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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 University, 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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Interviewer: What's becoming loud and clear is that most scientific studies are, well, unreliable. But there's help out there for scientists. We'll talk about that next on The Scope.

Announcer: Examining the latest research, and telling you about the latest breakthroughs. "The Science and Research Show" is on The Scope.

Interviewer: I'm talking with Darell Schmick, research librarian at the Eccles Health Sciences Library at the University of Utah. So there are a number of reasons for what some people are calling a research reproducibility crisis, including fraud. But even scientists with the best intentions are at risk for doing sloppy work and there are a lot of reasons for that as well. One of the things that you're interested in looking at is data management. I really like this example of the Postdoc who leaves the lab.

Schmick: So it was it an age-old issue where you do a lot of work and it happens to be on your personal computer. It happens to be in a folder with poorly-named files and you produce a bunch of research on behalf of the institution, but then, once you're done, you obviously take your computer with you, right, and then head off to that next position. Now, that seemingly innocuous, however, has a lot of implications. The data that you produce on behalf the university has ownership concerns. Is it the Postdoc's? Is it the University's?

Interviewer: How can that lead to issues with research reproducibility?

Schmick: So if the Postdoc takes all that data with him or her and hasn't been saved into the department bio or anything like that, how can you ensure that you have records of all the work that Postdoc has done? They could have taken just a little bit of it, they could have taken a substantial chunk of it. And it really leaves the PI as well as the rest of the members of the lab with potentially a significant disadvantage.

Interviewer: So there's a lot of knowledge that can be lost?

Schmick: Absolutely.

Interviewer: So what are some ways to avoid that?

Schmick: We do teach a research administration training class that talks about just the basic fundamentals of just good data management, which involves things like where to properly back up your files and how often to do that. Myself and a couple of the librarians on campus have been working on a pilot for electronic lab notebook technology.

So if, say for instance, you happen to have the perennial issue of lab members recording data in their own personal computers because it's inconvenient to share it, this sort of technology allows for a lab to share in a collaborative notebook technology something that has all those questions that we're talking about answered, like how frequently will it be backed up? Is it going to be backed up in a trustworthy source?

Interviewer: Right. Well, and not to mention that most lab notebooks that I've seen are kind of a disaster.

Schmick: Are you saying that scientists don't uniformly have amazing handwriting?

Interviewer: Exactly. Right, we're all human. And even how the information is recorded is varied from person to person. So the idea is that this would become more standardized?

Schmick: You hit the nail on the head there.

Interviewer: Any other approaches to make their data more accessible or reliable?

Schmick: When we're talking about optimizing the mechanics of anything in the research process, we want to ensure that we're doing it in a way that is not only accessible by us because we can look at our notes, presumably, and be able to understand what we were saying, understand what we were recording, understand what we were encountering in that process. But to think about how the results that you're producing are going to be read by somebody else that's not in that same context. So if you're doing an experiment, you do it for you, but you also ensure that you're doing it in a way that if somebody wants to reproduce that experiment, they can do that.

Interviewer: You've talked about ways about preserving information within a lab group, for example, a research group. What about sharing information more broadly with the scientific community?

Schmick: That's a great question, Julie. And a lot of people think that all you are able to really produce is that end product, that finalized article. And we don't realize, many times, that when we're doing experiments, when we're producing all this data that we're talking about, that data could be good data. It could be good information and good intel for another scientist that's stumbling across that same issue.

If you're embarking on answering a research question and you come across a dataset that has already sought to ask that question, you find out that maybe those results weren't satisfactory enough to produce something into a finished article, that could potentially save you years in otherwise reinventing the wheel.

So another thing that we like to talk about is the idea of ensuring that researchers know that the data that they produce is of value and there are places that you can store that. So one example that comes to mind is figshare. And figshare is a repository that you can actually assign a DOI to the data sets that you're uploading on there. Figshare are all about open science so they say as long as you're making it public, I mean, you can upload it for free.

Interviewer: So how can sharing data with the scientific community help with research reproducibility?

Schmick: There's a lot of news as of late in the way of that openness toward science where folks on a peer review panel want to see the steps you were able to take in order to draw the conclusions that you were able to take or able to make. And if they're able to go ahead and see that data right there from the start, it answers all those questions.

It's when things like that data being withheld presents a larger problem not only for you as author but greater implications for science in general. When we start to withhold that data, when we start to conceal certain steps in that recipe toward what we ended up with the final product, it leads towards a slippery slope of was it not open science and that closed scholarly environment, I think, is something that is well worth fighting against.

Interviewer: Where can people go to learn more about best data management practices?

Schmick: There are a lot of places. If you're embarking on a data management plan there's a great tool called DMPTool. That's dmptool.org, and it'll take you through the steps of the processes of, "Let's take you through the steps of the data management process." By asking you in a 20 questions format, "What's this data going to be for? Which agencies are going to be seeing this data?" And it'll give you recommendations at the end from there. If you're at the University of Utah's campus, I'd encourage you to talk to me or one of our other fine staff at Eccles Health Sciences Library. I'd be delighted answer any questions regarding data management plans.

Announcer: Interesting, informative and all in the name of better health. This is The Scope Health Sciences Radio.

Interviewer: Mole crowdsourcing: it's an innovative way to discover and identify melanoma and that's coming up next on the scope.

Announcer: Examining the latest research and telling you about the latest breakthroughs, The Science and Research Show is on The Scope.

Interviewer: You know, it sounds a little gross. Take a picture of what you suspect to be a cancerous mole and then post it to the Internet for others to see, but it turns out that might be a really effective way to screen for melanoma. Jake Jensen is from the Department of Communication at University of Utah and he won a $2.2 million grant, part of the 2015 and NIH New Innovator Grant to look at that. So first of all, is that an accurate way to screen for skin cancers, take a picture and have non-trained people look at it?

Jake: Well, a dermatologist might quibble with the phrasing of that. Whether it's a form of screening or not, we could debate. What it is is it's a really effective way to potentially move people to go to a dermatologist.

Interviewer: So let's talk about the way things are and why that's not necessarily getting the job done and the way that you hope your research points to the way things could be.

Jake: What happens right now? Well, right now, we say to people, "You should engage in monthly skin self-exams where you look at your body and look for strange moles, strange growths. And when you see them, you should make note of them and you should go into a dermatologist for a clinical skin examination." That's our basic game plan.

There are lots of problems with that game plan. The first problem with a game plan is skin self-exam is not very effective. People are very bad at finding odd-looking lesions and moles on their body. And so I spent the better part of a decade doing research on skin self-examination, trying to improve the technique. And study after study, people were horrible at it. No matter what we did, they were horrible. The only consistency in the research was no matter what we did, people were horrible at skin self-examination and that's what other researchers were finding as well.

We were all frustrated in one day, in my frustration, I walked into one of my colleague's offices because he has a wonderful couch in his office. And I flop myself down on his couch and I said, "I'm so tired of skin self-examination research. No matter what I do, it won't work. There is no solution." And we talked about it for a while as I vented and eventually, he said something that forever changed the way I thought about this. He said . . . keep in mind, he's not a health researcher. He's somebody who studies new media so he was a good person to vent to in that he was like, "I don't know what you're talking about, but I know you're angry." And he said, "Well, is it that individuals are bad at skin self-exam or is it that groups are bad?" And I thought, "Individuals are bad so groups are bad. So I don't understand the question."

Interviewer: Yeah. Because if one person's bad, how are 20 of those people any better?

Jake: I said, "I don't understand the question." He goes, "Well, there's this thing called collective effort that says sometimes a group is good at something when an individual is not. You can take a group of individuals who are bad at a task, but as a group, the group somehow can be used and mobilized to be good at a task." And I said, "Okay. Rather than, 'Are individuals good,' here's what I want to ask." I took a rock or a curve. It's a type of statistical analysis. I said, "Is there a rock curve that fits to this data?"

And there was, at the group level. And here's what it looks like, for most moles that people look at, they're not suspicious. Take a photo of any mole on your body or any mole that you encounter. Take any mole imagery and you show it to people, generally, they say, "That looks fine." However, when more than 19% of people say a mole looks suspicious, now we're in a different world. If you use that as a cutoff, you can find 90% of melanomas because there's something there that the group picks up on. Here's why I kept missing it: because when you're thinking about the individual level of ability, you're saying, "Well, I want individuals to find it 90% of the time." But individuals can't do that. Groups can when we use a cutoff and we say, "Well, if it's more than 19% of the group that's concerned, let's use that as an indicator."

Huge implications for that. It doesn't matter whether you train people. In a sense, the best group is a group where you say, "Just tell us whether you think that's a weird image or not. Is that a weird mole? Just yes or no." Sometimes, people would say to me, "Can we have laypeople doing this?" It's risk factor. They're not trained. When it comes to laypeople, I see no evidence that training them makes them better at this intuitive, sort of "Is it weird or not" because I'm only going to look at them on the group level. I'm not going to look at them as individuals. So I just want to know if the herd finds it weird. And the answer is when they do, we kind of find melanoma.

Interviewer: Let's go back to the original problem. The original problem is individuals aren't good at self-screening. So they see a mole and maybe they might not think it's a problem. Is that the problem? Or is the problem that they don't do anything about it?

Jake: Yeah. Well, chicken or the egg in some ways.

Interviewer: I guess what I'm asking is will this solve the problem that you've laid out?

Jake: So here's what we want to do. I want to tell a real quick story because it'll help you to understand where we want to go with this. Imagine there's a farmer and the famer's name is Joe. Joe is out in the field and Joe sees that he has a weird mole on his arm. He comes home that night and he tells his wife, Martha, "Hey, I've got this weird growth on my arm." Martha says, "You need to go in to a dermatologist."

Joe is reluctant to go to the doctor for any reason and Martha knows this. So she rails on him for the rest of the night. But Joe, "Eh, it's harvest." He'll go later. Eighteen months pass by before Joe eventually goes in. By the time he goes in, he has late-stage melanoma. There's very little they can do for it at that point in time. Joe passes away within the next few months.

I'll give you an alternative ending to that story. Everything is the same about the story, except this: when Joe comes in from the field and shows Martha the mole, Martha pulls out her phone. She snaps a photo of the mole. She doesn't even bother arguing with Joe because she knows Joe will not listen. The next morning at breakfast, Martha slides the phone across the table and says, "Ten thousand people have looked at your mole and 47% of them think it looks weird." And that's such a high score that it was flagged in the tele-dermatology system and a dermatologist who does rural rounds, who will be within 40 minutes of us next week, wants to see you at 9:00AM next Tuesday because she's looked at the mole and she is suspicious as well."

Joe goes in next Tuesday. It's pre-cancerous. The dermatologist chops it off. Joe lives. Now, that cell phone system I just described is what we're trying to build.

Announcer: Discover how the research of today will affect you tomorrow. The Science and Research Show is on The Scope.