parts list, that’s what a genome is, really, is how to make an organism. And, once you know what all the genes are, then you can look at two organisms, or two groups of organisms, and say, “The parts that are the same are likely to be fundamental to life.” You know? If they’re all occurring in lots of different organisms. And, the parts that are different, are maybe unique to an organism, explains what is unique about that, or some characteristic about that organism.
So, being able to think about genomics, before genomics had even really started, that was one of those times in life where, I had clarity that this was going to happen and be incredibly interesting and influential area to be in. And, so I had to get into it, and so, that’s really what got me started in genomics.
CW: And so, working with somebody like that that can see the future. Are they just wicked smart and they just can’t explain how they got there? Or, are they clairvoyant, or what would you … What did you see that being present in that phase, what can you tell us about a person like that?
DM: I think it’s a hard question to answer. I think that assembling the information that you have, and thinking logically about the consequences of, and the implications, of that information, can lead to startling insight. You can see things that other people can’t readily see, and so, my assumption is, for somebody like that it takes a certain personality, for sure. But I think it also takes somebody who is open to thinking in those kinds of expansive ways, and really being open to unexpected or startling conclusions.
CW: Challenge the conventionalism.
CW: But I understand the basis really well, and then challenge the conventional end of it.
DM: Yeah, and coming up with hypotheses, and thinking, “Well, okay. Can I test that? Can I … Is that really going to be true? Or, is that true today? Can I test that assumption based on what I know now?”
CW: And is that what computational biology lets you do more of?
DM: I think that’s, absolutely, one of the things that I find so exciting about it. So, what is computational biology? Really, it’s being able to apply computer science, computational techniques, to understand biology. And, that was something that was not really possible in a high throughput or a large-scale way, until very recently. Like I said, the first genome, 20 years ago, and so it’s a very new field.
CW: Yeah, and when the gene … Nobel Prize … For the first Nobel Prize for the gene was, like, in the ’70’s?
DM: Well, so, the techniques that were for mapping and splicing genes, I think, right? Were in the ’70’s. Certainly DNA discovered in 1953, so certainly genetics stretches back, depending on how you count it, back to the beginning of the 20th Century, but really, being able to read the DNA code and by extension RNA, and how genes are expressed inside a cell has come very recently, and so, one gene at a time was really how genetics was conducted until high throughput sequencing became available. Which, really was done on a massive scale, for the first time, during the Human Genome Project. Actually there was a small worm that was sequenced before that. Right? So, so in those days-
CW: What was that worm called, again? I can’t recall.
DM: C. elegans. So, and that was done here at Washington University, also, so in fact, that was, in a sense, to Bob Waterston, one of the architects of the Human Genome Project here at Washington University … There’s one of two places in the world that really led that project. He was a worm biologist, and so, he, I think, was excited about applying the technology first to a worm, and then really seeing where we could take that. In those days, this was still fairly high technology. You were using lasers to read the DNA, but it was very low throughput still, you know, you got a large bench-top machine, you would have what is a very fine, high-quality jello, essentially, that was pressed in between two glass plates. And you have it set up with an anode on one end and a cathode on the other end, so basically like a battery.
So, DNA is negatively charged. And so, if you inject DNA that has been slightly chemically modified, on one end, you can flow it through this gel, this high-quality gel, and then you can have … It separates that out by size, and then you can have a laser hit it and read off each letter of DNA.
And so this was done, we could do in one single run of one of these old machines, maybe 60 – 70 thousand letters of DNA we could read at a time.
And so the Human Genome Project. The human genome is about 3.3 billion letters of DNA, so you can imagine how many gels … How many of those old fashioned sequencing runs were necessary to do it. That’s kind of the story of the project, that it took many thousands of those, over many hundreds of people, over tens of years. And, all the engineers and computational people to analyze the data to assemble that back into the chromosomes, to the-
CW: And so we’re talking, so, just to keep stepping through, so people understand how you got here, so you go into law … Decided not to go into law, in college. University of Illinois, you … As we’re sort of approaching into the ’98 time frame, front edge of the Human Genome Project, you came down for your masters in Computational Biology.
DM: That’s right, so right, it was here in Washington University in St. Louis, so, and actually there’s a little step in between. I was trying to do human genetics, or was doing human genetics at the University of Chicago before the Human Genome was finished. So, in those days, we were … We had a … We were looking at an inherited form of Muscular Dystrophy, so, we had cutting edge technology, we had three generations of this family, and at that time, we were excited. It was a good result to narrow down the location of the causative gene for that disease to about a million letter chunk of the human genome.
So about dozens of genes were in there, and that was-
CW: A needle in the haystack.
DM: Yeah, and that was state-of-the-art in 1997. So, I was really interested in genomics. I looked around for where would be the best place to study that, and out of the 15 or so people in the world who were computational biologists at that time, nine of them were here at Washington University. And, so, pretty easy choice to come here and study with them, and take part in the Human Genome Project.
CW: So, I think something forgotten about the Human Genome Project, I recall I was here in Aerospace at the time, driving around the car, and constantly hearing about, both what was going on here in Cambridge, in terms of human genome. In terms of the core concept, what was going on with human genome projects? What was sort of the main charter?
DM: Yeah, the idea is that if we could determine the parts list, the whole set of genes, and really, the structure, the genome structure itself, that’s the blueprint of life for what makes a human. And, by being able to have that knowledge, we could then start to understand how humans are different and alike from other species. So, understand the function of each and every gene. So, remember, up until that point you’d study a gene at a time, or a small collections of the gene at once. So, if you don’t have the complete set of genes … if you don’t know what all of them are, it’s very hard to know, well, is this … How do the function of different genes overlap? If you … How they interrelate? Whether the pathways where these … the chemical pathways where these genes interact, and so having that information allows us to not only understand the fundamentals of human biology, but also allows us to think about things like eradicating disease and how the human body interacts with disease. And, we’re just getting to see the fruits of that, today. 20 years later.
CW: Today. So, human genome … So, I always imagined the human genome is … Sequencing the human genome is on scale with landing on the Moon.
DM: Yeah, it’s a massive, massive enterprise, and a huge-
CW: I think with a billion dollar underwriting for-
DM: At least a billion dollars, right. And, when I talked about the old technology that was used actually to accomplish the human genome, they knew at the beginning they would have to create new technology, new higher throughput technology to finish that on schedule, and indeed they did.
CW: And you brought up Craig Venter’s name. I recall there was a little bit of … Big government program funded, good. There was an effort between here and the Broad Institute?
David Messina: That’s right.
CW: To go ahead with it. And then Craig Venter said, “Oh, I can do this faster, quicker, cheaper.” So, what was the insight on that?
DM: So, I think the other piece of that was they saw an opportunity to do two things. So, 1) to use all the data, the public, publicly funded, right? So, public data that the Human Genome Project had generated. And, 2) to generate some of their own data and combine those two using a new computational technique for assembling the jigsaw puzzle into a full genome. And, by being able to do that on a private basis, I believe, although, I’m not sure, that their intention was to do private research there at a non-profit institute that would then also identify areas for a commercial enterprise, to be able to build off of that work, so both parties, and what was portrayed as a race to publication for the … between Craig Venter’s group and the public group, both parties were … actually have contributed to our ability to do this stuff today, through the work that the public. Really, the fundamental work that the public side did, and then … And the computational techniques that were introduced by the Venter team are still in use today for assembling genomes, those concepts.
CW: So, we’re sort of in the ’98, the 2000 timeframe. You continued on in the Human Genome Project for a while?
DM: Right. So, I worked as a staff scientist at the Genome Institute, now the McDonnell Genome Institute, here in St. Louis, and there were other genomes to sequence, so we did lots of other organisms, mammals in particular, I worked on the chimpanzee genome and things like this, and we continued to refine the techniques, and it took a really interesting turn in about the mid-2000’s when a new breed of sequencing machines started to appear. Which were much higher throughput than the ones I described earlier. Which allowed us to do a lot more sequencing in a lot less time. And, so, we talk about Moore’s Law with computers, right? Where this idea that every 18 months or so, the amount of power, or really, transistor density that you have per unit doubles. So, you get about twice as much compute speed every 18 months according to Moore’s Law.
So, with these new types of sequencers, these next-gen or massively parallel sequencing machines, the increase in sequencing output per cost over the last ten years, or so, since then, has been faster than Moore’s Law, so actually accelerating faster than what we’ve seen in computers. And we all know how dramatic the difference is in computational power over the last 10 years.
So, think about how impactful that change in just raw sequencing output and capability, what an impact that has in our ability to generate the raw data that we can then analyze to understand not only human biology but actually biology in general.
CW: And so that … We’re going to get to Cofactor, here, in a moment. How’d you go from there to Cofactor?
DM: Right, so like all scientists-
CW: On two levels, from: You’re a scientist in a lab, and Cofactor’s an entrepreneurial operation.
DM: Sure. So-
CW: And then 2) What was the inspiration or technology that sort of led to the point that you said, “Hey, here’s the time to go create a company.”
DM: Sure. So, as a scientist who started in academia, like most of them do-
DM: Right. Law, and then academia.
CW: I can understand why you went away from law, but –
DM: So, academic science, basic research, the goal is the creation of new knowledge. It’s a very important thing. However, we have been able, I think, to accumulate new knowledge much faster than we had been able to apply that new knowledge. And, to me, this is something that always bothered me. I would … We’d have all these great discoveries but until we can really see a tangible impact from them, you know, they’re not reaching their full potential. And, so, I was really motivated to apply a lot of the amazing research that was going on, and turn that into something, commercialize that into something that would have a real impact on people.
CW: Why do you think that is? There are lots of researchers that go their entire career never having that angst.
DM: I think it’s in part because I think there are plenty of people, and there is already lots of new knowledge being created, so there’s plenty of that already happening, you know, and working on the applied side to trickle more of that through to the end user, if you will, made sense. I think also, from my personality, seeing the tangible outcome or benefit meant more to me than the abstract creation of knowledge.
CW: So, you saw that opportunity to take the next step.
DM: And then for Cofactor, right, so, working at the Genome Institute. Remember there was an army of 300 people, and that’s just with the team in St. Louis, at it’s peak, around that many people working on the human genome. And, really cranking out that sequence. When this new breed of sequencers came out that were much faster in generating a lot more data, we saw the potential of these, and so did everybody else, and an institute like the one we were at was really built around large Federally-funded grants, projects like those, on a large scale. They weren’t set up to meet the needs of a commercial user.
And so when, not only other academic labs started knocking on the door, wanting to take advantage of this new high throughput sequencing technology, but also companies like Pfizer or other PhRMA companies wanted to use it. The Genome Institute was not set up to service those needs, and so that’s a business opportunity.
And so that’s really how Cofactor started, was, you know, we realized we could offer what the Genome Institute was offering, as a commercial enterprise. And, so that’s how the company was born.
Now, fast-forward to today, and we’re really taking what we’ve learned over the course of having worked with some of the largest pharmaceutical companies in the world, over the last several years, in helping them perform their research, we’ve realized that there’s a great opportunity to take what we’ve learned and turn it into more clinical applications and more clinical focus.
CW: So, before we dive into that, the day you left WashU, was it you? You and Jarret? What, I mean, was there a moment of utter and total fear, or was it clear? Or, what was the entrepreneurial emotion going through your mind at that point?
DM: Sure, I remember talking to Jarret about this, because Jarret was really the driving force there, and he had-
CW: Can you describe who Jerret is?
DM: Sure, so Jerret Glasscock, our CEO and Founder, he was faculty. Backstory is that he and I met in 1998. We both came to WashU to study computational biology for the same reason, because it was the place to do it. And, soon after that, we met at the Institute. Our third co-founder, John Armstrong, who’s a brilliant molecular biologist, and leads our research team.
So, I remember talking to Jerret about that moment when he decided to jump ship and he, I think, had that same kind of moment of clarity, where he saw that this was going to work. That this was a good idea. And, so, the risk of doing it didn’t seem like that much of a risk to him because he knew that it would work.
CW: It was riskier not to do it.
DM: Right. That, and I think also, because genomics at that time, and still today, and computational biology, is such a growing field and there’s so much work to be done that even if it didn’t work out, we’d be okay. We’d be able to continue on and find something else to do.
CW: And so you started initially, in a business, supporting PhRMA for something they needed right then.
DM: Right. Exactly.
CW: They needed the best guys that knew the best technology that could operate on a commercial basis, and so-
DM: Yep. And, there weren’t that many people doing it at that time.
CW: So, did you get a PO right away?
DM: Right, so, I think when the company opened its doors-
CW: Did you even know what to write on the PO? Other than the dollar number at the bottom?
DM: Yeah. I think when the company opened its doors, we were lucky to have several months worth of orders already stacked up, so we were cash-flow positive from the beginning. We really didn’t take on any external investment at the beginning.
CW: And at that point it was a bit more of a services business, but at a high bill rate.
CW: Higher than a lawyer would charge?
DM: I don’t know if we made it quite that far. That hourly rate is still pretty high, but yes, exactly. We had the expertise both on the molecular side, so taking the genetic material, turning it into a form that the sequencer could read, generating the data, and then doing the computational analysis to make it meaningful. So, we had all those components.
CW: And so, you did that for several years with Cofactor, and you reached a point where you saw an opportunity to go to the next level.
DM: Right so it happened in a couple of phases. So, at the beginning, it was all about DNA sequencing and then a little about RNA sequencing, so, perhaps it’s worth a sidebar on the differences there?
DM: So, DNA, we’ve been talking about this, the instruction book for an organism, for a human being if we’re talking about the human genome. And, DNA doesn’t change, really, over the course of your life. The DNA that you have when you’re born is pretty much the same as the DNA that you have today. And, so when we think about disease, what DNA tells you is really gives you an estimate of the chance, the likelihood, that maybe you’ll develop a particular disease or not. For some diseases it’s much more certain, based on your DNA, that you know what’s going to happen. For most diseases, it’s really just more of an estimate, or a probability, that you’ll develop a disease. So, interesting and useful information, yes. But it’s really just giving you a probability or an estimate.
RNA, on the other hand, is not the same as when you were born. RNA is really all of the genes in each cell in your body turned on or off at a given time. And, so RNA can tell you exactly what’s going on at a particular moment. And it can tell you right now whether your body is developing disease, or is … Or, right now whether it’s responding to a treatment. And, so that power, that ability, to see what’s going on inside a cell today, not just an estimate but actually what’s happening is very powerful. And, as we started to do more and more RNA sequencing at Cofactor and working with … We became experts in that and worked with pharma companies around their programs to understand RNA and their clinical programs, we saw an opportunity to really take RNA out of the discovery side, the research side, and into the clinical side, into something that would be even more applied, that would make even more of an impact on people.
CW: Just put some context in the sequencing process, what 23andME now does. You see a TV show and all of a sudden you see these cool new discoveries like 23andME. If you tried to do that same analysis in ’96 or ’98, how long would that have taken?
DM: Oh, right. So, I think 23andME looks at about a million individual locations in out of the 3 billion letters of your DNA, it looks at about a million of them, and they do it on a very specialized chip. Almost like a computer chip where they can put your sample on it and get a read-out on that. 20 years ago, if you were doing it using the old sequencing technology, it would have taken probably at least a year to do that for one person. Actually, the studies that they did and University of Chicago was doing that kind of what we call genotyping, determining somebody’s genetic code, or DNA, at particular spots in the genome. I did … I ran a gel a day, one of these old sequencing gels, a day for a year to get enough information for the people in that small study.
CW: So, you decided to go into a clinical application? And, so tell us what that is.
DM: Right. So, because RNA gives a real time readout of what’s going on inside your body, we realized that there are several opportunities to apply that to medicine, particularly as a diagnostic. And, so, can we help drug developers understand which patients are likely to respond to their drug? Can we help doctors understand which treatment is going to be effective for their patient? Those kinds of questions would have a tremendous impact on our ability to practice medicine today. And, indeed, that’s exactly what we decided to do, and that’s what our products are built around, is helping both drug developers and doctors understand what’s going on in each individual patient, on a personalized or precision basis, rather than treating every patient as the average patient. Which, is how, really, medicine is practiced today. When somebody is treated with a drug, it’s because that’s what works for most people most of the time. And what we’d like to do is help clinicians move to a situation where based on what’s going on in that particular patient’s body that we can see with RNA, they’ll be able to know what is likely to be effective for that patient with that particular disease.
CW: So, they could pick the right drug or treatment to give them? Decide whether they should give them a little or a lot.
DM: That’s right. Exactly. So, you can imagine that today when you’re treating people with what works most of the time for most people, these treatments are in some cases very expensive, and so you put a patient on the drug, you wait a few weeks or a month to see whether it’s working, you’ve not only spent a lot of money, but if it doesn’t work then you’re switching them to another drug, and maybe even have to go to a third drug. And, so, it’s a very expensive and wasteful process, but more often it takes time. Precious time that some of these patients may not have to give, to find the right treatment.
CW: And do you have any sense … It’s a broad question, but if you think through the conventional means of giving somebody a drug and it not working and then the disappointment or whatever the consequence of that, you know, that … whatever, versus the opportunity of being able to tailor these drugs, do you have a sense of what kind of impact that would have?
DM: We think that, well, if you think about how many people come down with cancer every year, so, current estimates are that between the US, UK, and Canada probably 50% of the population will at one time or another have cancer. And, 25% of those will succumb to that cancer. We’re talking about millions of people every year, and as we develop better medications, and as we develop approaches like Cofactor’s to better choose those medications, I would imagine the impact would be enormous. Certainly billions of dollars and countless lives that could be impacted by being able to be more precise about the treatment process.
CW: And, in terms of the treatment level, can you give us an example of where this type of technology has been explicitly used and changed an outcome?
DM: So, one of these motivating stories for us actually happened right here in St. Louis. So, there’s a leukemia researcher at Washington University named Lukas Wartman, and he tragically came down with the very leukemia that he was studying. Being at a world class center for medicine and genomic research, his physicians were able to apply all of the latest techniques to try to find a treatment for him, including DNA sequencing. They sequenced his genome and that was not able to find anything helpful for his treatment. It was only when they went to his RNA that they were able to see that there was a particular gene, which was being turned on way too high, it was over-expressed, that they found an opportunity. There’s actually an already approved FDA approved drug for correcting the over-expression of that gene. They gave Lukas Wartman this drug, and very quickly he responded to it. And, the course of his disease reversed drastically.
And so that was information that, from his RNA, that was able to change the course of his treatment. He was in, probably, the best possible place for being able to have that outcome, at a place where genomic medicine is at the cutting edge here. We want to make sure that everybody has that same opportunity that Lukas Wartman had, to be able to use RNA as a diagnostic tool.
CW: So, it becomes as easy as 23andME?
DM: That’s right.
CW: And at any … at your corner urgent care clinic.
DM: That’s right, and this is … we’re at the beginning of the path, but this is something that we believe will be possible at the very near future.
CW: And, so, what is the future? You’ve got Cofactor. We’re an investor in Cofactor. We’re intrigued with it for all the reasons you have mentioned. You’ve got products coming down that are both clinical products and then a product that helps in the research phase. As you look at your next generation products down the road, or what you’re going to be doing down the road, what will be standard of care using this type of technology five, ten years from now?
DM: Right, so I think it’s an expansion of the same approaches that we’re taking today. So, today the products we have, we have Pinnacle and Paragon. So, Pinnacle is focused for the clinician, for the cancer doctor who is trying to choose the right treatment for the patient. And, so we’re giving them information about that patient’s RNA to do that. And, then we have Paragon, which is giving insight into how the immune system and the tumor are interacting.
And that’s really interesting. I’d love to talk about that some more. So, both of those are right now focused on cancer, particular types of cancer. But, we’ve really built a technology platform that can be applied to many more types of diseases, and indeed, we like to think of it as all of the diseases, which is most of them, that that estimate-
CW: Meaning cancer, neurological, other systems.
DM: Right, Parkinson’s, yeah, exactly. Even heart disease. There are lots and lots of diseases for which, just the estimate that we get from DNA is not enough. And, so we think that the same approaches that we’re applying to cancer today, we’ll be able to apply to other diseases tomorrow.
CW: So, if a gene test has one unit of benefit, RNA has a ten unit benefit.
CW: And, right now a clinician is worried about immunotherapy, can use your products to help assist and guide them in their clinical decisions?
DM: Right, so the products in development today are aimed at the clinician, and then also the drug developer and the immuno oncology space, so there’s this tremendous new class of cancer drugs called immunotherapies, which are actually using the bodies own immune system to fight the cancer. And, it’s had a huge impact, already, in our ability to treat cancer, so-
CW: So, Jimmy Carter, in glioblastoma, is an example of-
CW: That was on 60 Minutes, is a good example.
DM: It’s a great example, and what we have seen is that in … For some cancers, what was previously maybe about a 30% survival rate over three to five years is now more like a 60 to 70% survival rate. So, that is a huge, huge impact. Particularly when we talk about cancers that are very common. So, lung cancer, there are about 150 thousand cases a year.
CW: And standard care is more around using DNA to type the treatment?
DM: That’s right. Exactly. So, DNA is being used to help guide those treatments today. So, with those types of opportunities, with our Paragon assay, being able to get much better insight into what’s going on inside the tumor. Is the tumor trying to evade the immune system’s attack? Does the tumor look like it’s particularly susceptible to the type of-
CW: So, you get a much more tactical view of … It’s not just, “Hey, it’s getting bigger or smaller.” But, what other systems are operating to help it get bigger or smaller and reactions and-
DM: Right, and for people who are developing drugs around this, we can even tell them what types of immune cells are infiltrating the tumor, and what amounts, so that information is really helpful in determining not only is the treatment effective but, also, in what way, and for what patients. So, which are the patients that are going to be most likely to respond to that, so, that type of information is essentially not available with as much detail and as broadly today. So, today you might have to run three completely different types of tests. One of them, talking about trying to identify the immune cells that are present. You would really only be able to see if you were working with a biopsy that was fresh out of the operating room. And, very rarely is that possible. And even if it did so, it requires a very technically challenging and expensive.
CW: This is part of the reason we invested, is that there are people sequence RNA, but you guys are fantastic at dealing with more challenge samples that actually in a production environment you’re going to run into other challenges in terms of getting a good sample.
CW: I mean it seems like a mundane thing, getting a good sample, but it’s critical.
DM: Most people don’t know that most clinical specimens are stored in a preserved way. So, people think back to your high school biology lab, where something’s preserved in formaldehyde, so, think about something preserved like that, but also encased in wax. And, this is done, really, historically, for pathologists, right? So, they put it in this form, so, not only does it preserve the tissue, but it can be sliced off very thinly, stained, and then looked at under a microscope. So, it’s really great for that purpose, but it’s not great at all if you’re trying to extract RNA and DNA from that, and so we’ve been able to combine, not only techniques, which allow us to get really high quality RNA out of those 95% clinical specimens, which are stored in that way, but also, pairing that with sophisticated software, which allows us to interpret that RNA data in a way that’s clinically meaningful.
So, it’s one thing to be able to generate good data. That’s part of the process, but by pairing that molecular process with sophisticated software systems, we’re able to get much more information and much more interpretation to the end user, to the doctor or to the drug developer.
CW: And, so, you’re business is also building at that software to feed into that analysis.
DM: Absolutely. And we’ve really found that controlling the entire process, both the molecular and the software pieces, are essential to getting a good result. A lot of people have thought about trying to approach this problem on one side or the other only, and there’s so much variability in the different techniques that you can apply that, and particularly we talked about the challenges of working with these poor quality, low quality, clinical specimens. You really have to be very thoughtful about which technique you use in the laboratory to extract RNA, and then pairing it with software that expects a particular output, so, a whole integrative process, we’re finding, gives a much cleaner, more sensitive and accurate result.
CW: And, so, what’s ten years out?
DM: So, we are at the very beginning of an incredible era. We look back to the human genome sequence and this new high throughput sequencing technology being 10, 15, 20 years old. Already we’ve seen an impact in our ability to practice medicine today from that.
The clearest way I can explain it, the best analogy I’ve found, is to think about what a personal computer was like in 1987. So, in 1987, it was very primitive by today’s standard. Maybe just a monochrome screen, not even a color screen. Big, heavy, low powered, no internet. Think about the impact that computers have had on our lives over the last 30 years. It’s hard to imagine the countless ways in which our lives are different because of the development of computers over that 30 year period.
So, today, for genomics in particular, for applying RNA to medicine, we’re kind of like at 1987 today with that. So, think about, in the course of the next 10, 20, 30 years, how much of an impact computers had over a 30 year span. The same kind of impact I expect to see from genomics and medicine over the next 30 years. It’s really going to be truly amazing.
CW: And are you the Apple, the Microsoft, the Amazon?
DM: We certainly think that we are moving this forward faster than anybody else. And the great thing is that as we’re pursuing RNA’s potential in medicine, there are lots of other companies doing equally amazing things in other areas. I think about the impact that genomics is having in agriculture and our food supply. That’s going to have a tremendous impact, and it is already on how our daily lives are led. And, so, there are all these multiple fronts in which this technology is being applied that are going to be absolutely incredible.
CW: And this is … I’ll just say, put a plug for St. Louis on this is that the fascinating thing here, is the human genome was done here. The technology is being commercialized here and up in Boston? And, that it flows easily between agriculture and human? And, it’s great because we can take talented people, and just sort of, they can wander back and forth, and have got 20, 30 years of legacy of what it was like in 1998 to do this. What it’s like today. And it sort of gives you a sense of the momentum of the technology.
DM: Absolutely, and we are fortunate sitting here in a city, which has some of the deepest talent pool in this area, and the world, to be able see all that’s happening around us.
CW: So, every startup needs a customer. Even one that’s doing cutting edge work like this. So, is your … Because customers really help you figure out how to reduce a product to practice. Who are you looking for to be your customers, at this point, to help Cofactor reach the next level?
DM: So, today, we’re partnering with clinical researchers and pharmaceutical companies doing some of the most innovative work. Research hospitals, institutions, who are running clinical trials. Really focus on immuno-oncology, who are looking to use our technology to understand better their patients. So, those are the people that we’d love to connect with, to help them understand, to apply this technology, to move their drug development programs faster, to be able to understand the right treatments for their patients. That’s our user base. Those are the people that are helping us develop our products.
CW: So, if a patient, or somebody around a patient is being treated by immunotherapy, tell your clinician? Tell your pharmaceutical company. And, because you get better by getting the pressure from those people to develop better answers for them.
DM: Like every company, we learn best from the people who use and love our products. And, they help us make them better, make the next generation even more effective for them, and so we’re no different. And we have clinical studies underway right now, which are allowing us to get that information from some of the brightest minds in the country and we’re always looking for more people like that who are interested in engaging with us, to learn from them, to help them with the challenges that they’re facing and understanding their patients.
CW: Great. Thank you for your time. We’re going to do this again, because there’s a lot more to talk about. And, we’re not going to do it over 20 years, but over a shorter period of time. But, thank you very much for your time. Thanks for everything you’re doing at Cofactor. We’ve really enjoyed the journey that you’re on. And, glad to be a part of it.
DM: Thank you, Carter.