It's always great to follow Ben.
Because I think that,
I, it's always great
to hear kind of some of the ethical
arguments.
You know, one of my first,
disclaimers here is, of course, like,
like most like Ben and most people
I think are probably talking me on the federal government
employee.
These are my own words, and my own opinions.
But, you know, I think the other thing
I would want to share with you guys is
I am trained as a genetic counselor,
but most of my research
is in this field of kind of implementation
science,
where I'm really very focused on kind of practical,
pragmatic outcomes.
And kind of the, you know, where the rubber meets
the road
when we start to think about these ideas
and how we operationalize them
for patients.
And in our work, in this, you know, kind of messy
environment where we're doing this
clinical research, with people.
So, I think you should just know that,
again, I have this really pragmatic
approach.
And I am situated
as a clinician, and I also don't, really,
hold with this view that genetic data,
in almost all cases, is really different
from other kinds of health data.
And so that's that idea of genetic
exceptionalism.
That been mentioned.
I, I just don't think that that's something that
that we need to spend a ton of time
thinking about, and I don't, I don't
I don't really hold with that view
too much.
So in terms of an outline here, I
there's two kind of major buckets
of things that I would want to talk about,
which is, how research participants
think about or feel
about getting genomic data as a result of research
participation.
And then, of course, what are the outcomes,
what happens to people once they learn this kind of
information?
And, you know, I think what I'll do
is refine this a little bit and,
and focus in on how people just generally
think
about and react to options for learning genetic data.
And the reason why I think it's important
to think beyond research participants,
when we think about how people view
genetic data, is that really
any person has the potential to join a research study right.
And we know that there are many
research studies that are actively recruiting
people
to from the general population to participate in genomic
research.
So we need to think just about, you know,
I think the views of the general public
are important to think about here.
And then, as Ben mentioned, most of what
I'm going to be talking about is,
are you know, what we understand about,
what happens to people once they learn about actionable
genomic data?
And so, you
know, when we think about attitudes
towards the return of results,
there have been many that much ink
has been spilled on this topic.
And I'll kind of circle back to that
in a minute.
But, you know, as genomic,
interrogation techniques became more ubiquitous and,
much less expensive, there's been just a lot of work
done on trying to solicit the thoughts,
opinions, motivations,
attitudes, intentions and preferences of of people,
of different groups of people towards
genomic data.
And so, you know, there's work that's been done with, asking
research
participants, you know, what kind of data
they would like, the general public.
And then, of course, you know, specific groups,
like parents of, pediatric,
sick children, basically,
patients who are presenting with,
particular,
you know, disease where genomic interrogation might be
helpful.
And then again, some, some studies of even more fine
grained,
you know, participant attributes.
And when we look at this body of literature,
there is an overwhelming preference
for return of results.
And so, again, I think some of this is
just further is substantiating
this argument that, you know, if you ask people what they
want,
many people, the first thing that's going to come to mind is
not
I don't actually want to know this information.
So, you know, across all of these studies,
in, you know, up to 100%
of people who participate in this kind of
work,
say that they want everything that you could tell me.
In general, there is, with increasing
actionability,
there is an increasing preference
for return.
So more people, if you give them an array of
choices,
you know, would you like to know about,
things like your ancestry or the baldness
example?
That was a really good one or a non treatable disease.
People have less interested in learning
some of that stuff, and more interest
in learning about actual actual results
that, you know, may make a difference
in their health.
But, you know, this is not the only boundary that people
will cite.
So many people will say, I want you
to tell me, even if it's normal.
So I even want negative findings.
And then people also will desire their raw
sequence data, for research studies,
even if they don't have,
you know, the, the expertise,
to interpret those data, they, they want to,
they want to have, you know, everything
that you could possibly tell me
is, is what many people say.
People are also very interested and motivated
to learn about research results that researchers think are
interesting.
So they'll say things like, well,
you know,
if you think it's interesting, like
you should definitely share it with me,
even if it might be interesting for some very esoteric reason
that has to do with, you know, some biochemical
function,
that a normal person might not really care about.
People still really want that kind of information.
And a very, very small minority of people will say that
they want
none of their genetic data back or that they only want primary
data back.
And what I mean by primary data are,
genetic results that relate specifically
to the reason why the sequencing was done in the first place.
So I joined a research study because my child has autism, I
only want
results related to autism.
That's it. Don't tell me anything else.
And when you ask people why they want this information
the most,
the most salient reason or the primary reason,
is that people find this to be very empowering.
You know, this idea again, that knowledge is power.
That there's something that they could do with the
information,
related to that.
Many people will talk about how well,
there's a potential for me to prevent
disease in myself or in my family members,
which is again, related to this idea
that, you know, perhaps this could be beneficial
to me,
beneficial to my child, beneficial to my
relatives.
But a lot of people are also just curious,
which again, this isn't rocket science,
right?
We have 23 and me and all of these
different direct to consumer
genetic testing companies that are selling
the same kind of information.
People feel like they can know themselves
better in some ways
by having access to, you know, their DNA.
And again, I think that relates to this other idea that, you
know,
people find this to be a way that they can, kind of explore
attributes
about themselves that maybe a clinician wouldn't
put, as you know, the most high priority item, like ancestry.
But that is has a lot of personal utility to
a person.
So these are all reasons why people say
that they want this information
and they say that they want all of this information or they
want
their genetic results, even as they cite
some of these concerns, like privacy,
you know, who's
going to maintain my genomic data
and how might it be used in the future?
Is there a burden to what I might,
you know, what's what's
the burden associated with the knowledge
that you're going to impart upon me?
Is it going to result in some sort of poor
outcome for myself
if people are able to kind of way
and think about these risks,
but they certainly do not override
this preference for returns.
So again, I think people can manipulate
these concepts in their minds,
and yet still come down on the side
of, you know, this is something that I really, really want.
And again, I think this really speaks
to this idea that people believe
that their genomic data are their data,
they want to control it.
They want to know who is looking at it.
When you talk to research participants,
they'll say things like, well,
no one cares as much about my sequence
data as I do.
You know, you might be looking at it
for this one reason.
But, I'm if you give it to me, then I can revisit it
and think about it over time and talk with
talk with my doctors moving forward.
And so, you know,
I'm uncomfortable with this idea of someone else determining,
you know, what's in my best interest, in
terms of,
making decisions for me about what kind of stuff
I should learn from from, research, sequencing.
And then I think, again, the converse of this.
Right.
Not to know, as Ben mentioned, is this right to know it's my
data.
I should have autonomy and control over it.
And that people view deliberation
and restriction
about the types of data that are, that are given to
people.
Is viewed as paternalistic,
generally by
people who are who are asked to think
about,
what kind of, genetic data they might want to get from
research.
So when you ask people, you know, okay, we've
established
that you want to know this information,
what are your preferences for,
for returning, you know, for learning this
information.
And so people will say after broad consent, so they want to
be informed
about what kind of genomic data are going to be generated.
And they also really want to understand
kind of what's the plan,
to to get this back to me, are you going to call me?
Are you going to send me a letter?
And I think many people have, have,
have argued and we have a lot of good data
to suggest that,
when you stick to a plan to return results, it is a great
way
to foster, ongoing participant engagement.
And, you know, we know that having participants
actively
engaged in research is, you know,
just a huge part of the whole enterprise.
And then there's some, you know, data
to suggest that, you know, people
prefer things like face to face genetic counseling for return of
results.
But I think there's a lot of room
for, innovation here as well.
And I'm going to come back to the idea of consent a little
bit as,
a little bit later.
I'm going to again cite Ben's work here,
because we can look at people who refuse to, to get, genetic
data.
And so again, I'll just emphasize that,
these people are in the minority.
And I think another really important thing
to to be aware of when we're looking
at this literature is that a lot of it
is hypothetical or analog.
So you would ask a group of research
participants, you know, if
we were to implement genomic sequencing
in our study, what choices would you make?
Or if you were to be offered these choices
which sound best to you?
There's a the body of literature that's focused on people
who are actually making these decisions
is much smaller compared to the,
you know, group of people who are, the
literature
that's on people who are actually making
some of these choices.
And as been pointed out, this refusal may not be durable.
And so,
again, there's, there's,
some data to suggest that people
can change their minds on this over time
and by kind of happenstance,
I have talked to some of the these people, these refusers
and returned secondary findings to them.
And they are fine.
It's anecdotal data, but they were fine.
And I think one thing that's
that's an interesting point that I will mention here,
because you might be asking yourself,
well, if they refused,
how did you come to talk to them?
I think this is another just really
practical argument against things like checkboxes.
Because if you are, there's a lot of possibilities
for, data integrity issues to come into
play.
So if someone checks the box no on a paper consent form,
then that choice needs to be carried
forward with fidelity
into whatever your kind of research database
is.
And so I just think it introduces the,
a lot of potential for kind of bidirectional error,
where you do find yourself in a position
where,
you know, maybe we weren't really supposed to be
analyzing
the sequence data of this person because they check the box for.
No, but we did analyze the data, and we're exactly in the
position
of the second case, that, Ben mentioned,
where we know something about somebody.
They said, no, how do we go back and, you know, talk to them
because now we know this information
and I think it's,
it's a different situation when you don't actually, you
know, seek
those, you know, those kind of data
from the sequence data that you generate.
You don't seek to look for secondary
findings. But,
again, that's a bit of a tangent,
but I think that, I thinking about,
how we categorize people's choices,
when we do
things like that is just another,
another opportunity to introduce error.
So, you know, I think when
we think about, you know, this whole topic,
we are still in a situation
where we know much more about people's
attitudes about genomic data
and whether or not research, you know, how research
participants feel
about getting these kinds of data
compared to
what the outcomes are.
And so I'm going to kind of transition
a little bit into talking about outcomes,
because I think we need to shift this ratio.
Again a lot of ink has been spilled over.
What ought we to do and why aren't we to do it.
And less has been on this area of, well,
what happens when we make decisions,
you know, what are the outcomes associated with some of those
decisions?
And I think this is a point to revisit.
One of, you know, revisit consent in one
way,
which is, you know, over the years,
I've talked to I've consented
many people for genomic sequencing
with return of secondary findings.
And I always like it when people decline
participation and research studies.
You know, you really shouldn't ideally,
have 100% of your prospective
participants signing that consent form.
Right.
It's great if most everybody does,
but I think it's a really great
I don't know, I like it when people decline because it
to me,
it's kind of an an internal check
that I'm doing it right,
because not everyone who I talked to
could possibly want to be in my study,
even of the selected population
that comes through to have that conversation with
me.
And so, you know, I think the cases in
which I've talked to people
in, they are concerned about receiving these, these
findings
kind of, again, go along with what
some of the things that Ben,
was saying about these like specific instances.
You know, I've had a couple of people
who I've talked to
who've been in the military and so have been really
concerned about
where their genomic data are going
and how that will affect their potential,
fitness, and concerns about a medical
discharge.
That's a really specific and important case.
That is, you know, important to talk about.
In another case, I've, worked with some families
where,
there are some programs where there's carrier screening
for genetic conditions that are really,
more common in a certain population.
And so there are kind of community
genetic screening
initiatives that are, again, community
led, community organized.
And one of the features of some of those programs is
that, people
who participate in the program learn,
don't learn their individual results
because the purpose of the program
is to help make sure
that a potential partner doesn't carry
the same,
pathogenic variant for a specific condition that
they might.
So it reduces the probability that they would have an affected
child.
The partnership is
analyzed and the potential for the that,
aspect of partnership to have an increased risk to
have a child
with one of these conditions, is kind
of, looked at on a community level,
but individual results are not returned
because that would be stigmatizing.
Again, this is such a specific situation
that it's great that people have again, brought
this up.
And I think that's something that absolutely should be
accommodated.
And then, you know, in other instances,
I've had people talk about how,
you know, they just feel uncomfortable
with genetic data,
because they've thought about, you know,
what does that mean for,
you know, choices that people might make
in like, a reproductive setting, right?
Again, I think these are all really
specific
examples that are worth exploring.
But, hopefully you can kind of tell
from what I'm saying,
what Ben was saying, that these are super,
super, super specific.
And again, I think we really need to think
about this idea
that we have a huge amount of data
to suggest that
by and large, people want us to tell
them these kinds of things.
So I just want to take a
step back before we start talking about
outcomes.
And again, some practical considerations.
In returning these kinds of findings,
to just take a moment to define them.
Of course I need to.
I have an xkcd comic also, just like Ben.
And I think, you know what's interesting
about this and the comic that,
that, Ben, put up also is
that, you know, I think that when you talk to a
person
in the general public about, like, genetic data, you know, I
think that
what they are hoping for or what they think might happen in the
future,
is that, you know, at some point we're going to go to the doctor
or the doctor is going to do like a fingerstick put a drop of
blood
in some like machine in the back and then come out with some
like wonderful results report, then kind of prescribe to you
ways
to change your life or change your health care to make you
healthier.
Right?
So they're going to talk to you about how you're
more likely to have diabetes or you shouldn't have gluten.
I think this is what people think about
when they think about like,
what is the utility of genetic information
in a health setting?
And so,
you know, I think that's a context
that I think about.
It's the in talking with people about exactly what it is that we
find
because we're still at the point where,
the kinds of findings that we would propose to return
to people
through an opportunistic screening program
are much more delimited.
Right.
And they're they take a lot more effort
to produce
than just, again, putting a drop of blood
in the machine in the back.
Right.
And so, you know, I think it's important
to discuss this idea that
when we are returning research results,
research genetic results to people,
this is this is opportunistic screening.
It's not population screening. Right.
But I think it's really important
to examine, you know, what we can learn
from this opportunistic screening,
because I do think that
the goal that we're all trying to get to
has been mentioned is to incorporate
genomic data into our health care more
broadly.
But there are a lot of things that we can learn from,
this moment in time when we are giving this
information
back to people, who are in a certain place and
time.
Right.
If you have the if you're joining a research
study
where genomic sequencing is part of it,
you might have the opportunity
to have your, you know, some of these findings returned
to you.
As opposed to, you know, what, if we were to implement this on
the
population level?
And again, I think it's also important
to think about the fact
that what we're really doing here, in most instances, is
repurposing
this list of variants that was published,
not the variants genes,
but specific variants that have specific
attributes and specific genes,
by the American College of Medical Genetics and
Genomics.
And this list was developed because
is developed for clinical laboratories.
Right.
So many, many more people are having,
clinical genome sequencing.
And the this list
and this policy was developed for,
you know, how should clinical laboratories
essentially handle the problem from Ben's case, right.
They were asked to analyze genomic data
to try to understand
why someone might have, you know, autism or a seizure
disorder
or something like that, but they they are in a position to know
much more.
How should they handle that?
And the consensus was that, well,
they should look for these things
and they should return them to people when
when people express a preference to learn this information.
And so I think, you know, the key
attribute
of all of these secondary findings
that we're talking about
is that they're actionable, has been mentioned.
So if you were to know about this,
there was something there's something you could do
about it.
And I think
another thing that's important to know
is that, you know, most of the find,
most of the disorders on the list
are, have autosomal dominant inheritance.
And what that means is, is that if you, as the patient,
are found to have one of these variants,
all of your first degree relatives are at
50% risk to also share that variant
with you and presumably that risk status.
And that's risk status for a treatable
or preventable disease for which
potentially life saving treatment
or surveillance is available.
But again, I think this is an,
an area where we need to really think
about,
you know, is this really secondary when,
we are in, when the Acmg list and the Acmg guidelines were
written
for clinical laboratories, where there is
someone who's ordering the test for a clinical reason,
and there's an interpreting laboratory
whose main expertise is trying
to understand genetic etiologies of specific
clinical entities in research.
You know, the ordering clinician is maybe
not going to be a geneticist, and they may not really be
ordering,
you know, a genome interrogation test,
for the same reasons that someone
in the clinical world might be.
So these are just a couple of caveats to,
to kind of think about and kick around.
But, you know, if things go according to plan,
ideally what we would see is a is a secondary findings paradigm
where as this finding is disclosed
and recommendations
are made to the recipient, and there are two major
recommendations that ought to be made
to people who are in this position.
So the first thing is that, the person should undergo
some disease specific evaluations
because now they know
that they have a genetic risk factor,
and so they should meet
with an expert to,
you know, begin surveillance or, you know, see if they already
have signs of this disease, because they have this risk
factor.
And of course, the the hope there would be
that if they are found to have features
they, you know,
could engage in earlier diagnosis or prevention or
treatment,
but if they don't, then they can participate in the
kind of
ongoing surveillance that would allow intervention at
a time
when it is most likely to have the best
outcome.
But then this gets to this second recommendation.
What I mentioned about these genes
that are on this list is that their dominant.
So again, if any person is found to have one of these findings,
an important secondary benefit might be to family members.
And so the second general recommendation
that people are given
is to tell people in your family about this variant.
So not only should you share this information with your
family
so that they can get tested, we call that cascade
testing is when you're tested for it.
We have genetic testing to see if you have a known familial
variant.
And if you're
positive, then you can kind of enter
this virtuous cycle of,
you know, increasing your surveillance
that could potentially save your life.
But also,
oftentimes, you know, these are findings
that are that people are not necessarily are expecting.
And you can better interpret the clinical meaning of the
findings
if you understand more about the family as well.
You know, so I think, it's important to
think
that, you know, this is kind of how we hope
things go.
And, what we did a couple of years ago
is turn to the literature to see kind of
what is the evidence base for any of the things that I just
mentioned.
And so we did a systematic review
where we looked in the, the literature, where the unit
of analysis
was individual recipients of secondary
findings on the Acmg list.
And so we were only able to find about 700 reports of,
these kinds of findings being returned to
people.
And that was a bit surprising to me,
because, you know,
the first iteration of this list was published in 2013.
And so almost ten years after, the list was published,
we still didn't find a ton of reports about
this.
But we looked at, what is known in the literature
about how these findings are returned
and what outcomes happen after receipt.
And I think the, one important finding that stood out to us
immediately
was that 97% of these reports were participants in research
studies.
So really a tiny fraction are people
who are getting these kinds of results
in a clinical way,
meaning the way the list was intended
to be operationalized.
That's, has interesting implications
when we think about
maybe like population screening, and kind of clinical efforts.
But it's good for us here because most of these people
are research participants. And that's what we're talking
about, is returning results to research participants.
So I'll just reiterate some things that Ben already
mentioned.
You know, again, there's been a lot of
hand-waving about,
what do people do?
What are adverse psychological outcomes
when people learn this information? And,
we found an array of, you know, in studies that
evaluated
how people approach these, results
from an emotional perspective.
People will say things like, they were surprised. They were
relieved.
Some people will say they're sad,
but hardly anybody says
that they regret their choice to learn this kind of
information.
So I think this is again, dovetails
with this, right?
Not to know, when given this option, very,
very few
people will say that they regret learning this information.
When we follow people over time,
levels of depression and anxiety from before to
before
results disclosure to after results,
disclosure remain unchanged.
Although again, there's not a ton of data on
this.
And some people even forget that they've been that
these that these data have been shared with them.
And I've experienced that personally.
High numbers of people report sharing their
results
with their family, especially to close relatives.
And we also saw that,
disclosure to primary care doctors
and then specialists, was common,
but certainly not universal.
And I think this is an important point,
when we think about again that that paradigm.
And I'll come back to that in a minute.
We looked for,
precision medicine surveillance outcomes.
And what I mean by that is, you know,
most of us will undergo screening
tests based on attributes that have nothing to do with our
DNA.
Right.
You're a woman over 40. You have mammograms, right?
You're a man over 50.
You get your PSA tested right?
A a precision medicine approach to surveillance
is, well, you're a woman over 40,
and you have a variant and a gene
that increases your risk for hereditary breast and
ovarian cancer.
So we should surveil you differently
from someone in the general population.
And so, adherence across the range of studies
where adherence was examined, was generally pretty good.
And people did refer people to, you know, specialists.
Many people were followed by specialists up there
after they received their results.
And in studies where cost was can was examined,
the costs were lower than expected.
And again, if you look across this body of
literature
and I'm going to put a few cases in, in a minute, you know, there
are
you can find reports of life saving treatment after people
learn
this kind of information.
And so this could include,
you know,
not taking medicines that avoid, that,
that increase your interval.
It has to do with your heart rate
and rhythm.
There's a genetic predisposition for that,
targeted therapy for familial
hypercholesterolemia,
early identification of cancerous lesions
by imaging
and then prophylactic surgeries, which we know, reduce cancer,
cancer risk.
So if we overlay our findings on to,
this ideal paradigm,
the blue circles here, are
proportionate to the number of cases
where there's data
to support a different, you know, different parts of
this process.
And so I think what you'll notice
immediately
is that, the proportion of data that are in the literature
about how we do this and what we should tell people
is quite,
is very large compared to, again, outcome
data.
And when we think about outcome data
specifically, there still is not a ton
of data in the literature about what are these specific health
outcomes.
You know, are we really moving the needle on people's health?
And another way to think about this is what is the clinical
utility
of sharing this kind of information
with people. So again,
there are reports in the literature
I'm going to give some case examples.
But this is really an opportunity
where we can improve the knowledge base
about what happens to people after they learn this kind of
information.
And so I'm going to wrap up by talking
about a couple of different things that we
do here at NIH.
While also
acknowledging that there are lots of other groups who are
working on
developing these best practices for return
results and outcomes research emerge.
And all of us and the Geisinger Mike code
initiative are things that come to mind.
And again, we're learning more and more
data about,
adherence and whether or not people
are sharing this information
and what are the most efficient ways
to share information across across health care
systems?
Might be I'm going to talk a little bit
about two different programs.
One and of course it's the federal government.
So we have acronyms for both of them.
GSP is the genomic Services research program.
And this is our secondary genomics
finding service.
So I'm going to start with GSP.
And this is a study that we that's
currently underway
specifically for people who've received
secondary findings.
So we are, you know, actively recruiting people for this
study.
We have about three, 300 people enrolled so far.
And so we are broadly recruiting people,
across many different,
recruitment
mechanisms to ascertain secondary
findings recipients.
And we're evaluating adherence to medical recommendations
and then communications outcomes.
But we also do some
we do quite a bit of genotyping of people in the family.
So we'll offer cascade testing, to relatives
who might
be potentially informative.
And that's because we want to really
understand
the prevalence of the phenotype that's associated, with the
variant.
Another way of thinking about that is your genes say you
should have
fill in the blank condition.
What do you actually have it. Right.
And I think this is important from kind of
a population genetics perspective
because although the the risks that we quote to people when we
return
these kinds of findings to them come from a selected population,
right?
So we know that the risk to develop
ovarian cancer, if you have a BRCA two
variant is, you know, 45% across the
lifetime,
that data point comes from,
years and years of
study of people who have hereditary breast
and ovarian cancer.
And so, you know, there's a there's a selection bias here.
And so it's really important to understand, you know, what
might be the actual what's the actual prevalence of
disease
in people who get this kind of
information.
So I have a couple cases to share with you.
Lots of people that we've seen here at the NIH.
This is a pedigree.
I don't know how many people maybe
have seen a diagram like this,
but squares are, men and circles are women.
And our probe end here.
That's the word that we use in genetics
for the first person who comes to,
clinical attention is a 43 year old woman who learned about
a pathogenic variant in this gene called able B and
B.
This variant in Apob is associated
with familial hypercholesterolemia.
Amia which is a genetic form of high cholesterol.
And she found out about this because
she had ancestry testing that she bought.
Many of these companies who do ancestry testing
will put their offerings on sale around Thanksgiving.
And that's what happened to her.
So you can choose to, you can check the box for do
I want to receive health data in addition
to knowing how Scottish or Irish I am?
And she was like, oh, it's on sale. Sure.
I'll go ahead and get that. And that's when she learned
about this,
result that really increases the risk
for high cholesterol.
And I know it's really hard to read here,
and we'll see if the cursor cooperates,
but you can see, that
you know, I actually don't think she needed a genetic test to
to tell me this because she has a brother
who had high cholesterol at age seven.
Many paternal relatives who've had heart attacks
and bypass surgery in their 40s and 50s.
And then, four of her six children,
were also reported to have high cholesterol.
So this pattern screams familial hypercholesterolemia to
me.
But when she enrolled in GSP, she,
had not really, this had not been ascertained
by any of the clinicians that she or her children were
seeing.
So we brought her here to the NIH,
and I think this is a great example
of how sharing this information with people.
And again, we did a lot for this person
because on our protocol,
we have the ability to bring them
to the NIH for for phenotyping.
But a home clinician could have done this too.
Genomic ascertainment can overcome barriers to
clinical care.
And so this is, a CT scan of her heart.
And she had the coronary arteries of,
woman in her upper 70s.
So she had some evidence of coronary artery calcification
already.
And we started two of her four children
on treatment for high cholesterol.
And hopefully that will lower their risks
of coronary artery disease.
So, again, I think this is just a concrete
example of how
this can move the needle for people.
Because it took even though I would argue that
it
you didn't need a genetic, test result
to recognize this clinical entity
in this particular family.
It took having those genomic data,
genetic data to make that recognition.
Here's another patient who we've enrolled.
This is a 36 year old, when she when
this woman we met her when she was 40.
But when she was 36, she joined kind of a biobank study that was
run out
of, historically black college and university and HBCU.
And she was motivated by curiosity,
but she also didn't know
very much about her dad's side of the
family.
And she thought it was really important
for minorities to be represented in genetic
research.
And through that, participation in that study, she learned about
a BRCA
two pathogenic variant.
This is a variant that really increases your risks
to develop breast and ovarian cancer.
Among other types of cancer.
She met with a genetic counselor who counseled screening
enhanced screening through mammography
in breast MRI.
And also her her primary care provider,
recognized this and urged her to have
mammograms.
And she actually was diagnosed with breast cancer
at age 39 on her very, very first
mammogram.
But having this finding, allowed her
to really motivated her to find out much
more about her dad's side of the family.
And so through this, she was able to reconnect
with folks on her dad's side of the family
and learned about this really striking
history of breast cancer and pancreatic
cancer,
which is also associated with this variant, on her,
paternal side of the family.
So again, another example of, you know, some outcomes
that can that can come from sharing this information
with people.
Both of these families have been on different episodes of the
podcast
DNA today.
So if you're interested, you can find them there.
And I'm
going to wrap up just by talking about
GFS,
which is again, the clinical service
that Ben alluded to that
we run to help people return these findings to, research
participants.
And, in 2010, what was a very was the first
time
our group started using Acxiom in genome sequencing to try to
understand,
the causes of some really rare pediatric onset conditions.
And I really remember at the time
writing the consent form,
for that study,
and really thinking about,
that my, my position has obviously really
changed
because I thought, well, we can't possibly
tell these people anything
other than the cause for their kids
condition.
Because that just seems so,
seems like so much to to burden them with.
Right.
But again, my thinking has really, really changed here.
And I think
what I'll say is that, you know, through things like HDFs,
what we're really doing here is operationalizing
this institutional duty here at the NIH
to return these kinds of results.
And so,
this is a clinical service that's
available to intramural investigators,
where we screen sequence data that's been
generated through a research protocol.
So we don't sequence the people they can't
they send us their sequence data,
and we look for these acmg findings.
And then we help research teams to clinically validate
and return these these findings to people.
And so I do this so I provide genetic counseling for these
people.
And then we, you know,
get a family and personal history
and then, make a local referral.
And I think,
again, the,
the, the major question I get when people are applying
to use the service is, well, again,
because the recommendation
is to do this prospectively, how should I write my consent
form?
And, and can you give our team some training on consent.
And I just consenting people to this is just really not hard.
I get where you're coming from because
I used to think it was really hard.
But again, thinking about the build up to
this point,
most people really want this information.
Most people think that genomic data are important,
and I don't know that we necessarily need
to think about consenting
for receipt of genetic test results
that much different
than any other kind of health data.
So I'm just just think to yourself,
if you have ever, you know, had a doctor
order like a lipid panel or hepatic panel,
have they ever spent a long time
talking with you about like, so here's what we're going to
find
and we might find this also, I think it's just kind of
implied right.
And so, you know, to wrap up, what I'll say here is that,
you know,
this has been, there have been some implementation challenges
here.
And I think these are important things
to think about.
So, you know, there are some individual
attributes about participate
that can make this process of returning researchers
research
genetic results challenging, things like their age or their
wellness.
If these people come from a kind of a frail population and been,
Scott mentioned this earlier, and then finding local referrals
for research participants even in the US is not easy,
right?
Like there is not a cardiovascular
genetic test in, you know, within a 200
mile radius of people living in places
like Idaho and North Dakota.
But I think the major concern here
is recon targeting people.
And so, you know, I think the biggest lesson
learned here
might be to just the way you can make this move go
smoothly
is to maintain contact with your research participants
so that you're able to get to them, later in time.
So just some very brief anecdotal data
about kind of what we found in CFS.
The clinical genetic testing I think is, is
uncommon.
So another benefit of doing this whole thing,
is that many people, like the family
with familial hypercholesterolemia
that I presented earlier have signs of what's
going
on, and they are not getting diagnostic testing.
So if we look across, you know, the,
the people who we've done this for in CFS,
about 25% report a clinical phenotype
or diagnosis, but no genetic testing.
And that's not great, right?
Because if they've got a clinical
diagnosis, maybe, you know, they think
there's it's important to kind of
have the molecular background for that.
And about 50% of people who we talked to report
a consistent personal or family history
that's consistent with the variant
without a clinical diagnosis or genetic testing.
So, in conclusion, I think, I hope
I've convinced you that most people want
this information and that,
there's the
potential for some really good outcomes
that result,
when we do this, but we need to study this more.
And again, I think we do have the potential
to improve outcomes for participants.
But the biggest, again, lesson learned for me is that this
requires
ongoing investment on the part of institutions,
who,
you know, are thinking about this
institutional duty to do this, but also
on, you know, participants, we do put a burden on them
to follow up with the, you know, recommendations that
we make.
But also, you know, individual researchers
to maintain
contact with their participants to facilitate this.
But again, I think this has the potential
to close gaps in clinical care.
And I think we're highlighting some real systemic challenges,
and alleviating some of those.
So a lot of people have contributed to
some of the work that I presented today,
and I'm very grateful to them.
And you can find all my references here.