[PODCAST] Cofactor Genomics and the Future of Personalized Medicine

[PODCAST] Cofactor Genomics and the Future of Personalized Medicine

Read the transcript below or listen to the podcast.

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.

CW: Okay.

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.

DM: Yeah.

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-

Read the rest of the podcast transcript here.