Precision Health for All: NIH’s Precision Medicine Initiative Cohort Program
Title Slide
Precision Health for All
NIH’s Precision Medicine Initiative® Cohort Program
Eric Dishman
Director, PMI Cohort Program
National Institutes of Health
NHGRI Council Sept 12, 2016
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Mr. Dishman speaking at the front of a conference room.He is holding a remote control for a projector. Behind him is a lectern with “National Institutes of Health” inscribed on it.
Mr. Dishman:
I want to go on record in saying Eric [Green, Director, National Human Genome Research Institute, National Institutes of Health]specifically and Terry and Carolyn Hunter have been fantastic at both trying to get me at least a little less stupid about what NHGRI does, as well as just helping me settle as we move across the country from Portland to the area. So that’s which grocery stores to go to, which traffic to avoid, all of those are very important codes for how to live here successfully. So I call this precision health for all, and what I’m going to do—Eric, tell me how long we have, because I could go for days or weeks or months. I was going to be a Dickens scholar at one point, paid by the word, so it’s like I’ve never seen a word I can’t use.
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A PowerPoint slide appears on the screen. Its heading reads: Know Enough To Be Dangerous…And Appreciative
Below this, in the left column, is a box with the heading Genomics and Society. The box contains a short article that discusses psychosocial and ethical issues in genomics research, psychosocial and ethics in genomics medicine, legal and policy issues, and broader societal issues. Below this boxis a sentence that reads: Understand how to situatethis new knowledgein our culture!
In the right column below the slide’s heading is a logo that reads, emerge network, Electronic Medical Records and Genomics.
Below this logo is a sentence that reads, Learn from your pioneering work onEMRs and biorepositories!
Below this is a logo for ClinGen, Clinical Genome Resource. Below this logo is a sentence that reads, Continue to drive data sharing,curation, & trust in “meaningful use” of variant data!
Mr. Dishman continues:
So at this point, I would say I’ve read your strategic plan. Certainly the part of the strategic plan around genomics and society is particularly near and dear to my heart as a social scientist and as somebody who has spent a whole lot of my career focused on the legal, policy, and social implications of capabilities. Back in the early days of telehealth, I built very successful telehealth equipment and even workflows with clinical teams when I worked for Paul Allen, the co-founder of Microsoft, all those years ago.
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The screen splits, with the previously described slide on the left of the screen, and a camera view of Mr. Dishman speaking in the lower right corner.
Mr. Dishman continues:
And technically it worked, but the payment model, the entire sort of reimbursement scheme, all of the legal and social implications and the development of workflow were not ready, and in many ways we’ve taken 25 years to catch up with the technology and start to incorporate those. So I am very sensitive to those and the need to work on those early on, when you’re trying to do bold new innovation. I know enough of that emerged to be dangerous and I’m excited what we can already learn from the work that you’re doing on EMRs and biorepositories and certainly on ClinGen as well in terms of—you know, I don’t mean it in a negative way, but how do we make meaningful use of variant data and clinical practice?
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The previously described slide again takes up the entire screen.
Mr. Dishman continues:
Part of the discussion that you were just having and not only do I think a lot of the experts and a lot of the people that are frankly in our awardee community are part of, we’re already part of the awardee communities at NHGRI. So you’ve helped fund both training wheels and sort of moving into the 10-speed and then we’ll eventually get to 27-speed bicycle racing on this as we go forward in time.
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A new PowerPoint slide appears. Heading: Appreciative of Your Work More Than You Know.
In the upper left corner is a photograph of Eric Dishman as a teenager, lying on a couch with a puppy. Caption: First week of very imprecise chemo,summer 1989, Chapel Hill at age 19.
Mr. Dishman continues:
I’m very appreciative of your work, as Eric mentioned. I won’t go through the whole story here, but the long story short is, this is me the first week of a very imprecise chemotherapy regimen, the first of about 60 over the next 23 years that I went through. That’s me in the summer of 1989, Chapel Hill. My wife was wise enough to get a dog, or a puppy, to help me survive that first round of cisplatin and other nasty things and all the pre-nausea–drug days. So I was a pretty small guy at that point in my life, at age 19, and got a lot smaller really quickly. And I did manage to somehow—and I have donated my genetic material to serve two different resilience and survivor studies, because you look at my clinical history and say, “He shouldn’t be alive, just based on the drugs that we actually put him on.” And it was a whole genome sequence, as I was in my role as an executive at Intel years ago, four or five years ago, going around to different genome companies that needed our highest in computing to be able to even make their research or their business model work. And one of these had seen me speak at a nephrology conference years before and said, “Well, why don’t we do a whole genome sequence and we’ll help you understand our technology that tries to take that and start to visualize that data.” I didn’t think much of it. I mean, I did. We arranged to have tissue and blood and all of that done. I signed forms that said they could share it with my clinical team.
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Mr. Dishman speaking at the front of the conference room.
Banner at bottom of screen: NIH NHGRI logo. Eric Dishman, Precision Medicine Initiative Cohort Program, National Institutes of Health
Mr. Dishman continues:
At that point, it took three months of processing on Intel’s highest-end equipment and the top sequencers that were out there to compare me to the “not me.” And then, I didn’t know, but my clinical team took another four months trying to get help from startups, from clinical research, to actually the first time they’d ever done it, makes sense. And they basically came to a lucky well-educated guess. They were very precise when they told me that 92 percent of everything they’d ever put me on was destined never to have worked. I now understand that they couldn’t actually know that, but it was a good guess. But they got enough data and understanding to say, “It looks more like the mechanisms that cause pancreatic cancer are what are causing your cancer; we’re going to put you on a pancreatic cancer drug.” And they did, and I became cancer-free really quickly. Full kidney failure. Intel employee donated a kidney to me because my old ones were failed, and suddenly I’m healthier now at age 48 than I was at age 28 or even age 20. So I came out of that. My wife will tell you, in the ICU that—I can’t remember it, but she took notes because we knew that I wouldn’t remember at all—she said I’ve got to figure out how to make this kind of stuff available to everybody else.
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Split screen, with the previous slide at left, and a camera view of Mr. Dishman speaking in the lower right corner.
Mr. Dishman continues:
And that’s been my mission when I came back to Intel and ever since then, so this fits in.
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Content is added to the previous slide. Below the first photo appears a photo of a stack of spiral notebooks with the caption, Have kept a journal since 3rd grade.
To the right of the photos appears the following text:
WORLD FELL APART: Sept 11, 2001, Portland
Exhausted. Quick. Much more later. Like I need to writethis to remember, but planes destroyed WTC in NYC andPentagon attacks. Saw horrors wish I could un-see.Wonder if more will hit tonight.
Ash and I can’t sleep—talking mostly about fear and, in away, glad this whole ride has meant we never broughtkids into this insane world. Am not afraid of dying in aterror attack (though I do worry bombings are now in ourdaily lives as in many parts of world). Cancer will certainlysave me from an explosion—there’s one benefit!
Mr. Dishman continues:
I’ve kept a journal since third grade, and yesterday I was actually looking back at what I wrote on 9/11, 15 years ago, and it was kind of interesting.
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The slide now takes up the entire screen.
Mr. Dishman continues:
You can start to see some of the mindset that I wrote in here. “I saw horrors that I wish I could unsee. I wonder if more will hit tonight,” meaning attacks. And then I said, “Ash and I can’t sleep, we’re talking mostly about fear, and in a way, I’m glad this whole ride, this whole cancer ride, has meant we never brought kids into this insane world. I’m not afraid of dying in a terror attack, though I do worry bombings are now in our daily lives as in many parts of the world. Cancer will certainly save me from an explosion, there’s one benefit.
I could never have imagined, each year they would say, ‘Well, you’ve got about 9 to 14 months to live.’ And after about 10 years, I said ‘Stop doing that.’” And I now recognize that what was happening was sort of two things: a complete lack of data and understanding to know how to come up with a definitive diagnosis with me that stuck, but also, it’s pretty clear I was morphing, my tumors were morphing in response to the various things that were happening. So it’s true that I didn’t probably have one diagnosis that made sense during that period of time.
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The next PowerPoint slide appears on screen.
Heading: Good, Consistent Advice from Many Leaders in Genomics…
Left column:
One thing Rehm would definitely not recommend? Spending all of the Precision Medicine Initiative budget—President Obama proposed 215 million dollars to start—on sequencing.
“I would argue you need to spend more time focusing on the effective collection of phenotypes to then correlate that [genomic] data to,” she says, advocating for smaller pilot projects to successfully link genotypes and phenotypes before sequencing the whole cohort.
The best way to design infrastructure, she believes, is to define projects where you are seeking answers to specific questions, and use the questions and expected answers to drive the building of infrastructure.
Right column:
A photograph of a woman standing in front of a display of the human genome. Below this is a diagram with the ClinGen logo illustrating how patients, clinicians, laboratories, and researchers share genetic and health data to answer ClinGen’s critical questions:
- Is this gene associated with a disease? (clinical validity)
- Is this variant causative? (pathogenicity)
- Is this information actionable? (clinical unity)
All of which lead to building a genomic knowledge base (ClinVar & other resources) and improved patient care through genomic medicine.
At the bottom of the slide is the following credit: From Bio-IT World, “ClinGen and Lessons for the Precision Medicine Initiative,” by Allison Proffitt, June 10, 2015.
Mr. Dishman continues:
So, as we think about that, I’ve been talking to lots of folks within NHGRI and people that you fund, even back when I was on the working group and we were hearing from people around the country. So there’s been some good consistent advice from many leaders in genomics, which I thought was summed up well in the Bio-IT World piece that Allison Proffitt wrote, and it says, “One thing Rehm would definitely not recommend? Spending all of the Precision Medicine Initiative budget—President Obama proposed $215 million to start—on sequencing. ‘I would argue you need to spend more time focusing on the effective collection of phenotypes’”—something that we’re exploring and trying to execute. And towards the bottom, and we should “use the questions” that we come up with and “expected answers to drive the building of the infrastructure as we go.” That theme of learning and iterating as we go is one that I certainly bring from industry and it’s one that I’m going to describe to you here in terms of the mindset and the processes that we’re setting up to be able to go for the long run of building out this program.
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A new slide appears, which reads, “A review and status update of PMI CP.”
Immediately another new slide appears.
Heading: The Precision Medicine Initiative® Cohort Program
- One million or more volunteers, reflecting the broad diversity of the U.S.
- Opportunities for volunteers to provide data on an ongoing basis
- Data shared freely and rapidly to inform a variety of research studies
At the bottom of the slide appears the PMI logo, comprising silhouettes of a diverse group of people of various genders, ages, professions, and abilities. A silhouette of a woman stands in front of the others and is highlighted.
Mr. Dishman continues:
So I’m going to give you a quick review. Eric shared with me previous slides, so I think most of you know the basics. I’m going to run through this part quickly, but then I’ll dive down a little bit more deeply on the sort of status of things right now.
So I believe you know that the Precision Medicine Initiative Cohort Program is part of the broader Precision Medicine Initiative. Our piece is to, you know, 1 million or more volunteers reflecting the broad diversity of the U.S., opportunities for volunteers to provide data on an ongoing basis, and data shared freely and rapidly to inform a variety of research studies. That’s sort of it, in a nutshell.
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The next slide appears. Heading: Mission: Accelerate Science & Breakthroughs that DriveTowards Precision Health for All!
Below this is a circular flow chart in which Questions, Problems, & Hypotheses lead to Capturing, Secure, Clean, & Share Data; which leads to Unleashing Science & Diverse Scientists; which leads to Translate Into Action, Practice, & Meaning; which in turn leads back to Questions, Problems, & Hypotheses.
Mr. Dishman continues:
The mission—and this is not a formal mission—but as I came, I arrived on a Monday and they said, “Oh, you need to do some White House prep and briefings because we’re about to announce the first 33 awards”—it’s really eight huge awards, but to 33 organizations—“and you need to lead a three-day workshop starting Wednesday because they’re all coming to town.” So I said, “OK, all right, I can do this. I know where the bathroom is, that’s a start. And how to park, and then we’ll do this.” And I wrote on that first day when we all came together to present, for the first time, the initial awardees. I said, really, this mission is to accelerate the science and breakthroughs that drive precision health for all, and using the broader word “health” because medicine is a key part of it but it’s not the only part of it.
And if you think about sort of all the different—you can go back and read all the different models of how science works or how inquiry works. But you know there’s different versions of it, but it all comes down to three or four stages of questions, problems, hypotheses. How do we capture data? And are kinds of data secure, and clean it and share it because it’s a very complicated thing to do. How do we unleash science and diverse scientists on top of that data, and how do we then translate that into action, practice, and meaning? As you’ve just been discussing, the challenges of getting people to accept anything that’s non-human data as they’re informed. And if you think about that knowledge turn, a turn that comes from economics that used to be the way they would evaluate the potential growth of a company, uh country, was based on its knowledge turns, its ability to ask questions and have its workforce walk through the cycle—that takes a certain period of time.
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The circle and arrows at the center of the flow chart shrink.
Mr. Dishman continues:
And when we succeed, we’re trying to shrink the period of time it takes to move through those and let lots more people actually move through those cycles by building the baseline infrastructure of both a million people who are going to trust us, to engage with us, and provide different kinds of data over time as well as different kinds of data types.
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The next slide appears. Heading: A Transformational Approach to Diversity
Reflecting the country’s rich diversity to produce meaningful health outcomes for historically underrepresented communities.
Next to this are several photographs of people of various ages, races, genders, and ethnicities, including a man in a wheelchair, a group of teens, two children and a senior man blowing out candles on a cake, a mother holding a baby, a man of mixed ethnicity, and an African American woman. Superimposed over these photographs are the labels people, health status, data types, and geography.