Episode Transcript
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Interviewer: Not long ago, doctors hoping to cure human disease spent a lot of time in the laboratory looking through microscopes, but computing power and big data have changed that. Now, a lot of physician scientists spend more time in front of the computer than they do the microscope and they're analyzing large data sets, and that has fundamentally changed how we find cancer treatments.
We asked Dr. Rakesh Nagarajan, the Director of the Institute of Clinical and Translational Sciences Biomedical Informatics program at Washington Ï㽶ÊÓƵ, about how drastic this change has been, what the barriers are to being more successful and when we might realize the full potential of precision medicine for cancer prevention and treatments. Dr. Nagarajan, the first thing I want find out, though, is more about this notion of how technology has changed biology from a qualitative to a quantitative science.
Dr. Nagarajan: Biology and medicine in the early '90s were a qualitative science. So looking under the microscope or looking at a gel to make a determination. And not only was that qualitative, it also limited how many genes or RNAs or proteins or lipids or metabolites you could look at. With the advent of the Human Genome Project, technology really below up in a variety of ways, much beyond DNA itself to DNA and RNA. Technologies such as microarray technologies were one of the first where you could massively, in a parallel way, quantitatively look at DNA changes and RNA changes at the whole genome level.
Interviewer: And then, how does this change? How is this the game-changer? You had mentioned to me, before we started speaking about this, that this just kind of happened recently, during your PhD?
Dr. Nagarajan: Yeah. So, it was pretty amazing. In the early part of my PhD, I was running a lot of gels and I was actually quantitating individual spots. And I could look at one or two genes, or maybe three genes if I worked really hard. In the middle of my PhD, a technology came about that was called Quantitative RT-PCR that looked at MRNA levels, or messenger RNA levels, and now I could do 30 or 40 or 50 genes if I worked really hard.
And near the end of my PhD, the microarray technology came about that I talked about, where now I could look at every gene and its expression and I didn't have to actually work that hard. But now, I had to work much harder at the computer. So it really converted how much time I spent at the bench versus how much time I spent at the computer.
Interviewer: So what are the challenges that we're currently facing that are slowing us down or preventing us from being where we ultimately want to be?
Dr. Nagarajan: Yeah. So I think there is a multitude of challenges. The first really is how do we use this genetic and genomic information appropriately, correctly, and who's going to pay for it? So payment is a big issue. And payers are saying, so both CMS, which pays for Medicare and Medicaid, and private payers like Blue Cross and Anthem and Aetna are saying, "Well, where's the clinical utility in sequencing?"
And researchers and clinicians are saying, "Well, it's the chicken or the egg. We need enough data to show you that there's utility. And if we had to pay for all of it through research, that would not grow fast enough."
Interviewer: So this is even before we're even talking cures? We're just talking about being able to collect enough data to create treatments.
Dr. Nagarajan: Correct. That's right, that's right. And really, a lot of us have moved from saying, "We need to do all of this work through research," to "Let's do it clinically," because that's where the volume is at and that's where you can get the massive data in order to then have cures faster. So the typical workflow in the research space is to find a molecular mechanism of disease. Pharma will then target drug. Drug will then go through a 20-year cycle of getting FDA approved. We want to short-circuit that.
Interviewer: Sure. When do you think we'll get to where we need to be, where this thing's firing on all cylinders? I know you're always continually pursuing these things, but
Dr. Nagarajan: Sure. So it's always slower than you think because there are legal, social, regulatory barriers, as well as informatics or IET and our knowledge, which is a barrier, or lack of knowledge, which is a barrier. But we would really expect cancer to be a chronic disease similar to HIV, where now multi-therapy can prolong life and sometimes even provide an effective cure.
And that would be, I think, what I would say would be the end game for cancer. Not to completely eliminate it, I'm not sure that's in the cards, but maybe early prevention is another way. So I think that that is on our time horizon of life. So I would say 20, 25 years.
Interviewer: That we'll be in a pretty good spot?
Dr. Nagarajan: I think so. I think so. That's the optimistic view, but I am very optimistic.
Interviewer: That's pretty exciting stuff, isn't it?
Dr. Nagarajan: It is.
Interviewer: You're a physician as well, correct?
Dr. Nagarajan: Yeah. So I'm trained as a physician scientist. I decided to use my skills to do data science or informatics. So I hung up my stethoscope and hung up my pipette, but I've a good understanding of both bench science and medicine.
Interviewer: Yeah. And when you first started your medical career, did you ever imagine this would be where you'd be? Or where medicine would be?
Dr. Nagarajan: I didn't know that this is where medicine would be, but in tenth grade, when I learned about DNA and I come back to that day, actually, and I can remember it. It immediately hit me that, "Well, why don't we just fix this DNA?" And so that is ultimately what we're trying to do, but doing it in a more practical way, post-DNA.
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