Male Speaker: Sure. So please join me in welcoming today’s
speaker and my longtime colleague, Dr. Eric Green.
[applause]
Eric Green: Thank you, Andy, it’s a -- it’s a pleasure
to be here. As Andy may have mentioned in week one of this series -- the series actually
started with Andy and I and then eventually bringing Tyra Wolfsberg into this picture.
I’ve -- starting back in 1995, this is like the 12th time I’ve given some version of
this lecture. I should have by the way immediately thanked Tyra and Andy for organizing this
iteration of it, the 2016 version. They included my name on that, which was very kind of them.
It’s purely honorific. I really had nothing to do with organizing this, and you should
thank them for all the logistical aspects of bringing this year’s series together.
I’m more of a legacy left as a named co-organizer, but it is -- it is a pleasure to be here.
I will say that the title of my presentation sort of is very broad with genomics and the
genomics landscape as I see it today. I will point out I’m going to emphasize heavily
the human genomics landscape for reasons that will become pretty obvious, but I’m going
to limit this a little bit, especially towards the end. I should also immediately point out,
as you might imagine, I’m fairly boring, so I have absolutely no relevant financial
relationship with commercial interests, and the other thing I should point out is that
major aspect of what I want to try to accomplish today is really context setting, both for
those of you sort of using this series as an opportunity to learn a lot of genomics
for the first time but also as a framework to some extent for the speakers that will
follow. In fact, usually I give the lead-off talk in the series for that exact reason,
and my schedule just didn’t allow me to go last week, but you will see as I go through
why this is very much of a context setting talk.
I’m going to first start off giving you some historical context for genomics as a
backdrop and then talk about some of the major achievements that have happened since the
Human Genome Project ended 13 years ago, but really emphasize paint the landscape of what
the human genomic circumstance is today and importantly beyond, and as I said, really,
my goal for this as much as anything besides giving sort of a foundation of information
about genomics is to really help you see how the other speakers fit into this landscape
that I’m about to paint.
So, in terms of a historical context, if we really go back, even before genomics was brought
about as a discipline, it is really important to -- and think about the series of major
historical figures and their important contributions to help really fertilize the ground, if you
will, of which genomics was able to grow out of. I could probably spend the whole talking
about that. I’m just going to give what I think are the -- some of the key highlights
to think about. Obviously, Mendel deserves tremendous credit understanding and elucidating
some of the basic laws of inheritance. Of course, he had no idea where those -- that
inheritance was actually coded for or where the information resided. Some of the clues
started to come about with Miescher’s work in the late 1800s when he actually discovered
DNA as a -- as a molecule, but it really wasn’t until Avery and colleagues’ discoveries
of the 1940s that demonstrated that DNA must be this inherited material, which therefore
focused a lot of interest on DNA as an information molecule, setting the stage brilliantly for
what Watson and Crick were able to accomplish in 1953.
In fact, I would contend that the Watson-Crick discovery of the Double-Helical structure
of DNA in 1953 was arguably the most important single biomedical research discovery of the
last century. I would certainly contend that the paper shown to the left was the most important
publication of the last century because what happened with the insights brought about by
the knowledge of the structure of DNA really set the stage for then really figuring out
how DNA was the information molecule of life and how it therefore encoded all the life
processes, if you will. That was of course in the 1950s. It’s then in the 1960s some
of the key things we saw were -- for example, the elucidation of the genetic code. Those
who don’t realize much of that work was done right here on this campus, in fact just
outside of this auditorium is a small museum exhibit talking about some of Marshall Nirenberg’s
work in elucidating this genetic code that we all now take for granted as the key translator
table of going from DNA sequence to protein sequence. Better and better tools started
to come about in the 1970s and particularly the 1980s, leading to DNA cloning, where for
the first time we were able to actually isolate and clone and manipulate DNA in the laboratory
and even then being able to develop methods such as in the late 1970s coming about and
much improved throughout the 1980s to actually read out the Gs, As, Ts, and Cs within DNA.
So, this progression from Mendel all the way through molecular biology and DNA cloning
in many ways then set the stage for what transpired in the late 1980s, and what transpired were
the coming together of all of these tools and technologies to allow us to start thinking
about how to go and study in a more comprehensive way our genomes, and that gave birth to this
field called genomics, and you may not realize that that word didn’t even exist until 1987,
at least not in the scientific or literature. In fact, the first use of the word genomics
in scientific print came about in this lead editorial of a brand new journal called “Genomics,”
1987, where they talked about a new discipline, a new name, a new journal, and in the lead
editorial, they talked about this newly developing discipline of genome mapping and sequencing,
for which they had adopted the term genomics and put this into the scientific literature.
Eighty-seven was particularly relevant for me because it was the year I graduated as
an M.D.-P.h.D. student, reminding myself why I had never heard the word genomics once in
medical school or graduate school because it simply didn’t exist. So, it really is
important to emphasize we’re only sort of 30 years into this word of genomics as a discipline,
so it is a remarkably young discipline, and I think it’s prominence on the biomedical
research stage sometimes confuses us to think that, “Wow, it’s been around forever,”
but it really hasn’t. It actually is a very youthful discipline.
Now, of course, the reason the discipline was named and the reason that there was a
lot of attention in the late 1980s about genomics was because of this idea that was crafted
in the late 1980s and launched in October of 1990, this notion of a Human Genome Project,
this large, audacious, international project that aimed among a number of goals to read
out the three billion Gs, As, Ts, and Cs that constitute the human genome. It’s important
to point out, by the way, that we did have an odometer moment recently, October 1 of
last year, of 2015, marked the 25th anniversary -- 25th anniversary of the launch of the Human
Genome Project. Painful for me to think about because I was a trainee. I feel like it was
just yesterday, but it’s been 25 years since I was there, literally at the starting line
participating in the genome project on day one and involved in it throughout its entire
13-year span. In fact, it was remarkably successful, finished ahead of time, not in the original
15 years, finished in 13 years, and it’s now just sort of a key part of the rich history
of biomedical research.
I actually had the opportunity to co-write a prospective piece that some of you might
be interested in to commemorate the 25th anniversary of the launch of the Human Genome Project,
and I did it with the two individuals who have held the job I now hold, Jim Watson,
the original director of the institute I now lead, and Francis Collins previously was the
director of the institute before I became NIH director, and the three of us wrote this
prospective piece not so much talking about the science of the genome project but talking
about sort of how many legacy elements were left because of what the Human Genome Project
did in terms of changing big biology, if you will, so I put you to that -- point you to
that article if you’re interested in reading some of the historical aspects and importantly
the legacy elements of the genome project beyond the base pairs.
And, in fact, speaking about beyond the base pairs, we are celebrating this odometer moment,
this 25th anniversary in another lecture series. I’m going to put in a shameless plug because,
in fact, we have an ongoing lecture series that takes place right at this podium every
month, and in fact Thursday of this week, which I believe is tomorrow -- yes, it is
tomorrow, Ewan Birney will be speaking here at this podium at 2:00 because we’re bringing
in a series of individuals who were there, heavily involved in creating and executing
the Human Genome Project, including people like Ewan, who really came into prominence
at a very young age to help with the genome project’s end stage and now really used
that as a launching pad for his remarkable career. So, if you’re free at 2:00 p.m.
tomorrow, please come here. If you happen to miss it, of course we video tape this stuff
and make it all available on our genome TV channel of YouTube.
So, it’s been 25 years since the launch of the genome project, and just a little over
that since the beginning of this field of genomics, so to review things I thought I
would just talk about sort of what I think are the six key accomplishments or highlights,
if you will, of genomics in its first quarter century or so of existence and in reviewing
these six areas I want to also contextualize some of the signature efforts that you’ve
probably heard about, or if you haven’t, you should be aware of, that really have been
incredibly important for moving the field forward. A common theme of this will be the
-- some of these efforts are -- most of these efforts are big and they’re audacious and
they’re very much cast in the kind of style that the Human Genome Project was cast in
because it was remarkable what it accomplished, and in fact highlight number one on this six
highlights I’m going to give you is that the human genome was sequenced for the very
first time by the Human Genome Project, and that absolutely is the number one highlight
in many ways of the past 25 years, and it’s a highlight both because it provided such
incredible information about our blueprint, which has launched so many other efforts that
I’m going to talk about, but it’s also important because it launched a whole lot
of other areas, use of genomics as a pivotal tool for advancing those fields. And in fact
this is a subset of those areas, but everyone wanted these areas, had been remarkably advanced
and enriched because genomics has found their way to be impactful in these areas. And every
one of these could be a talk in and of themselves, and probably an entire symposium. I’m not
going to talk about any of this because as I -- although Julie Segre will talk about
and she’ll be one of the speakers in this series on May 18, and will in fact point out
an example of how genomics is really in many ways completely changing the face of diagnostics
when it comes to infectious agents.
My emphasis will be more on human health and human disease and medicine because that’s
the one that in particular is of greatest relevance for us here and NIH in particular
for my institute, the National Human Genome Research Institute, and as Andy mentioned
in the introduction, I’ve been at the institute for about 21 years. I’ve been in the field
since the beginning, but six years ago I became the director, and while I certainly was involved
in thinking about these things when I was in my previous roles at the institute, certainly
when I became director I became increasingly laser focused on thinking about how to facilitate
the application of genomics to health, disease, and medicine, framing it around the concept
of genomic medicine as sort of the ultimate goal, if you will.
By genomic medicine, I mean this as a medical discipline that involves using genomic information
about an individual as part of their clinical care, and of course, important other implications
of that clinical use. This, of course, is largely synonymous with other terms you’ll
hear: individualized medicine, personalized medicine, and later on we’re going to talk
about precision medicine. I would say our framing of this is really very much limited
to genomic information as a subpart of some of these other ways to frame it, but we’re
really going to stay focused on the genomic information as a means of individualizing
care.
So, in thinking about what we want to do as a field, certainly what my institute wants
to do as a research funder, we really think about this as a progression where we need
to traverse a series of accomplishments to eventually see genomic medicine become a reality.
We are grounded heavily in the starting line of the Human Genome Project. That’s what
we really think started all this, which in some ways means the starting line was 13 years
ago because we think once we had a sequence of the human genome, that really set up the
circumstance for accomplishments that I’m about to tell you about, and eventually we’re
going to realize genomic medicine, and we’re going to see the practice of medicine changed
because of the use of genomic information, but this is not going to happen overnight,
and it’s not a sort of a simple, one kind of project effort. It involves many steps.
It’s going to involve many countries. It’s going to involves thousands of scientists.
It’s going to require an amazing amount of creativity, and we can’t even anticipate
all the things we’re going to need, although we can anticipate some of them, and I think
we’ve done a lot in the last 13 years.
I also want to emphasize this is going to require a community, a highly interdisciplinary
community of scientist, health-care professionals, and people in all -- all people that are touching
health care in some form and science and research, and they’re going to all have to be very,
very highly collegial, and we’re going to have to be doing this together for a long
time. The analogy I’ve been using is one of a marathon. I mean, really, we’re going
to have a lot of people running shoulder to shoulder. It is not a sprint, and we have
to be in this for the long haul because there’s a lot complexities, some of which I’ll be
unpacking throughout my talk. But that’s a tall order. I mean, you know, sort of thinking
about how do you go from sort of the base pairs provided by the genome project to actually
change how we take care of patients at the bedside or maybe if you prefer the metaphor
from double helix to human health, you know, that’s going to require some pretty clear
and important strategic thinking around this. And there’s one thing the genomics community
I think is really good at is I think we’re really good at being strategic, and we’re
also very good at sort of organizing how we want to pursue things, and we’ll sort of
in the fabric of what we did during the genome project. In fact, the way we accomplished
the genome project was to every couple of years develop a new strategic plan that would
guide the next few years and be willing to, by the way, rip up a strategic plan when it
seemed outdated after a few years and come up with a new one.
So, since that was sort of -- sort of culturally what we did to sort of map out the next set
of things that needed to be accomplished, it probably wasn’t surprising that literally
the day the genome project ended 13 -- nearly 13 years ago, our institute published following
an incredible amount of consultation with the community a strategic plan for the future
of genomics immediately starting after the genome project was completed, and it served
us well, I will tell you, for a number of years, but it -- but like many things, when
there’s scientific advances it doesn’t serve you well forever because new opportunities
come up, and in fact we saw those new opportunities by around the end of 2009, 2010 in particular,
and we recognized it was time for a new strategic vision or updated strategic vision, in which
we put out, once again co-authored by members of the institute, again involving a lot of
strategic consultation with the community.
The big difference for the first time with our 2011 strategic plan, which is the one
we still use, was the incorporation of genomic medicine as a key element, as a key goal,
as I’ve articulated to you, in fact putting it in the title of that strategic plan. So,
for those of you who have not read this, it is, you know, five years old, and we’ve
really looked critically at it very recently and actually still feel it has a very, very,
very good shelf life in terms of just still being very fresh and robust. I guess I’m
not allowed to give out required reading for this class, but I will tell you that a lot
of things that we say in the strategic plan will be on the test, so for those of you who
really want to know what’s on the test at the end of this -- what, three people just
left because they thought there really is a test. They just dropped the class. No, I
can’t make mandatory reading, but I would strongly encourage you, if you’re here to
learn about genomics, this would be a great article to read. Even though it’s 2011,
it’s still quite relevant to everything I’m talking about and many things I don’t
have time to talk about. If you want to quickly download it, you can get to the PDF immediately
by going to the URL, but don’t read it while I’m talking because I have a lot of things
I want to cover. You can read it afterwards.
But let me just give you a general overview of what the strategic plan describes as a
-- as a framework because in fact the framework that we put forth in this serves as an organizing
principles in many ways for almost everything we’re doing at our institute, and I think
in many ways is a nice framing of many of the things going on here in the entire field
of genomics because what we heard during our strategic planning that led up to the 2011
publication was that it was finally time for the genomics community to be more specific
and more sophisticated in describing how they were going to actually go from basic genomic
information to actually changing the practice in medicine. It was always a thing we would
say during the genome project, you know, “One day, this will be really important for how
we practice medicine,” but now, 2011, it was time to actually start to describe a research
agenda that would inch you closer and closer to actually changing the practice in medicine.
It was important to organize the thinking and programmatically important to know how
you were going to develop research programs that helped with this progression from left
to right, so at the end of the day, we found that we could describe everything we needed
to do or most things we needed to do in five major bins of activities or domains, as we
called them.
Let me introduce you to those domains. I mean, one of the -- the first one was saying we
were very familiar with understanding the structure of genomes, largely what we had
done during the genome project and the immediate period beyond, but also recognizing that we
also needed to understand the biology of genomes, how those Gs, As, Ts, and Cs did all of their
work, and that was an important thing, ongoing, but was a domain of research activities, and
with knowledge then about how the genome works, it provides you opportunities to use genomics
to then understand the biology of disease. How is it that changes in our genomes influence
our health and well-being? And having a clear domain focused around human disease was very
important. Obviously, if you start to get insights about human disease, it provides
you the opportunity to think about how to advance medical science, and that would involve
clinical research that would eventually give you an ability to think about how to use genomics
to maybe have a more sophisticated approach to medicine, but just because you have better
ways of practicing medicine doesn’t necessarily mean you’ve proven that you’re going to
change how well health care works, so we also sort of put down as a domain of responsibility
in many ways for doing research to eventually demonstrate that you can actually improve
the effectiveness of how you’re caring for your caring for your patients using genomic
approaches.
So, these five domains really do represent what we think about, and I think as I will
now continue to describe my highlights of the last quarter century, you will see how
we are moving from left to right on that progression through these series of domains, eventually
finding ourselves thinking about medical science and hopefully eventually improving the effectiveness
of health care. So, with that as a backdrop, let me continue with my highlights of the
last quarter century of genomics. Well, we sequenced the human genome for the first time
in the Human Genome Project, but, while that was incredibly satisfying, we were thinking
about one day sequencing patients’ genomes, and to do that we needed to make sure that
we could cost-effectively and highly accurately sequence people’s genomes, not just once
by many, many, many times. Well, to accomplish that, we needed to very much reduce the cost
of sequencing. The good news is, we’ve done it. In fact, the cost for sequencing the human
genome has been reduced nearly a million fold over -- since the Human Genome Project’s
first sequencing of the human genome.
Now, that didn’t happen by accident, and in fact our institute deserves, I think, some
credit, although not exclusive credit, but some credit of recognizing that this was pivotally
important for the -- what needed to happen once the genome project was completed. And
in fact in the strategic plan that we wrote and published the day the genome project ended,
we said a lot of things in that strategic plan, but one of the things of relevance here
is we talked about “…technological leaps that seemed so far off as to be almost fictional
but which, if they could be achieved, would revolutionize biomedical research and clinical
practice.” And we gave as an example -- we gave several examples, but the key example
relevant here was -- an example was, “…the ability to sequence DNA at costs that are
lower by 45 orders of magnitude than the current cost, allowing a human genome to be sequences
for $1000 or less.”
So, here it was. The genome project ended, and we put our names on a nature paper that
said, “We need to now go out and figure out how to sequence a human genome for $1000.”
It was a rather audacious claim, considering that day marked the final day where we had
finished sequencing the first human genome, but when we added up all the costs associated
with sequencing that first human genome, it came in at something like $1 billion, and
people will argue about whether it was $600 million, $700 million, $800 -- I just round
it up, roughly $1 billion, and here we were on that day proposing, “Oh, we just need
to knock six zeroes off of that figure and eventually come up with a thousand-dollar
genome,” and while it was audacious, it was catalytic. Now, meanwhile, it was catalytic
because we decided as an institute to put out a major granting program, and that major
granting program aimed to collect great ideas from creative scientists around the world
actually. Their goal was basically to get rid of this, because this was the factories
that were used for sequencing that first human genome, which was one of multiple factories
that were used for sequencing the first genome, and we wanted creative people to come up with
some fancy-schmancy way to sequence DNA, shown here in iconic form, as something magical
and revolutionary that would knock six zeroes off of that figure, and the good news is not
only did we get creative scientists to come in, and we gave grants to, and they did remarkable
high-risk things, many of which paid off. The good news was that the private sector
met us as partners, and the private sectors recognized this was what was important, too,
and there was a considerable amount of private sector investment as well on in many cases
commercializing things that came out of our scientists, our funded scientists’ efforts.
And the rest is history. I mean, it’s 13 years, but the rest is history. This has been
chronicled in nature articles talking about our -- this program and these efforts, and
the graph on the left is sort of an iconic graph that we put out, and we have a whole
webpage that catalogues and has been cataloguing the cost of sequencing by -- especially by
the centers that we support for big, large-scale sequencing, and in green is the cost of sequencing
on a logarithmic scale, the cost of sequencing a human genome. It’s just fallen precipitously,
and it’s because of fancy, wonderful new technologies such as those shown on the right,
and it’s not just the fact that we are getting really close to a thousand-dollar genome because
that’s one thing we are, and that’s where it’s almost a million-fold reduction.
It’s that it’s also how quickly we can sequence genomes, so to give you a perspective,
you know, that first human genome sequence as part of the genome project cost something
like a billion dollars, but it also took six to eight years of active sequencing. That’s
a long time to get a sequence, but today, using new methods, only a few thousand dollars.
We’re getting close to a thousand, but you can also do this in about a day or two, and
in fact there’s many believe using new protocols we will get this down to a day or less than
a day in the coming year. And the other thing that’s really exciting about this and these
technologies is that whatever you think we use today, it probably won’t be what we’re
using two or three years from now. It is very much like sitting in an airport where you
know you have a lot of nice planes on the ground ,but you look over on the horizon,
and there’s more planes coming, and there’s another and then there’s another one. I
happen to know there’s some really cool technologies that are sort of early stage,
aren’t ready to be commercialized, but, you know, they’re about the second or third
plane in, and probably within about a year or two they will be supplanting, I think,
some other -- the technologies that we currently use today.
So it’s a very, very exciting time, and it’s not letting up, and in fact there’s
a lot of excitement over nanopores. That’s the latest rage, and devices such as that
shown here on the bottom right that literally plug into the USB port of your laptop and
can sequence DNA and maybe be able to sequence a human genome within a day if all things
work out. Remarkable exciting developments, and we just sort of stand back and watch this
happen, and so there’s a lot to be described in these new technologies, and in fact one
of the most popular lectures in the series of late has been Elaine Mardis, who’s kind
enough to fly here and give a lecture, a real leader in sequencing technology, and she’ll
be here May 25, and it’s not a lecture you want to miss because I think it always gets
the highest YouTube hits if I’m correct. And she will -- she will describe what I just
gave in three slides and she’ll talk about it for over an hour, and there’s a lot to
talk about in DNA sequencing technologies.
I do want to point out because some of this talk is a little philosophical how important
these technological advances have been for the field of genomics, you know, and in the
history of science, you’ve often seen major inflections influxions in scientific progress
because of technological advances. I’ll give you a few. You know, needless to say
the telescope sort of changed the face of astronomy. The microscope changed the face
of cell biology, and certainly devices such as the shown on the left, various radiographic
methods really changed the face of radiology as we know it, and trust me, that’s exactly
what is happening with these new instruments. Technologies for sequencing DNA in that last
13 years have completely changed the face of genomics. In fact, I think it’s changing
the face of biomedical research as genomic sort of permeates across the entire enterprise
of biomedicine.
So, that’s a great accomplishment. Elaine will tell you more. Well, that’s great.
Now we can sequence genomes quite inexpensively, and that we particularly want to do because
we want to sequence many people’s genomes, and now we can afford to do it, and it’s
-- and the reason we want to do it is we’re not just interested in that first reference
sequence. That just sort of gave us a hypothetical individual. It wasn’t even one person. It
was a patchwork of people. It was a reference. We in fact want to sequence hundreds, thousands,
tens of thousands, eventually hundreds of thousands of people because we want to figure
out how all of us are different. The good news is that even so far we’ve already sequenced
now tens of thousands, and I probably have to change this slide fairly soon. That will
have to say hundreds of thousands of human genomes have actually been sequenced in one
form or the other worldwide, and that is providing us a very remarkable opportunity to understand
how we all have different blueprints.
So, let me just remind you any one -- any two of us differ about every one out of a
thousand bases as you go across all the letters in your genome. Those differences are variants,
at least depicted here a single nucleotide variance, a G where other people might have
a C or an A or somebody else might have a T, and so forth, and so we have millions of
those variants compared to any referents or compared to the person sitting next to you,
but the great majority of those variants are inconsequential from a biological, a medical
perspective, but a subset are very, very consequential, and we want to know those, and by the way,
it’s not that you all have your own private set of millions of variants and nobody -- most
of the variants you have are very common, and other people have -- probably other people
in this room have, and that makes it a situation where we could imagine if their very -- relatively
common, if we just sequenced enough people, we could develop catalogs of those variants,
and then if we had catalogs of those variants and we had really good methods, we could probably
start figuring out which ones are consequential and which ones are not and which ones might
be not so good variants, might confer a risk for disease and which ones might be good variants
because they maybe protect you from a disease. They may be associated with some other positive
attribute.
And so, as a result, having sequenced that first human genome, there was remarkable motivation
to start cataloging common variants in the human population. You might have heard about
the desire to get single nucleotide polymorphisms and indeed -- or SNPs -- and the first effort
was something called the SNP Consortium, but that quickly, once it started to get some
traction, gave rise to something called the International HapMap Project, which attempted
to not only catalog these SNPs, these single nucleotide variants, but also to help us figure
out how they relate to one another on blocks of DNA called haplotypes, on human chromosomes,
because it turns out that not all these variants just sort of go all different directions as
they’re inherited, but rather they have neighborhoods of variants that have many variants
in a big block of DNA on a chromosome -- tend to stick together as they get inherited from
one generation to the next. And so through a series of publications, the last one being
in 2010, a lot of information about SNPs, single nucleotide variants and their haplotype
structure, was elucidated and shared with the biomedical research community.
At right around 2010 or even a couple years before then, new methods for sequencing DNA
came about that allowed us to really accelerate the pace of discovering variants in the human
population and that gave rise to the signature project, the 1,000 Genomes Project, which
was another audacious, large, international effort like the HapMap Project and like the
Human Genome Project, to catalog the most common variants across the world actually
and you can see from a collection of -- by the way, a lot of times in genomics we overachieve
and so we originally named the project 1,000 Genomes and quickly overachieved. So over
2,500 genomes were sequenced in the end, collected from 26 populations in the world, initially
described in this “marker paper,” as they’re called, in 2010 and then finally culminating
in this remarkable paper coming out last October that’s sort of the final paper -- final
major paper of the 1,000 Genomes Project, and what’s remarkable about their effort
is that where once upon a time when the Genome Project ended and we had information about
maybe thousands, tens of thousands maybe of variants that we knew existed in the human
population at specific points in the genome, 1,000 Genomes had sort of gotten us up to
a much higher threshold.
In fact, they got us to the point where there are about 90 million places in the human genome
we now know are variant across the human population and we know the variants that sit at those
particular sites. So we went from tens of thousands to nearly 90 million variants of
sites that we know exist and that gave rich, rich, rich catalogs of information that could
then be used for scientists to test which of those variants are important, and so Lynn
Jorde -- I will once again take the last three slides I gave and unpack it in much greater
detail when he is here on April 20 talking about population genomics and some of these
efforts that I just quickly described to you. Now the other interesting thing about the
ability to sequence tens of thousands of human genomes is it begins to give us insight about
what any one of our genomes look like, because one of the things we’re always curious about
is when we eventually get to this point of using genomics to take care of patients, we’re
going to want to know what is a typical patient’s genome, what’s it like, and what can we
learn from it. So we’re starting to learn this and a lot more, but I just thought it’s
fun numbers to put in the back of your head: what does your genome look like by the number
and if you ever get your genome sequenced you’ll want to know some of these numbers.
I mean for example, you have six million nucleotides roughly in your genome, right, because there’s
three billion as the reference sequence, three billion nucleotides, but you have two genomes,
right? You got one for mom, one for dad, so when we sequence a person’s genome we’re
actually sequencing six billion nucleotides or getting information on six billion nucleotides.
But a typical person -- on average when you sequence there are six billion nucleotides
-- they have about three to five million single nucleotide variants and if you do the arithmetic,
that’s about what we expected. So compared to the person sitting next to you, there’s
about three to five million differences between your two genome sequences and as I told you
earlier, most of the variants you have, in fact the great, great, great majority of them
are common. We already know about them, they’re in the databases, you could open up a browser
and go to a DDA to resource and you will find that variant, but that’s not all of them,
because about 150,000 of those variants, a minority of your three to five million, are
not in databases yet.
So every single time we still sequence a new human genome, we come up with new variants.
Those are the very rare variants, but they’re still worth having and we keep collecting
them and actually what’s very interesting is that when we sequence a given person’s
genome, on average we’ll find about 60 such variants that did not exist in either parent
and of course this is how new variants get created and in the process of creating the
two germ cells that gave rise to you, all that DNA had to be replicated and while there’s
a lot of DNA repair going on, there are some oops’ that happen and each of you is associated
with about 60 oops’. Most of those, I’m sure the great majority, are completely inconsequential,
but occasionally this is how you end up with a genetic disorder in a child that -- where
it didn’t come -- wasn’t inherited from either parent, because it came about brand
new and that would be an example of that, but again the majority of these differences
are completely inconsequential to your health and wellbeing. So that’s just a little aside
-- and a lot more being learned about how many of your repertoire of genes -- how many
times your genes mutated and broke and so forth and we’re learning a lot about that
for the average patient.
Well, having had sequenced the first human genome, developing ways to sequence genomes
cheaper and cheaper and then going out and actually sequencing many, many genomes and
getting lots of knowledge about variation in the genome, it immediately started to -- already
was happening in parallel -- wanting to know, okay, well when you have a sequence difference,
what does it do? How does it influence the viability or any -- in the development -- any
aspect of a creature, in this case of a human? So to do that we really needed to understand
how it is that the human genome actually functions and I would tell you 13 years having -- following
the end of the Genome Project, there have been profound advances in understanding how
the human genome actually functions. And let me just remind you that that was not what
the Genome Project was supposed to do. This is what the Genome Project was supposed to
do and they did it, they basically read out all these letters -- of course this is only
a subset of what the Genome -- this is only .00001 percent of what the Genome Project
did and it’s a complicated language, that is very hard to sort of immediately grasp
where is the important parts, and I will tell you that when the Genome Project ended 13
years ago, our tools for actually interpreting the three billion letters were really -- really
weren’t that great, they were quite nascent.
They were not bad for genes but for understanding non-gene -- the functional parts of the genome
that are not genes, were actually quite weak and we had a lot of work to do, but we did
know a thing or two about genes and in fact we knew that genes had introns and exons and
we knew that DNA got made into RNA and that that RNA could be alternately spliced and
you can get different gene products as a result of that and we were armed with that genetic
code I told you about earlier, so at least when it came to the protein-coding genes we
could look up and figure out how they made proteins. So we were pretty good shape for
genes and so we went to it -- and when I say “we” I mean the community went to it -- and
went through and quickly highlighted all the parts of the human genome that looked like
they were genes, acted like genes, therefore probably were genes for the most part and
at the end of the day that only accounted for about one and a half percent of the letters
of the human genome and by -- although we still work on the exact number, it’s about
20,000 genes; much lower than we anticipated, but that’s the number.
What was interesting was that we knew there was a lot more functional stuff in there and
that we were surprised by how little of the human genome actually coded for genes, only
one and a half percent, and we knew there was amazing amount of other choreography that
had to be at play to figure out where, when, and how much genes were going to get turned
on and helping chromosomes function and all sorts of things and we knew we had to find
that stuff, all that functional stuff outside of genes, and it was interesting because at
the end of the day, you know, we would have felt we’d have really brilliant people on
the planet, brilliant scientists to help us -- but at the end of the day we actually looked
back in time to help be guided how we were going to figure out all the functional sequence
of the human genome and probably one of the most inspirational figures that influenced
us were not any of the people listed here, but actually someone that was listed even
before here and that was Darwin, and it was sort of interesting how Darwin really came
to be commonly discussed immediately when the Genome Project ended, because there were
so many things that Darwin taught us in his writings, you know?
One of his famous things that was at least attributed to him is, you know, ‘Not the
strongest of the species that survives, nor the most intelligent, but it’s the one that’s
most adaptable to change,’ and he hinted at the idea that something was going on through
evolution, that something was being changed to have sort of species survive and adapt
and eventually, you know, sort of deal with sort of the evolutionary progression. Of course
we now know that stuff’s what’s -- is the DNA. That’s where it’s all at and
that’s why a more contemporary scientist, genomicist, wrote, right around the time the
Genome Project ended, that “for the last three and a half billion years, evolution
has been taking notes,” and those notes are in the sequences of the genomes of all
these creatures and so I mention Darwin, I mention that quote, because what happened
when the Genome Project ended was a recognition that we needed to do lots of comparisons of
our genome sequence with other creatures to better understand how our genome sequence
works and we also recognize that we were just -- as a species, just one really, teeny little
twig on a tree of life of great richness and that buried in those notebooks in the DNA
sequence of these other creatures was lots of important information.
So that’s the reason -- and many of you probably recognize that’s the reason why
we went off and started sequencing lots of critters and their genomes and first we started
with laboratory models, mice and rats and companion animals like dogs and our closest
relatives like chimps, but in fact we needed to use the power of comparative sequence analysis
and sampling more broadly across the tree -- why we started sequencing lots of other
critters, selectively and strategically picked off of different trees. A [unintelligible]
is here, originally it was like 25 species, then it went to 100 species, now we’re -- well
over 200 species have been sequenced and we used all that rich data to basically start
asking questions like what sequences in the human genome are conserved across all mammals
or across all vertebrates or across all primates?
Because if they’re conserved that heavily, they don’t change over that many years of
evolution, they must be important, because evolution just has a way of going in there
and wanting to sort of change things if it’s not important and so that was the rationale
for moving beyond the human sequence and now sequencing many, many, many species and in
fact what that gave us was remarkable insights about where in the human genome are the most
conserved sequences through evolution, pointing to the sequences most likely to be functionally
important, and in doing that you end up with about five to 10 percent of the human genome
sequence -- five to 10 percent of the three billion letters -- are conserved across almost
all mammals and they’re almost certainly functionally important, but that’s five
to 10 percent of which the genes -- protein-coding genes, one and a half percent, is a minority.
So around five to 10 percent must be functionally important at a minimum and only one and a
half percent of that is protein-coding genes, which means the purple stuff is non-coding,
functional sequences, in many cases conserved as aggressively throughout evolution as have
been our protein-coding genes. Now what are these non-coding functional sequences doing?
Well, they’re doing a lot. Probably the thing we know the most about is that they’re
incredibly important in this complex choreography of gene regulation, all these elements, enhancers
and promoters, and silencers, et cetera, et cetera. That’s all these sequence elements
that are controlling these crazy things are going on of where, when, and how much genes
are turned on, and so that’s some of that, but we also know that it’s not just all
gene regulation.
I mean there’s important sequences that help package up chromosomes, important sequences
that help segregate chromosomes, important sequences that help replicate chromosomes
and in fact we know for certain that there’s all sorts of complexity in RNA and we have
really started to reveal a remarkable amount of function associated with non-coding RNA,
something we didn’t even know about when the Genome Project began and now that’s
very, very instrumentally important in many biological functions including gene regulation,
and finally I would contend we should just recognize that there are things we just don’t
know about in non-coding parts of the genome that are certainly functionally important.
We just haven’t found them and figured them out yet and just nobody’s written about
them in textbooks, but they’re coming and we’re going to figure this out and in fact
that’s a very high-priority area.
Oh, and the other thing that’s transpired in the last 13 years was a greater and greater
and greater appreciation for yet another way that DNA functions, not by directly having
the primary sequence confer function, but by having marks on our DNA put down that influence
how DNA functions. These are called epigenomic marks, alluding to the whole world of epigenomics,
and this involves chromatin and all -- and methylation and various modifications to DNA
and it just turns out that the same methods that you can use for sequencing DNA can be
adapted to read out the epigenomic marks in DNA. So now we have this incredibly strong
ability to read out the second genomic code, if you will, and in fact I’m going to turn
this whole topic over to Laura Elnitski, who’s going to on March 16 come and tell you much
more about epigenomics and also about gene regulation, the topic I was just talking about.
So a lot has happened in this arena but, boy, we also know a lot more needs to happen. It
actually keeps getting more complicated because I would say in the last five years we’ve
also realized that DNA is not just some innocent little linear molecule that lays out of the
nucleus, but rather DNA also takes on, in the form of chromosomes, three-dimensional
structures and that these three-dimensional structures also have some functional activities
going on with different domains interacting and so the whole world of genomes in three
dimensions is unfolding, again because of technological advances, that we can figure
out what those interactions are and that’s also a very exciting area of active research.
How do we do this? I mean how do we figure this out? What have we been doing to elucidate
genome function? Well, it’s not simple. It will involve, I will tell you, multiple
generations of scientists to help us fully elucidate the function of the human genome,
and it also involves a number of different elements, if you will. I mean I will tell
you to start off with that we recognize that like other efforts in genomics, Human Genome
Project, 1,000 Genomes and so forth -- this was the -- we needed a team of people working
on this to figure it out. That is why almost immediately after the Genome Project ended,
we launched a program called the ENCODE Project. ENCODE stands for encyclopedia of DNA elements,
aiming to catalog all of the functional elements in the human genome like those color highlights
I showed earlier. It’s actually -- and it’s been going quite effectively; we’re about
to start the next phase of ENCODE over the next year or so. It also had a sibling for
a while called modENCODE for model organism ENCODE and what these efforts -- ENCODE, modENCODE
-- basically do is create GPS-like views of the genome.
So here you’re looking at one such view; this is just a view of the human genome that
you can get to public -- on a publically-accessible website, where you zoom in and out and look
at it and this is just an overwhelming amount of data generated by ENCODE. Some of it’s
laboratory-based data, some of it’s experimental data, but -- I’m not going to go through
it, but needless to say it has everything we could possibly imagine at the moment having
information about -- that region of the genome, with respect to where are the genes, where
are the conserved sequences, where’s transcription factors binding, what parts of DNA gets made
into RNA, where’s the chromatin opening up and so forth, and that data really provides
some insights about where’s the functional stuff and the challenge of course is synthesizing
all this and really getting a very strict interpretation for every nucleotide, what
the function is, but this is the kind of thing ENCODE has been doing and other efforts. There’s
a big epigenomics effort that went on similarly, has contributed to this as well and will continue
to do so.
This has involved not just looking at human DNA, but recognizing that model organisms
play a very important role in this; a very basic, science effort to understand genome
function that sort of traverses everything from yeast all the way to humans. I’ll tell
you that increasingly computational modeling comes in; this is not just about doing experiments
and more and more sophisticated computational modeling methods are needed to fully elucidate
how the human genome works and as I sort of alluded to earlier, there have been major
and will continue to need to be major advances in technology development to really figure
out all the nuances of the human genome and I will tell you that I -- John Quackenbush
will be here April 27 talking about many aspects of gene expression and systems biology and
I’m sure he will touch some of the things that I’m representing in this part, in particular
in this slide, and so I thought I would point that out.]
So where are we in interpreting the human genome? Well, I guess I could say a couple
things: I sometimes say that it’s multi-generational; we’re just in the first generation. My grandchildren
I would imagine will be interpreting -- helping to interpret the human genome sequence. You
know, I sometimes say that, you know, when the Genome Project ended, we knew about this
much about how the human genome works; now we know about this much and eventually we
need to know about this much, so we still have a long way to go. We’ve made a lot
of progress. Sometimes I just sort of joke and say we’re sort of at a SparkNotes view
of the human genome, for those of you who know what SparkNotes is. You know, maybe it’ll
get you ready for the test tomorrow but, you know, if it’s really a hard problem, we’re
going to need more than just SparkNotes and -- but that’s why we will need to keep hammering
at this. This is -- this is going to be a long haul part of the marathon, figuring out
how the human genome works.
Well, here we are. We have the sequence, we have our blueprint, we can easily and cheaply
get at other sequences, we’re beginning to understand how our sequences differ among
people and we are starting to get better and better insights about the function of the
genome. It is time to start thinking about what we originally thought was going to be
really valuable for genomics, and that is to be able to start applying our efforts to
understand human disease and I would contend there have been significant advances, especially
in the last 13 years, in unraveling the genomic basis of human disease and this certainly
deserves the fifth part of the highlight.
Now I will also point out and I’m going to programmatically just sort of mention that
I think our institute has been very helpful at figuring out how to use genome sequencing
to study human disease and that really grew out of our commitment to having our -- especially
our extramural research efforts be sustaining us, keeping us at the cutting edge of genome
analysis. So our largest part of our institute’s extramural portfolio is our -- called our
Genome Sequencing Program, which has had a progression starting with the Genome Project.
They were the groups that in the United States, in particular the NIH-funded parts -- that
were heavily involved in generating the first sequence of the human genome and then moving
on to help us figure out the sequence of these other genomes I alluded to and then working
on things like the HapMap Project and the 1,000 Genomes that I’ve mentioned and then
starting to focus on disease and initially working on cancer, which I’m going to have
more to say about in a bit, but the -- a very well-known project called the Cancer Genome
Atlas, which we did in partnership with the Cancer Institute, which is now in its last
phase, but then in particular of late and now moving forward, focusing on rare and common
diseases, simply asking the question: how can you scale up the use of genome sequencing
to be able to figure out the genomic basis of rare and common diseases, and so let me
just remind you, because this is really important to understand, the differences between rare
and common diseases when it comes to the underlying genomic architecture.
Well, so on the one hand we have rare diseases; these are diseases like sickle-cell and cystic
fibrosis, Huntington’s disease; rare in the population but it turns out they’re
quite simple, quite -- it’s an understatement, but in terms of simply involving mutations
affecting a single gene. These are monogenic disorders, also referred to as Mendelian disorders
-- after the famous geneticist Mendel, and here it is very clear there’s great potential.
There’s over 7,000 known rare diseases and remarkably, we recognize that with the new
sequencing methods, we can really accelerate the pace at which we can figure out the genomic
basis of rare diseases.
We’ve had a -- for several years and we just renewed the program -- a program called
the Centers for Mendelian Genomics, which is a series of centers that truly is doing
that, tackling these rare disorders in an industrialized fashion to try to figure out
the underlying mutations in these genes and they’ve made great progress along with the
other worldwide efforts, but you have to recognize there’s more progress to be made, which
is why we have these centers working so hard. There are about 7,500 rare genetic diseases
but we have found the gene, or the defective -- the mutated -- mutations underlying those
diseases for about 4,300 of those. Forty-three hundred is a remarkable number considering
that the day the Genome Project started, we only knew the genomic basis for 61 of those
diseases. So in a quarter century we’ve gone from knowledge about 61 diseases, to
knowledge -- molecular knowledge of about 4,300. That’s the good news. The challenging
news is we want to finish this up; we want to get the next few thousand and that’s
what this project is doing and so that’s been remarkable advance I think in the last
quarter century with respect to rare diseases.
Now what about the other class of diseases, common diseases, because common diseases are
what you’re much more familiar with? You know, rare diseases are rare in the population;
they’re certainly quite burdensome to families and patients, but in aggregate that’s not
what accounts for most hospital visits, clinic visits, that’s not what fill hospitals and
clinics. It’s common diseases that fill hospitals and clinics; this is hypertension,
diabetes, autism, Alzheimer’s, cardiovascular disease and so forth. When they’re common
in the population, the hard part is that they’re complicated. They’re complicated because
it’s not just a single gene, in fact it’s not even necessarily genes, it could even
-- because we actually believe a lot of it is non-coding functional sequences, it’s
multiple, multiple variants we believe scattered throughout the genome, with what is typically
a greater contribution of the environment, especially compared to rare diseases, and
so we knew this was going to be really complicated and indeed, it certainly has proven to be
pretty complicated.
This of course was why there was a big push once we -- that’s the reason why we wanted
to get all these variants cataloged, so that we could do studies to analyze these variants
in thousands of people and we could do this across the whole genome, genome-wide, and
we could try to associate known common variants with diseases like hypertension, diabetes,
and so forth. That’s what gave rise to the genome-wide association study idea, GWAS,
and GWAS was an idea that on paper everybody hoped would work and it did and it started
in 2008, was the first published genome-wide association study, and since 2008 there’s
been over 2,000 such studies that have come about and in fact there are now about 4,000
places in the genome that we believe have a variant in them that are conferring the
risk for getting the disease.
The problem is it’s just a risk, it’s not an absolute -- usually these are absolutes
-- these are not and it’s not telling you which variant it is; these GWAS studies only
tell you what neighborhood they live in, they only tell you a region of a chromosome. There’s
still lots and lots of variants there and so the good news is that we -- and again,
we’ve gone from sort of not knowing how to decipher this complexity, to having some
really good ideas of where we really need to hunt in greater detail, but there’s been
only a very few examples where we’ve actually gotten it down to a very specific variant
and known what that variant is doing and we need to do that thousands and thousands of
times for all these very important disorders.
So we need to -- actually what we’ve learned in the last five years in particular is we
need bigger and bigger efforts. That’s why we actually have now turned the attention
of our largest centers, in a new program which actually we just formally announced a few
weeks ago called the Centers for Common Disease Genomics and these are centers that are going
to teach us how to do this, we hope, along with other efforts in the world, where there
are -- we now know that you don’t just -- we need to completely sequence the genomes of
lots of people with a disease like hypertension or cardiovascular disease or autism and Alzheimer’s
disease, diabetes, and then lots of controls and you need to probably do tens of thousands,
we now are recognizing, and have very large datasets to analyze and then use very fancy
statistical methods to tease out which of the variants that are actually the ones conferring
risk. And so while we don’t have as much to report yet with common diseases like we
do with rare diseases, I would just say we are on a trajectory I think that’s going
to give us a lot of insights about -- strategically how we’re going to do this and hopefully
we will get a lot of new insights over the next five and ten years.
But I will tell you that all of these efforts, especially what’s going on now in efforts
like this where literally tens of thousands of individuals’ genomes are going to be
sequenced and analyzed, are going -- is just an immense amount of data and that’s on
top of all the other data, of all the other projects I’ve been telling you about and
-- oh, and by the way, I forgot to mention, before I go on: Dave -- with everything else,
a lot more will be said about rare diseases when Dave Valle is here on April 13 and a
lot more will be said about common diseases, in a much more sophisticated way than I did,
by Karen Mohlke when she’s here on April 6, but what all of them will tell you is that
-- and in fact all of you probably realize, is that the world we now live in, as these
wonderful technologies get us more and more data, is that we have the circumstance where
we are overwhelmed by the amount of data coming out of these sequencing instruments and -- in
particular and other new technologies and this is sort of putting us, in fact, into
a new circumstance where the bottleneck is not generating the information, the bottleneck
is analyzing it, and so this bottleneck really is one where these fancy new methods for sequencing
DNA are giving us a circumstance where reading out a genome sequence is not the hard part.
You know, the hard part actually is sort of progressing on and then figuring out what
to do with the information about the variants in our genomes. As you’re going to hear
from other speakers, you know, there’s issues just around hardware and having enough capacity
to store all this data. There’s issues of varying -- we need increasingly better and
better software tools and of course we need a workforce that’s able to do all of this
which is why some of you are here, is to help become that workforce, and that also has to
include taking it to the final stage of knowing how to take information, write individual
variants and knowing how relevant that is for individual patients. So as -- this is
why Terra [spelled phonetically] and Andy in particular dedicate three lectures -- talk
about the data analysis, the data science, the bioinformatics, whatever phrase you want
to use of everything I’m describing, because data analysis is actually sort of a big part
of all of this and in many ways is sort of the grand challenge that all of us are working
on.
So I’ve gone through five highlights of the past quarter century and then of course
as a recognition I’ve said nothing about medical practice, as if maybe there wasn’t
any highlights and probably a few years ago I would have been very limited in what I might
have been able to say, but I actually think that this is worth putting as a sixth highlight
of the first quarter century of genomics, because I really do believe there are vivid
examples of genomic medicine that are just starting to emerge. It is the tip of the iceberg
and there will be a lot more, but I think it is worth highlighting what some of these
are, because I really believe that we are seeing genomic medicine come into focus in
a fashion that actually -- even more exciting than when I spoke in this series a couple
of years ago and so I thought I would just quickly go through what these highlights are
because Bruce Korf will talk about it in much greater detail when he is here talking holistically
about genomic medicine, but I thought in particular you may want to hear especially from my perspective
-- as the director of the Genome Institute, what do I think are sort of the hot areas
in genomics and what are some of the programs we’re doing to facilitate advances in those
hot areas. And so I’m going to just sort of, again, go through highlights. I’m going
to highlight five areas that I think are the hottest ones in genomic medicine.
I’ll start with cancer because cancer is the hottest area in genomic medicine implementation.
I don’t need to probably tell a sophisticated audience like this that cancer’s a disease
of the genome and what happens in cancer is that mutations get picked up by normal cells
and eventually make those cells grow out of control to become tumors. But those mutations
are sitting in the genomes of these tumors and those genomes can be sequenced just like
a normal cell’s genome can be sequenced. And with better and better methods for sequencing
DNA we can open up these tumors’ blueprint, that’s genome, and read it out and begin
to catalog all the sequence changes. And that’s why efforts like I mentioned, the Cancer Genome
Atlas did exactly that.
What that also can do then is start to give better and better information to diagnose
cancer and perhaps to think about better ways to treat cancer. And there is a whole -- incredible
areas that I couldn’t possible represent. I will say that in terms of actually changing
the practice of medicine -- since I’m trained as a pathologist actually, I do recognize
the diagnostic potential is here, you know, as one example of many, you know, for many,
many decades most cancer diagnostics involved histopathology as the major tool. And so that
will continue, but I already have seen and for some kinds of cancer that that histopathology
is augmented by genomic signatures that in genomic profiles, if you will, of tumors that
come out of machines like this and other machines. And it’s here and now. This is not hypothetical.
It is absolutely here and now for some types of cancer. And if you don’t believe me,
just watch television, go to websites, and so forth. You will find a website such as
that shown on the top. You will find, and may have seen advertisements -- and I keep
seeing them all the time, they’re increasing -- on television, whereby -- from prominent
cancer treatment facilities talking about genomic this, genomic that, talking about
the DNA of cancer care and so forth. This is mainstream. It’s used for marketing because
genomics is absolutely here to stay with respect to cancer diagnostics and cancer treatment.
It is the lowest hanging fruit.
I think another low-hanging fruit is the world of pharmacogenomics -- pharmacology meeting
genomics -- recognizing that there is a reason why all of us respond differently. We respond
differently to everything. That’s -- I’m the guy on the left, okay? That’s me. My
children are like this, right? Actually maybe it was a bad example because that might imply
genetics that’s not in play here, so maybe that’s not a good example. But we all respond
differently to everything. What I really am getting at is not rollercoasters. What I’m
really getting at is medications. Every medication in this pharmacy, in this hospital, in CVS,
Walgreens, every one of those medications work, they just don’t work in everyone.
But, and in fact, they are really -- often, often don’t work. In fact, “Nature”
had an article about this which I thought was very interesting just recent talking about
the imprecise medicine. And here are sort of 10 very commonly prescribed medicines.
And the person in blue in each case is the person where the medicine works. And the people
in red are the number of people proportionally where the medicine doesn’t work.
Well, there’s a lot of reasons why the medicines don’t work, but a good part of that is different
ways that we metabolize drugs or how it affects us physiologically, much of which is due to
variations in our genomes. And so the idea underlying all of them -- and we are learning
more and more about this -- is that we can take individuals with the same diagnose, but
do genomic profiling of them, get genomic information on them, and figure out who has
variants that are going to make you a good responder or a not-so-good responder, or even
be a bad responder. And do that before you decide on what medication to give a person
or what the dosage might be, and so forth. So pharmacogenomics here and now recognized
widely as something and will increase substantially over the next decade, which is why Andy and
Tara have Howard McCloud coming here, a regular in this series, to talk exclusively about
pharmacogenomics on May 7.
Third highlight here and now, actually this building’s a great place to talk about it
in, is the use of genomics to do rare genetic disease diagnostics. The notion of having
disease strike from nowhere, individuals with conditions that nobody seems to figure out
what’s wrong them, but for a -- in many cases these individuals have major amounts
of resources spent trying to diagnose them, that the idea of just sequencing the genome
as perhaps giving a clue, just makes a whole lot of sense. So as “Nature” pointed out
in this article, disorders not readily explained by standard tests can sometimes be diagnosed
through genome sequencing analysis. And sometimes -- it’s about question to a third of the
time by today’s methods -- we’ll find out what the diagnosis is by sequencing a
genome.
And the notion of undiagnosed diseases, undiagnosed conditions really, has come to the fore -- actually,
deserving a lot of credit, activities taking place right here in this building. You know,
patients on these long diagnostic odysseys, going from doctor to doctor, medical center
to medical center, nobody can figure out what’s wrong with them. Shown here is Bill Gall [spelled
phonetically], who’s our clinical director at our institute, but also is the leader of
something called the Undiagnosed Diseases Program, which took place right here, started
here in this -- the NIH Clinical Center and really reduced to practice the idea of bringing
these patients in and having a rigorous clinical evaluation in addition to a genomic analysis
to try to see if you can figure out -- get a diagnosis, not that that yields a diagnosis
every time, but it does quite frequently and it has been a remarkably successful program,
and is here and now and is mainstream and, in fact, has just been expanded. Recognizing
its success, it is now a nationalized program. NIH has its pivotal role right here, but we
now have established through a common fund program called the Undiagnosed Diseases Network,
a series of other sites in the country who are doing similar work, as well. Here and
now, genome analysis as part of diagnostics for rare, often undiagnosed conditions.
Also here and now, in one case and in another case being contemplated certainly, is the
genomics of -- the package as I understand is called Genomics of Pregnancy. It’s actually
two stories in one. But genomics plays a big role now in pregnancy. There’s a sign -- it’s
a sign that I think is important to think about. Let me tell you each of these. The
here and now is this: you know, we’ve doing prenatal testing for many decades actually,
but we had to access fetal DNA. The way you access fetal DNA traditionally is through
invasive procedures like amniocentesis chorionic villus sampling. But with the new methods
for sequencing DNA are so exquisitely sensitive that now we have -- the community has figured
out how to basically access that DNA that gets shed into the maternal blood stream from
the fetus. And that cell-free DNA can be accessed by a simple blood draw, a relatively non-invasive
procedure. And so, the idea of doing non-invasive, prenatal genome sequencing and genome analysis
is here and now. I mean, there’s lots of literature about this. In fact, it’s winning
lots of prizes over the past few years because it is changing the face of prenatal diagnostics
and written about in the popular press, as well.
Recent data came out that’s just, I think, breathtaking. You think, “Oh, okay, there’s
a few people doing this. They get this blood drawn, they get this -- no, no, not a few
people worldwide, millions of people in the world. In an article last year, you can see
the rise in the use of non-invasive methods involving genome analysis by simple blood
draws. You can self-read DNA of the fetus. You can see an aggregate -- it’s well over
a million and well -- and [unintelligible] suspected to be well over a million in 2015.
I haven’t seen the numbers yet. Remarkable.
Here and now is actually reducing amniocentesis chorionic villus sampling substantially as
a result. Here and now prenatal testing using genome sequencing. Not so here and now is
this notion of the other end of pregnancy you get a baby. You have a newborn. And “Time”
magazine thinks that by 2025, everyone’s going to get their DNA mapped. I think they
meant DNA sequenced, but I’m not so sure. And I think we want to think about that. And
it’s an interesting notion but it brings in a lot of logistical concepts and challenges
and certainly a lot of ethical ones, as well. Do we want every child sequenced at birth,
have their genomic information carried with them maybe in their electronic medicine record
for life, not clear.
So we actually are studying this. We actually set up a program with the Child Health Institute
to study this, to get a research foundation to think about these things. We have a series
of sites that are now sequencing the genomes of newborns and asking how does it change
their care. And we’ll learn a lot over the next five years.
I will highlight one of these studies, in particular. One of these sites is dealing
with not healthy newborns. And here “Nature” talked about an article that an investigator
is doing, which I think is remarkable, Stephen Kingsmore, where has basically take acutely
ill children in the NICU -- newborns in the NICU -- where the doctors have basically -- have
no idea what’s wrong with the child, know the child will die within a matter of days,
and simply don’t know what’s wrong with them. And he’s reduced to practice the idea
of getting a small amount of blood and sequence their genomes in less than a day and getting
information that in some cases, not always -- but in some cases, gives insights about
what’s wrong, in some cases saving the children, saving those newborns. And in fact, I think
this is also going to become more commonplace for acutely ill children in the NICU where
they simply don’t know what’s wrong with that child to quickly try to get a genome
sequence. So another here and now.
The last hot area is hot not because it’s solved, but because it’s really important.
And it relates to the development of information systems that are connecting our knowledge
of variants to their clinical relevance. And I really want to emphasize that generating
a human genome sequence today is almost trivial. That is just not -- it’s not hard to read
out the six billion Gs, As, Ts, and Cs of a given patient. What is really hard is then
taking those six billion letters and rounding on the patient the next morning and having
any clue what those variants mean. I mean, we just don’t know -- the vast, vast, vast
majority of those variants we have no idea if they’re clinically relevant or not.
We need to fix that. There is a disconnect even between what we know in the scientific
literature and what a busy health care professional is actually doing in terms of trying to manage
the care of patients. So we believe that we need to create much more robust -- that’s
why it’s hot -- we need to create this acutely clinical genomics information systems, probably
ones that integrate nicely with electronic health records and also ones that deliver
very clear guidelines to health care professionals in a very simple way because they’re going
to be look this up on these kinds of devices and the busy workflow of a nurse, of a pharmacist,
of a physician’s assistant, of a physician. And that needs to be done in a very robust
fashion. The truth, though, is it doesn’t exist. So we’ve put together a research
network called the Clinical Genome Resource or ClinGen, which you can read about in this
paper or look at this website, which is basically trying to scientifically figure out how do
you build the knowledge base that could then be used by busy health care professionals?
And so we’re at early stages. We’re not even building it yet, we’re just trying
to figure out how do you take this explosion of literature about variants and which ones
are medically important and which ones do you act one, which ones do you not, and reduce
it to something that can be looked up quickly so that you can have the patient management
be done efficiently with knowledge of that genomic information. So that’s something
to look for, but it’s -- we’ve got a long way to go but we’re trying to facilitate
it.
So I want to transition before I spend the last 15 minutes talking about something else
and just point out that I’ve just described to you sort of a romp, if you will, through,
you know, a progression over the last quarter century that just started with saying this
is audacious is just getting to sequence the human genome. But now, I was actually thinking
about how are we actually going to use this information for clinical management like I
described in the last slide? And it actually is more complicated than that because what
we have found as a community of scientists that were mostly basic scientists thinking
about genome structure and function and evolution, now we’re confronting the ecosystem of medicine.
And the genomic medicine ecosystem, as it’s turning out, is really complicated because
anything you go to change medical practice, you start touching lots of things that are
really complicated. And that ecosystem is not healthy unless you think about all the
things.
So what do I mean by this? Well just think about health care delivery and reimbursements
and all the aspects of changing clinical practice. It’s like this -- it’s really complicated.
Parts of our institute, parts of the community are dealing with this. I’m not going to
talk about, I ‘m just going to point out to you that it’s now becoming much bigger
than we ever thought it was when we were just thinking about just the genome. You know,
there’s other aspects of this related to also education and genomic literacy. You know,
we need -- the language of genomics will be spoken as part of medical care. We need a
literate public -- patients to go in. And you can see here is starting to train the
next generation of what this is all about. We also have a whole profession out there
of physicians and physician’s assistants, and pharmacists, and nurses. They all need
to be literate in genomics. And how do you do that when they’re at mid-career, not
just when you’re getting them when they’re being trained. And we’re thinking about
that. And these have huge complexities. Oh and, of course, we never thought about this
stuff when the Genome Project began, but, you know, there’s a lot of regulatory oversight
associated with any practice -- any aspect of practicing medicine. And where genomics
meets that regulatory oversight, there’s a lot of stories and sub-stories there that
we’re dealing with.
So I’m just mentioning these, not that I’m going to talk about them, but just to point
out that it really is complicated and sometimes it’s almost daunting and overwhelming. So
as, you know, we draw nice graphics that get us over to actually changing the practice
of medicine, you know, there’s new surprises along the way and new mountains that we have
to climb. And I will tell you at times it can get very exhausting because all of a sudden
we’re dealing with the complexities of health care delivery on top of everything else and
you can get rather pessimistic at times. But as I transition to my last topic, I do just
want to point out I like this quote because sometimes when I show this graphic people
say, “Oh my gosh, you’re never going to do this.” And I just think that a pessimist
would have that view because a pessimist sees the difficulty in every opportunity. I think
our community of genomecists, you know, we see this progression and we see the ecosystem
and yes, it is daunting, but we don’t get -- we don’t think of it as pessimist, we
see it as this is a great opportunity. And I think this why we keep pushing forward to
see this progression become a reality, even when it really gets complicated.
So let me transition now. I’m going to spend the last 15 minutes on one last topic and
I think it is -- because it will talk a little bit about the future. And the future is a
reflection, I think, in many ways of the change of what we have seen in genomics, not about
the science that I have described to you for the last little over an hour, but even just
the relevance of genomics. It touches on even the ecosystem aspect I was just talking about
because when genomics started, you know, 25, 30 years ago, this -- it was a discipline
just involving biomedical researchers. You know, I was one of those geeks just worrying
about, you know, mapping and sequencing DNA. I think there was a pivotal transition when
the Genome Project ended as we recruited health care professionals to start to work with us
to think about how genomics might change. And we’re starting to see the fruits of
that. And the hot areas are real because health professionals are getting involved in thinking
about how to use genomics.
But I think the real change that’s going on how is that patients are coming relevant
in this conversation, and therefore friends and relatives of patients because genomics
is becoming part of the language of cancer care, of pharmacogenomics, of rare diseases,
and so forth, and you see it all the time in the news, on advertisements, in newspapers,
and so forth. This is part of society now. And there’s a lot of issues that become
relevant. I touched on some of them, like education. But there’s a lot of issues even
around public health. And so, we think a lot about the societal implications and societal
complications of genomics and think about some of the public health aspects of it, which
is why Colleen McBride was invited to come here on March 23. She’s going to talk about
more -- about some of these things.
But because this is becoming so relevant for all of us and seeing the great potential for
this, I did think -- I thought -- I would just use the last minutes to just update you
about some breaking news. It’s not that breaking anymore but there’s a lot happening
here. It was particularly breaking when it started in June of 2014, because involved
this guy. I hope all of you know who this man is. And besides being President of the
United States, this guy really likes science and he really likes genomics, I’m proud
to say. And in June of 2014, he started some conversations actually with Francis Collins,
our director, around the idea of maybe launching a big project near the tail end of his Presidency
that might involve this vision of genomics, genomic medicine, individualizing patient
care, and so forth, because he thought it really could have great impact on the future.
And he wanted to see what he could do in the phases of his Presidency.
Those conversations evolved in the summer of 2014, eventually getting framed around
the concept of precision medicine. But precision medicine really sort of goes a step beyond
genomic medicine. Genomic medicine would just be the Gs, As, Ts, and Cs -- showed here what
precision medicine is being more precise by starting to account for things like environmental
exposure, lifestyle, diet, and things like that, other aspects of our life that we might
be able to use as information to be more precise for medical care. It is just a broader context
for individualizing medical care to advance human health.
And what the President saw as a great potential and aided by people who he spoke to, began
to get a great appreciation for the idea that, you know, today we really do most of our medical
care based on the expected results of the average patient. Almost everything we do is
based on the average patient. But the world is changing and there could be a tomorrow
where we could be more precise if we would only account for individual genomic differences,
environmental differences, lifestyle differences, and have that as a way to be more precise
in preventing and treating diseases. And the President really just wanted to know how could
he get from today to tomorrow.
And so a series of strategic planning efforts went on, and actually quite small, but important
over the small numbers of people because the President wanted this quiet. It was during
the summer of 2014, leading to sort of plans that emerged here at NIH and other parts of
the federal government. In the fall of 2014, the President was presented this plan. This
is actually a picture from that meeting -- you see Francis here -- other very important people
I won’t go through. The meeting took place in October of 2014, where this plan was discussed
and then the President got fully behind it, announced it in the State of the Union address
in early 2015, and then formally announced here and shown here in these pictures from
the East Room of the White House. These are photographs I got to take sitting center and
fairly close to the podium when the President announced January 2015, the launch of this
thing called the Precision Medicine Initiative.
At the exact time that the President announced this in January of 2015, the “New England
Journal” published this paper describing -- it’s actually the only scientific paper
officially coming out describing the general framing of the Precision Medicine Initiative
by Francis Collins and then-NCI Director Harold Varmus. If you haven’t read this I encourage
you to read it. Harold was an author in part because there was going to be a major cancer
genomics along the lines of what I said, part of the Precision Medicine Initiative. I’m
not going to describe that now. I just thought I would briefly describe the other major element
you’ll be hearing a lot about in the coming -- next coming months, actually hopefully
the coming years and even decades.
And that relates to the Precision Medicine Initiative’s launching of a U.S. national
research cohort. The idea is to collect -- recruit and enlist millions of people, at least a
million, hopefully more, U.S. volunteers who will agree to participate in this hopefully
multi-decade project and program where the participants are going to share genomic data,
lifestyle information, biological samples, all of this will be linked to their electronic
health records. And this is a big kind of project that not only aims to do incredible
studies, but also to forge new models for how science is done. The idea is to do this
in a fashion that fully engages the participants, shares the data very openly in a very genomics-like
way of having data sharing, and also to of course make sure that all their privacy is
adequately protected.
The notion of having such a big program of involving many years studying lots and lots
of people actually isn’t brand new. None other than Francis Collins when he had the
job I currently have about a decade ago, actually called for this in a commentary that he wrote
in 2004. So you may wonder, well why did we bring back to the President a decade later
an idea that was a decade old that never went anywhere because it turned out when this got
proposed in 2004, it just didn’t get any traction, in part because it was a little
too early and it was a little too expensive. But the world had changed in the intervening
decade, which is why we brought a -- new version of this was brought back to the President
in 2014, why it got traction then.
And let me just briefly tell you why the world has changed in the last 10 years and it very
much overlaps with some of the things I’ve described earlier. You know, compared to a
decade ago genomics has changed. I mean, think about all the things I’ve described, the
cost of sequencing genomics or understanding the genome, understanding the variants and
so forth. So genomics: breathtaking changes in the last decade and that sort of has been
very influential for the launch of the Precision Medicine Initiative. But there’s other areas
-- electronic health records. Electronic health records are critical for what’s going to
be done to capture this information electronically. But a decade ago it was only about 20 of health
care professionals in settings had electronic health records. That’s why you couldn’t
-- what didn’t exist -- the infrastructure didn’t exist in 80 percent of places to
collect the information. Now that figure is over 95 percent in the U.S. of health care
sites.
And then meanwhile we have done a lot to learn how to marry genomic information with electronic
record information and other information feeding with electronic health records. We have a
program that’s been going on since 2007, called eMERGE for Electronic Medical Records
and Genomics, which has really taught us a tremendous amount of how we might be able
to capitalize on genomic information, electronic medical record information, and other information
in electronic medical records. And that has served as, in some ways, a pilot for what’s
being envisioned for the Precision Medicine Initiative.
But meanwhile, what the President also -- and the Congress -- by the way who’s now funding
this -- got very enthusiastic about its recognition that’s it’s even beyond genomics into
other technologies and better and better ways of measuring physiology and environmental
exposures and lifestyle. And, you know, this idea of -- and I’ll show you a paper just
from last year -- you know, the -- all these new sensors, these M-health devices that measure
all sorts of things about our physiology, our various analytes, our cardiovascular system,
and so forth. This is all sort of just at early stages and many people wear FitBits.
Those are recreational devices. They’re awful, but much more robust technologies are
coming. And one can imagine harnessing the power of those technologies, having these
individuals wear these devices, collect the data, and have that data streaming in to its
central data resources for scientists to analyze. Oh, by the way, it’ll stream in through
their smartphones, which a decade ago, about -- only about a million smart phones existed
and only two percent of Americans carried a smartphone a decade ago. But now over 60
percent of Americans carry a smartphone.
So one can imagine having an immense amount of genomic data, electronic health record
data, mobile health data, all streaming in on a million or more people, and that will
create an amazingly rich data resource, but it also means we have a lot of data analysis
to do. And data science will become a prominent feature of this. But you know what? As Tara
and Andy are going to continue to tell you in subsequent lectures, we’re in a new world
here. Data science is front and center in biomedical research, compute power’s gone
up 160-fold in the last decade. And we’re, you know, we’re not totally ready, but we’re
going to have to be ready because this is what we’re going to be doing as biomedical
researchers.
But the last element, which I do think will be interesting and important to watch that
will make this different, is the notion of how we will engage the individuals who will
be part of the Precision Medicine Initiative and the cohort, in particular. These people
will not be subjects. They’re not going to be patients. They’re going to be partners.
And the reason they’re going to be partners is that studies have shown and continue to
show that actually most Americans want to participate in biomedical research, but they
only want to participate if they are sort of engaged from the beginning that they know
what’s happening to their data, they get to opt in and out to things along the way,
and if they’re treated as partners. And from the very beginning of the planning the
participants are being featured as partners in the scientific enterprise. And I think
the whole social media/Facebook era is very important here in how we will engage them
through social media, through smartphones, and so forth. And it’ll be a new way of
doing science. I can tell you there’s a lot of cohorts that have been created in the
United States. This one’s going to be unlike any of the ones that have preceded it.
So there’s a lot associated with this. If you want to read the current blue print for
the Precision Medicine Initiative cohort, there’s a working group report that you
can read and for -- actually URL will be here. It’s a very convenient URL to keep in mind.
This is the landing page for the initiative, which hopefully will exist for the next 10
and 20 years or 30 years, because a lot of information about the initiative is being
put to make this a very transparent process on the landing page for the NIH -- NIH’s
effort in the Precision Medicine Initiative.
So I wanted to end by sort of tying it all together because it’s actually very interesting.
The Precision Medicine Initiative sort of gives me a very interesting sense of Déjà
vu because there are a lot of things we don’t really know how it’s going to play out.
It is an audacious -- yet another audacious effort and there’s so many uncertainties
associated with it. And it happens to be happening exactly 25 years or so after the launch of
the Human Genome Project. And if you would have asked me -- and I was there -- the day
the Genome Project started, if you would have asked me, “Well, how exactly are you going
to map and sequence the human genome?” I would have said, “I have no idea. But it
just is a compelling goal and I think we can do it. And we’ll figure it out as we go.”
And that’s exactly what we did in getting the Human Genome Project completed. We set
audacious goals and we were willing to change course as needed, and I’m telling you, it
feels exactly the same now 25 years later with the Precision Medicine Initiative. We’re
launching it, it’s audacious, it has these -- we’ve never done anything like this before,
and there’s so many details. If you ask me or the people who are going to be organizing
that on the front line, they’ll say, “Well, we haven’t really figured that one out yet,”
but that’s okay because they’ll figure it out as we go. I think these -- the comparison
is sort of a -- and the Precision Medicine Initiative will go on longer, actually than
the Genome Project, I predict. But it’s that same audacious willingness to sort of
change mid-course. But I think it’s absolutely required in order for it to be successful.
And so this is -- I gave you that bit at the end because I think it’s very exciting to
watch and maybe some of you will participate in this either as part of the cohort or as
researchers analyzing the data and I hope that you do. And there’ll be a lot of news
coming out in the coming weeks and months about it as we stand this initiative up in
the next year or two.
Lastly, if the topics I talked about and programs I talked about are of interest to you, I will
shamelessly plug this because it’s free. I put out a weekly -- not a weekly, that would
kill me -- a monthly newsletter that the institute’s staff helped me put together that highlight
things along the lines of what I described here. And if you want to get that feel free
to follow the link on this and you can subscribe to it and get a monthly newsletter from me.
So I realize I am coming up exactly on the time that was allocated to me so I will end
there and I would encourage anybody who needs to leave they should leave and maybe people
who have questions should just come down and see me at the podium so that we can finish
the official session in an hour-and-a-half. Thank you.
[applause]
[end of transcript]