Amanda Mainguy | Staff Photographer
Keith Yamamoto, of the School of Medicine at the University of California, San Francisco, discusses the flaws in the current health care system that inhibit its improvement during the morning lecture Monday in the Amphitheater.
Medical research is at an inflection point, Keith Yamamoto said in his 10:45 a.m. morning lecture on Monday in the Amphitheater. But with strategic moves in data aggregation and collaboration between disciplines and sectors, medical researchers can revolutionize health care through what he called “precision medicine.”
Yamamoto serves as vice chancellor for research, executive vice dean of the School of Medicine, and professor of cellular and molecular pharmacology at the University of California, San Francisco. His was the first lecture in Week Nine, “Health Care: From Bench to Bedside.”
Health and disease are difficult to understand, Yamamoto said, because complicated biological mechanisms are controlled by intersecting, multi-step signaling networks. They are context-dependent and affected by countless variables. Thus, he said, scientists are stuck researching “imprecise medicine,” because they fail to understand mechanisms and concepts underlying disease.
This needs to change, Yamamoto said.
“If we can do it, what will happen is that we will reach this mechanistic understanding of biological processes, moving from description to understanding,” he said.
This, he said, can be accomplished by the integration of biological information, incorporating concepts and technologies from the physical sciences, engineering, math and computer science.
And researchers need to develop a working continuum not focused on understanding a specific disease, but on understanding biological processes. That continuum, he said, would not be a “one-way street”: researchers, medical practitioners and patients would both contribute to and benefit from such a program.
Clinical research, Yamamoto said, is unsustainably expensive. But precision medicine approaches make use of data that is already easily collected on such devices as smartphones, which can combat high costs.
A key issue in medical research and clinical practice, Yamamoto said, is the way in which diseases are classified.
“What if we classified diseases, instead of by symptoms and organs, by mechanisms?” he said. “That would be a reasonable thing to do because all of you know that there are diseases that are actually caused by multiple mechanisms. And if we just lump them together, thinking that we’re studying one thing, we’re going to miss critical bits of information.”
Type 1 and Type 2 diabetes, for example, are each caused by multiple mechanisms, yet are conceptually lumped together, Yamamoto said. Each kind of cancer is also stratifying into multiple mechanistic types, he added, citing the example of breast cancer associated with overexpression of the HER2 gene.
Overexpression of HER2 does not necessarily predict breast cancer, and normal expression does not necessarily preclude women from getting breast cancer.
“It’s neither necessary nor sufficient,” he said. “But for every breast cancer patient that overexpresses HER2, there’s a drug called Herceptin. That drug helps every single woman who has that type of breast cancer.”
Twenty-five percent of women with breast cancer overexpress HER2, he said, and though Herceptin does not cure breast cancer, it increases such patients’ lifespans.
Genentech, the San Francisco company that developed the drug, would never have finished the 12 to 13 years of clinical testing on tens of thousands of patients if researchers had not already understood the mechanism, Yamamoto said.
“If they had not known that that drug that they were making was specific for every woman who overexpresses HER2 and has breast cancer, the drug would never have made it to the market,” he said.
With research as expensive as it is, understanding biological mechanisms and not mere symptom descriptions is key to drug development, Yamamoto said.
“Understanding mechanism, in this case, is the ballgame,” he continued. “Without it, no drug. With it, a drug that helps all these people.”
UCSF has created an institution-wide pilot program to explore precision medicine.
“It has become the driving vision for the way that our institution is operating,” Yamamoto said.
Taking a step beyond personalized medicine, in which patients take a more active role in their own medical data, precision medicine aggregates that data with that of medical research, population research and more.
The precision medicine approach, Yamamoto said, allows researchers in different fields to collaborate on biological mechanisms that underlie the diseases that they study.
For example, defects in cilia, which Yamamoto described as a “little hair” extending from each cell, are associated with diseases as diverse as situs inversus, retinal degeneration, polydactyly, polycystic kidney disease and hydrocephalus.
“The people studying those diseases don’t know that they’re studying the same thing unless they’re really paying attention to the basic science literature, and so they don’t talk to each other,” Yamamoto said. “And things that go on in studying polycystic kidney disease may end up helping our understanding and eventual treatment of hydrocephalus. But if the people aren’t talking to each other — because we’re not classifying disease by mechanisms — then we lose that possibility.”
Q: A question that you know is coming: What are the implications of precision medicine for patient privacy and ethical concerns? This is from Twitter.
A: Critical question. And so the implications, I think, need to be stated in a very direct way, and that is that the one completely unambiguous identifier about you is your DNA sequence. And as we begin to work sequencing of your genome into the information about you, then every piece of information, every other piece of information about you, is going to be connected to that unambiguous identifier of you. So the notion of real privacy and security of your information is going to change dramatically; in fact, one could say that it’s going to go away. So how do we deal with that as a society? How do we deal with the fact that information could be misused by organizations, companies, insurance companies and so forth; I don’t mean to single them out but why not? Who could misuse the information; what are we going to do about that? Because there’s no choice. We gain from collecting the information, but you risk this loss and you risk the misuse. So we have ethicists on the one hand and health economists on the other working on this, because it’s my view, and I’d love to know what you think, that there’s no way to get insurance companies to behave themselves out of the goodness of their hearts. And that the only way is to convince them that, if they are good stewards of the information, their costs will go down — they’ll make more money. And if they’re not, the barriers will go up, they’ll lose access to that information, and health care costs will continue to spiral. Now can we do that? What are the answers to the economic models? We don’t know. Early days, but the implications are incredibly strong, they’re very powerful, they have huge effects, and I think there’s no escaping them, and we’re going to have to find ways to address them.
Q: When will Alzheimer’s qualify for precision medicine?
A: Already, there’s an enormous amount of work going on now. Some of you know that, in April of last year President Obama, announced the Presidential BRAIN Initiative. It was framed in much the same way that President Kennedy’s call in the 1960s was, that said in 10 years — in the decade of the ‘60s — we’re going to land a man on the moon. At the time that Kennedy announced that grand challenge, there was no way. We had no technology that was capable of landing a man on the moon. We were having a hard time getting rockets up into the air. And what that challenge did that was framed at that level and that magnitude was that it motivated people to come together to develop the technologies necessary to put a man on the moon in that decade. And in 1969, as you know, that was achieved. The goal of the Obama BRAIN Initiative was the same, in which the president said, “We’re going to understand the functions of the brain and the main things that go wrong with it in Alzheimer’s and Parkinson’s and other degenerative diseases that you’re all aware of in the next decade.” And he pulled together a set of forces, a set of government agencies and private foundations, and some companies, and said, “These are the starting points. We’re going to put together a committee of scientists and clinicians to find what the key issues are and move in that direction.” There’s lots of great work going on at UCSF in Alzheimer’s and other places in Alzheimer’s now, and I’m confident that we’re going to make progress quickly.
Q: I’m going to ask four questions and kind of combine them because we have very little time, and these are all about the process in schools. One person from Twitter wants to know, how are scientists and clinicians incentivized to work together at UCSF to achieve precision medicine? There’s another question about how precision medicine is going to affect medical schools. Another about other education and research institutions who are using this process or similar processes, and then another about schools of public health. So it’s a general question about educating and use of the precision models.
A: Those are great questions, and you know, interestingly, I hadn’t thought about this before, but I realized as you were asking it that my answer to what’s going to motivate scientists to work together toward precision medicine is the same as the one I gave about insurance companies. The currency for researchers is to be able to get credit for what they do, and academic promotion policies and hiring policies is based on that kind of individual focused performance. So what would motivate an individual scientist to share his or her data? The only thing is going to be if we can convince those individuals that their individual work will go better if they share. And I think I tried to make a case here, and we’re trying to make that case at UCSF and elsewhere, that the payoff is really good, that suddenly questions open up to you that you hadn’t thought of before and if you had, you wouldn’t approach because you don’t know how. But if you worked with a team then that can make a big difference. So downstream, my hope, my fantasy is that academic institutions, research institutions in general, academic institutions, will look very different in, let’s say, a decade. Where they will still be composed of departments and all the standard academic organizational stuff, but in fact the way that they’ll function is that every faculty member will be a member of, let’s say, a half a dozen teams. Those teams are dynamic. They’ve come together because of information that dropped out of the knowledge network that is fascinating to that group of individuals that got together, and sat down and coffee and said, “You know, we could do something amazing here if we worked together.” And you’re on maybe half a dozen of those. And maybe one of them, your affiliation to one of them ends today because your job is done, they don’t need a molecular biologist like me anymore, they need an epidemiologist, so I’m done, because of success, and maybe team B ends because in fact the idea was fascinating and interesting and compelling but wrong. And so the team realizes that with a little bit of work and they dissolve, disappear. No harm done, right, no stationery was printed in the course of making the team. And now I’ve got head room for joining two more teams I wanted to join but I don’t have time. Our life as investigators and clinicians and scientists changes into this kind of sliding progressive dinner of things that we’re really excited to be working on and never thought that we would be able to but now we can, and I think that would be a really strong motivation.
Q: Ladies and gentlemen, we’re out of time but we have lots and lots of questions, so I’m gonna suggest three things. One is that we will be hearing more about some of these collaborative concepts as we move through the week, so be sure and be back. Another thing is we will keep the questions, and if Dr. Yamamoto doesn’t have time to look at them and answer them for us, we’ll ask our friends at the National Academy of Sciences to maybe do an article off-season. And the other suggestion is what Dr. Yamamoto was saying about precision medicine, it’s going to be up to us, so maybe there are some ideas that you’d like to research on your own computer. Thank you and thank you Dr. Yamamoto.
—Transcribed by Quinn Kelley