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TED2014

Stephen Friend: The hunt for "unexpected genetic heroes"

March 18, 2014

What can we learn from people with the genetics to get sick — who don’t? With most inherited diseases, only some family members will develop the disease, while others who carry the same genetic risks dodge it. Stephen Friend suggests we start studying those family members who stay healthy. Hear about the Resilience Project, a massive effort to collect genetic materials that may help decode inherited disorders.

Stephen Friend - Open-science advocate
Inspired by open-source software models, Sage Bionetworks co-founder Stephen Friend builds tools that facilitate research sharing on a massive and revolutionary scale. Full bio

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Double-click the English subtitles below to play the video.
Approximately 30 years ago,
00:12
when I was in oncology at the Children's Hospital
00:14
in Philadelphia,
00:17
a father and a son walked into my office
00:18
and they both had their right eye missing,
00:21
and as I took the history, it became apparent
00:25
that the father and the son had a rare form
00:27
of inherited eye tumor, retinoblastoma,
00:30
and the father knew that he had passed that fate
00:34
on to his son.
00:37
That moment changed my life.
00:39
It propelled me to go on
00:41
and to co-lead a team that discovered
00:43
the first cancer susceptibility gene,
00:47
and in the intervening decades since then,
00:50
there has been literally a seismic shift
00:53
in our understanding of what goes on,
00:56
what genetic variations are sitting behind
00:58
various diseases.
01:01
In fact, for thousands of human traits,
01:02
a molecular basis that's known for that,
01:06
and for thousands of people, every day,
01:08
there's information that they gain
01:11
about the risk of going on to get this disease
01:13
or that disease.
01:16
At the same time, if you ask,
01:18
"Has that impacted the efficiency,
01:20
how we've been able to develop drugs?"
01:23
the answer is not really.
01:25
If you look at the cost of developing drugs,
01:27
how that's done, it basically hasn't budged that.
01:29
And so it's as if we have the power to diagnose
01:33
yet not the power to fully treat.
01:37
And there are two commonly given reasons
01:40
for why that happens.
01:42
One of them is it's early days.
01:44
We're just learning the words, the fragments,
01:47
the letters in the genetic code.
01:51
We don't know how to read the sentences.
01:53
We don't know how to follow the narrative.
01:55
The other reason given is that
01:57
most of those changes are a loss of function,
02:00
and it's actually really hard to develop drugs
02:02
that restore function.
02:05
But today, I want us to step back
02:07
and ask a more fundamental question,
02:09
and ask, "What happens if we're thinking
02:11
about this maybe in the wrong context?"
02:13
We do a lot of studying of those who are sick
02:16
and building up long lists
02:19
of altered components.
02:22
But maybe, if what we're trying to do
02:25
is to develop therapies for prevention,
02:27
maybe what we should be doing
02:31
is studying those who don't get sick.
02:32
Maybe we should be studying those
02:35
that are well.
02:37
A vast majority of those people
02:39
are not necessarily carrying a particular
02:41
genetic load or risk factor.
02:43
They're not going to help us.
02:45
There are going to be those individuals
02:47
who are carrying a potential future risk,
02:49
they're going to go on to get some symptom.
02:51
That's not what we're looking for.
02:53
What we're asking and looking for is,
02:55
are there a very few set of individuals
02:57
who are actually walking around
03:00
with the risk that normally would cause a disease,
03:02
but something in them, something hidden in them
03:06
is actually protective
03:09
and keeping them from exhibiting those symptoms?
03:11
If you're going to do a study
like that, you can imagine
03:14
you'd like to look at lots and lots of people.
03:17
We'd have to go and have a pretty wide study,
03:19
and we realized that actually
03:23
one way to think of this is,
03:24
let us look at adults who are over 40 years of age,
03:26
and let's make sure that we look at those
03:30
who were healthy as kids.
03:33
They might have had individuals in their families
03:35
who had had a childhood disease,
03:37
but not necessarily.
03:39
And let's go and then screen those
03:40
to find those who are carrying genes
03:43
for childhood diseases.
03:45
Now, some of you, I can see you
03:47
putting your hands up going, "Uh, a little odd.
03:48
What's your evidence
03:52
that this could be feasible?"
03:53
I want to give you two examples.
03:55
The first comes from San Francisco.
03:57
It comes from the 1980s and the 1990s,
04:00
and you may know the story where
04:03
there were individuals who had very high levels
04:05
of the virus HIV.
04:07
They went on to get AIDS.
04:09
But there was a very small set of individuals
04:11
who also had very high levels of HIV.
04:14
They didn't get AIDS.
04:17
And astute clinicians tracked that down,
04:18
and what they found was
they were carrying mutations.
04:21
Notice, they were carrying mutations from birth
04:24
that were protective, that were protecting them
04:27
from going on to get AIDS.
04:29
You may also know that actually a line of therapy
04:31
has been coming along based on that fact.
04:34
Second example, more recent, is elegant work
04:37
done by Helen Hobbs,
04:40
who said, "I'm going to look at individuals
04:42
who have very high lipid levels,
04:45
and I'm going to try to find those people
04:47
with high lipid levels
04:49
who don't go on to get heart disease."
04:51
And again, what she found was
04:53
some of those individuals had mutations
04:56
that were protective from birth that kept them,
04:58
even though they had high lipid levels,
05:01
and you can see this is an interesting way
05:02
of thinking about how you could develop
05:06
preventive therapies.
05:08
The project that we're working on
05:10
is called "The Resilience Project:
05:12
A Search for Unexpected Heroes,"
05:14
because what we are interested in doing is saying,
05:16
can we find those rare individuals
05:18
who might have these hidden protective factors?
05:21
And in some ways, think of it as a decoder ring,
05:25
a sort of resilience decoder ring
05:28
that we're going to try to build.
05:30
We've realized that we should
do this in a systematic way,
05:32
so we've said, let's take every single
05:36
childhood inherited disease.
05:38
Let's take them all, and let's
pull them back a little bit
05:39
by those that are known to have severe symptoms,
05:42
where the parents, the child,
05:45
those around them would know
05:47
that they'd gotten sick,
05:48
and let's go ahead and then frame them again
05:50
by those parts of the genes where we know
05:53
that there is a particular alteration
05:56
that is known to be highly penetrant
05:58
to cause that disease.
06:01
Where are we going to look?
06:04
Well, we could look locally. That makes sense.
06:05
But we began to think, maybe we should look
06:07
all over the world.
06:10
Maybe we should look not just here
06:11
but in remote places where their might be
06:13
a distinct genetic context,
06:15
there might be environmental factors
06:18
that protect people.
06:19
And let's look at a million individuals.
06:21
Now the reason why we think it's a good time
06:25
to do that now
06:28
is, in the last couple of years,
06:29
there's been a remarkable plummeting in the cost
06:31
to do this type of analysis,
06:34
this type of data generation,
06:36
to where it actually costs less to do
06:38
the data generation and analysis
06:40
than it does to do the sample
processing and the collection.
06:42
The other reason is that in the last five years,
06:46
there have been awesome tools,
06:50
things about network biology, systems biology,
06:52
that have come up that allow us to think
06:55
that maybe we could decipher
06:57
those positive outliers.
06:58
And as we went around talking to researchers
07:01
and institutions
07:03
and telling them about our story,
07:05
something happened.
07:07
They started saying, "This is interesting.
07:08
I would be glad to join your effort.
07:11
I would be willing to participate."
07:14
And they didn't say, "Where's the MTA?"
07:16
They didn't say, "Where is my authorship?"
07:18
They didn't say, "Is this data going
to be mine? Am I going to own it?"
07:22
They basically said, "Let's work on this
07:26
in an open, crowd-sourced, team way
07:29
to do this decoding."
07:31
Six months ago, we locked down
07:35
the screening key for this decoder.
07:37
My co-lead, a brilliant scientist, Eric Schadt
07:40
at the Icahn Mount Sinai
School of Medicine in New York,
07:45
and his team,
07:48
locked in that decoder key ring,
07:50
and we began looking for samples,
07:52
because what we realized is,
07:55
maybe we could just go and look
07:56
at some existing samples to
get some sense of feasibility.
07:58
Maybe we could take two, three
percent of the project on,
08:01
and see if it was there.
08:04
And so we started asking people
08:05
such as Hakon at the Children's Hospital in Philadelphia.
08:07
We asked Leif up in Finland.
08:11
We talked to Anne Wojcicki at 23andMe,
08:13
and Wang Jun at BGI,
08:17
and again, something remarkable happened.
08:18
They said, "Huh,
08:21
not only do we have samples,
08:22
but often we've analyzed them,
08:24
and we would be glad to go into
08:26
our anonymized samples
08:28
and see if we could find those
08:29
that you're looking for."
08:31
And instead of being 20,000 or 30,000,
08:33
last month we passed one half million samples
08:35
that we've already analyzed.
08:38
So you must be going,
08:40
"Huh, did you find any unexpected heroes?"
08:42
And the answer is, we didn't find one or two.
08:47
We found dozens of these strong candidate
08:50
unexpected heroes.
08:53
So we think that the time is now
08:55
to launch the beta phase of this project
08:57
and actually start getting prospective individuals.
09:00
Basically all we need is information.
09:03
We need a swab of DNA
09:06
and a willingness to say, "What's inside me?
09:08
I'm willing to be re-contacted."
09:11
Most of us spend our lives,
09:14
when it comes to health and disease,
09:18
acting as if we're voyeurs.
09:20
We delegate the responsibility
09:23
for the understanding of our disease,
09:26
for the treatment of our disease,
09:28
to anointed experts.
09:29
In order for us to get this project to work,
09:33
we need individuals to step up
09:36
in a different role and to be engaged,
09:38
to realize this dream,
09:42
this open crowd-sourced project,
09:45
to find those unexpected heroes,
09:48
to evolve from the current concepts
09:52
of resources and constraints,
09:55
to design those preventive therapies,
09:57
and to extend it beyond childhood diseases,
10:00
to go all the way up to ways
10:03
that we could look at Alzheimer's or Parkinson's,
10:05
we're going to need us
10:09
to be looking inside ourselves and asking,
10:11
"What are our roles?
10:14
What are our genes?"
10:16
and looking within ourselves for information
10:18
we used to say we should go to the outside,
10:21
to experts,
10:23
and to be willing to share that with others.
10:24
Thank you very much.
10:29
(Applause)
10:32

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Stephen Friend - Open-science advocate
Inspired by open-source software models, Sage Bionetworks co-founder Stephen Friend builds tools that facilitate research sharing on a massive and revolutionary scale.

Why you should listen

While working for Merck, Stephen Friend became frustrated by the slow pace at which big pharma created new treatments for desperate patients. Studying shared models like Wikipedia, Friend realized that the complexities of disease could only be understood -- and combated -- with collaboration and transparency, not by isolated scientists working in secret with proprietary data

In his quest for a solution, Friend co-founded Sage Bionetworks, an organization dedicated to creating strategies and platforms that empower researchers to share and interpret data on a colossal scale -- as well as crowdsource tests for new hypotheses.

As he wrote on CreativeCommons.org, "Our goal is ambitious. We want to take biology from a place where enclosure and privacy are the norm, where biologists see themselves as lone hunter-gatherers working to get papers written, to one where the knowledge is created specifically to fit into an open model where it can be openly queried and transformed."

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