GM9 Next Steps Discussion


Teri Manolio:
So we’re getting our act together. As people I see are getting a little bit restless, we
will do our best to be parsimonious and efficient with the time, but we want to be sure we have
a full discussion. One of the things that we do as a consequence of many of these kinds
of meetings is to write a summary manuscript afterward and send it to — we’ve been fairly
lucky — journals have been relatively interested in the kinds of work that we’re doing. So,
what we generally do is have all of the people who were presenters or moderators be co-authors
of those, that requires that you follow IJCME rules of authorship. And you do have to respond,
so if you don’t respond back you don’t get to be an author and we’ll send you a
note back saying, “we haven’t heard from you, we’d really like to have you, please.”
But if we don’t hear from you then we can’t include you in the authorship, so please do
respond. It doesn’t require that you write, you know lengthy and brilliant notes and that,
but you do have to respond to kind of, you know respond back and let us know that you
approve the final version and that sort of thing. So, are you linked? You are linked,
outstanding! All right, let’s see, you’re showing a Mac machine, is that a PC? Female Speaker:
Yes. Teri Manolio:
Would you rather that I do the scribing, okay because I know you’re a person, but — Female Speaker:
I’m ambiguous. Teri Manolio:
You’re ambiguous! Yeah, that’s right! [laughter] All right, so what we tried to do was to come
up with some overarching themes and then we do have several themes from sort of each of
the sessions, but the overarching ones probably are the ones that we’ll capture most of
what we were aiming for. So, what we’ve tried to kind of highlight, and I’ll ask
Carol to comment on these after I go through them is, you know one of the major things
— themes that we heard today in terms of dealing with variants. We kind of divided
this up into sort of variants phenotypes and then people. And in the variants was really
trying to prioritize where we should functionalize. So, the biologic axes to organize these activities,
you might remind people Carol, of what that was in Nancy’s — since it went by kind
of fast, in terms of the way Nancy described it, or Nancy can remind us. Nancy Cox:
So, the — it was like just the last two slides I had pointed out that non-Mendelian genes
but also Mendelian genes tend to collect on certain axes. So, that is to say that altered
expression of these genes are associated with sets of phenotypes on one end and a different
set of phenotypes on the other end. But it’s several genes that have that same pattern
of association. And some of those genes, we already know work in the axis of say innate
immunity wound healing or TGF-beta signaling or a ptosis and growth. And so you can recognize
some of the axes that are piling up a lot of phenome, and a lot of gene — genetically
determined gene expression that help to drive those axes. And people tilt in one direction
or the other depending on genetic and environmental factors. But the idea that if you pile on
those phenotypes, sounds like absolute right. It means that kinds of assays that you could
look at for function are going to be useful for a number — for sort of everything on
the axis, at least at one end of the axis, maybe both ends. And so, really delineating
that can help with the functionality studies that get — that we try to think about and
high through put now. But also with how we ultimately think about treatment, you know
thinking about going after this one gene at a time is exciting because there’s — you
know you can knock down genes now. There’s a lot of drugs that try to target genes, but
in many ways targeting the higher biological pathways is — might be even better right? Teri Manolio:
Great, so those were two ways, there are probably many others in terms of prioritizing genes.
So those that look like they’re piling up phenotypes just from data mining as well as
those that clinically, you know, seem to have some importance either the ACMG-56 or something
else, other ways. So we have Wendy and then Calum. Wendy Rubenstein:
So at the risk of talking too much and losing my introvert status. One way would be to look
at the — well one way that I mentioned yesterday to Doug is to look at GTR for tested genes.
Those tell us about genes that people care about enough to test. But even more so perhaps,
looking at ClinVar, you know the list of variants I took a peek at that represent at least two
labs have submitted it and there’s a conflict. And three — let’s see three quarters of
those are maybe you know one degree, you know you probably care about those. But the other
ones, 25 percent of them are, you know two, three or four degrees of change. So you know
that’s really important, especially with the notion that our, you know our whole endeavor
of interpreting genes can be called into question by a number of groups, insurers, payers who
wonder why can’t you labs agree? You know get your act together, your methods must be
fallacious, so that’s one idea. Teri Manolio:
Calum? Calum MacRae:
I was just going to emphasize that a lot of the things that are on the biological axes,
that Nancy outlined are phenotypes that have very clear representation and fly and worm
and muddle organisms that could then be organized around those types of traits and form and
be informed by the clinical phenotypes. Teri Manolio:
So I wonder, you know we have several suggestions for how to do this, but then there’s kind
of a question of well, “who could do this and how would we get the information to the
people who could best use it?” So any thoughts on that? Nobody wants to raise their hand,
“oh I’d be happy to.” Female Speaker:
I mean it gets done. I mean, a lot of the stuff that Daniel’s been talking about is
publicly available through XAC and through publications that sort of give the whole set
of statistics and I think — so, its just not been applied in some of the ways that
might be useful in these more clinical contexts. And so the connection to what’s actually
publically available data and information isn’t always being made and you know, so
that’s — it’s as simple as that I think sometimes. Teri Manolio:
Well perhaps, and one could say to Doug, “go look in those databases Doug”, and you know
and figure them out — and you know might have the time to do that, might not. You know,
I think back a little bit onto the clinically actionable gene problem, where there were
not only lists of things that might be relevant, but lots of disagreement as to how to weight
them. So might it be useful to have a similar, you know, dedicated small group of people
basically, you know, pull together to spend six months or so, or maybe it wouldn’t even
take nearly that long including the basic folks as well as the clinical folks to try
and prioritize these. Melissa? Melissa Haendel:
Yeah, so I agree with Nancy, I mean I think that, you know, like there is a number of
groups that have done a lot of data integration and really have a whole purpose level perspective
on what to focus on, but it’s the lack of connection with the clinical activities that’s
really missing. So if we can come up with a couple different groups that are doing these
kinds of things and actually all work together with some clinicians who are really invested
in exploring those because we’re not going to go look at every single little database.
We want to actually use the data in aggregate to inform these decisions. And there are different
approaches that different groups have taken to integrate data, but it’s the data integrators
working with the clinicians that I think is the answer for your pilot. Teri Manolio:
Well and perhaps not only the data integrators but the data producers to some degree. I mean
the basic scientists who produced them, those who pull them together and then the clinicians
who are saying, “you know we really don’t care about this particular gene for blonde
hair or whatever.” Female Speaker:
I think there’s a couple of opportunities to build upon some of the groundwork that’s
been laid. So both Caesar and the UDN might provide great opportunities to go back and
do that more focused discussion to try and pull out the use cases from the clinical folks.
I mean we already know that those folks are invested in this area, maybe not focused entirely
on this question because they’ve got a bunch of other things that they have to do as part
of those, you know those grants. But it might be a good place to start rather than going
after something completely different. Teri Manolio:
Great. Yes, Gail. Gail Herman:
So I mentioned this yesterday that I think among the clinical folks is the clinical molecular
geneticists to molecular pathologists because they’re the ones who take the data and are
interpreting it at the laboratory level. And while the clinician hopefully look at that,
scrutinize and maybe have their own opinion, I think those are an important group of people
and should be included. And actually I’ve heard now twice in the last couple of months,
there’s a new job out there, which is the variant caller. They’re in high demand,
computer and biology, and probably make more than most clinical geneticists right now.
But I think this whole area of bridging the informatics and the biology at the laboratory
level were people you definitely want in there. Female Speaker:
I completely agree, but I also think that finding EduCases can often help you to define
what you need to build. And some of the best EduCases for this might come from M.D’s
from different specialties. The NICU one may be a great one. And then we could brainstorm
a bit what these other folks might look like in terms of their background and then have
them work together as part of that group. Teri Manolio:
Yes, Marc? Marc Williams:
So related to that, that could be the role — a possible role for the ISCC since we’ve
had some challenges in terms of figuring out how best to use them. That maybe for this
type of specialty specific case, we could take advantage of the resource that you’re
funding at least to some degree, to task them with a tangible work product. Teri Manolio:
Sure, and for those who don’t remember, the ISCC is a group of professionals’ society
that’s largely focused on education of physicians, but also they are linked in to, you know scientific
experts in various professional specialties. And so that may be a reasonable — so other
thoughts on this? So Erin first, and then Liz. Erin Ramos:
This came up yesterday, but I would just add to the list that the ClinGen gene curation
working group efforts of their identifying — curating genes and — that have, you know
strong, intermediate, moderate, limited evidence for clinical validity. So those on the lower
end where if we had some more functional data might push them over the edge, would be good
places to start. I think that something that they could do, I mean we already make — the
resource needs to be improved but all the assessments that they’ve done so far are
available online through ClinGen — publically available so you can see the functional evidence.
It needs to be packaged better, but we could ask them to do it, but also put it out there
for other groups to take it and go from there. Teri Manolio:
Well one might — give me just a second Liz — one might question whether ClinGen’s
view of the world — while important is the only one that you’d want to include. I mean,
it seems like that was certainly one that you’d want to include in a discussion, but
may not be the only one. However, I think it would be great to give this job to Erin
because she gets everything done. [laughter] And so that’s a good suggestion. Mike did
you have your hand up, somebody over there did. I know you do. Okay, I keep thinking
I’m seeing you Mike, I’m sorry. Liz please? Liz Worthey:
Just to add that we could maybe stack the odds in favor of being successful by using
some of the data that, for instance Nancy presented around what we might find and we
can choose our specialists based on the functions of the genes that look like they might be
attainable. Teri Manolio:
So not to put Nancy or anyone on the spot, but it would be really useful to be able to
use those kinds of data. And recognizing that you guys are publishing and that’s appropriate
and that sort of thing. I mean, is that something that we could work with you to sort of mine
your resource? Nancy Cox:
Yeah, no I think, you know with all the caveats, the kinds of phenotypes we’re best powered
for are phenotypes that come to tertiary care medical centers like Vanderbilt and require
blood work because those are the ones that get into BioView and so forth. That said,
it’s just a list of phenomes right? I mean, it’s a list of phenome-packets, as it were,
that coincide with a set of top genes. Those that accumulate disproportionate phenome and
those that are loss of function depleted, right? So, yeah. Female Speaker:
Just to follow up on that, so what would be cool and what we were just talking about during
the break was, you know if I actually mapped your phenotypes then we could pull from our
system all the most similar sets of phenotypes from the model organisms and find all those
model organism biologists that might be most related to those sets. So that might end the
pipeline there at the biologist, whoever said that earlier. Teri Manolio:
Good. So it sounds like this could be one deliverable, for example we’ve heard several
ways now that we could consider prioritizing these. It sounds like we need to get people
in the room who, you know sort of know how these kinds of things fall out and then do
some debating with clinicians and basic scientists and data integrators to figure out, “okay
how do you actually mesh all of these together?” And it may be something that, you know once
done, may need to be re-visited at some point as knowledge accumulates and all. But it sounds
like people are reasonably enthusiastic about that being a useful thing. In the meanwhile,
you know in the six months to a year that it might take us to do this, Doug needs something
to do so — [laughter] Douglas Fowler:
I have lots to do — Teri Manolio:
Yeah, I’m sure you do. But I think we heard yesterday the ACMG56 would certainly be a
list to start with — Douglas Fowler:
No, and I’ll — I mean I’ve already talked a little bit with Carol — I mean I think
that’s the plan is to go back and look at the — annotate that list with what we think
is doable and then you know, from you know your data where we shouldn’t look because,
you know mutations that are just likely not to be important. So I mean that’s something
we intend to do in the next couple weeks anyway. Teri Manolio:
Okay, Carol anything else on that aspect of variant functionalization? Carol Bult:
We have another slide, but I don’t seem to be able to advance — Teri Manolio:
So the evidence to move variants from an unknown to known function classes, so that, you know
the question was I think — it had come up, “can we define amongst us, you know some
criteria or some kinds of evidence that actually help you to move something from a view as
either to benign or to pathogenic?” Yes? Female Speaker:
I have a question. So in ClinVar, does it get updated or reviewed? You know if stuff
is old and something — you know data from 2010, 2011 said it’s pathogenic and now
there’s more data. How do you get stuff out, or what criteria are there to, you know
condense it down to this is what it really means, and who does that? Female Speaker:
I can address that, I work on ClinVar. So the major way to get data updated is for the
submitter themselves to update it. We treat them like GenBank records, where the submitter
owns the data. Another way is for an expert panel to rule — but again that takes a lot
of time and effort to get those panels started. And then to compensate the people for their
time to do those expert opinions. We have had a discussion, particularly with the ClinGen
group about what to do about literature reports. Say old literature reports where there was
a single family and the group said, “yep that looks pathogenic.” And now you get
a lot of data from contesters, and it really looks uncertain or even benign. I think this
will come up for other types of assertions in ClinVar as well, not just those for literature.
I think if we have labs who don’t update regularly or research groups to submit, we’re
also going to get out of date submissions. So we are thinking about how to handle those,
maybe move them into a legacy category and then they wouldn’t contribute to an aggregate
value, something like that, give a score to conflicts. We’re open to feedback about
what we are — I don’t really think removing it from the database is right because it is
an archive and you do want to know and see what was the history here. But I do agree
that we do need a way that when we have better — newer information — how can we take out
the noise that was just incorrect information from the past. Teri Manolio:
Great. So in terms of the evidence needed to move variants from one class to another,
I don’t know that anybody has any, you know set ways of doing that or even ideas of how
to do that. I think that’s one of the big questions of dealing with VUS that our meeting
was meant to identify. On the other hand we probably should as a field agree what it is
scientifically to do that so that people who look in on what we’re doing have some view
that, “yes we do know what we’re doing, we have standards and criteria that we apply
in a systematic way.” I think would make it much easier for the world to interpret
the findings that we have. Yes, Howard? Howard Jacob:
Hi. I guess I wouldn’t overemphasize that part because — at least with currently — there
are so many different uses for the exact same variant ranging from no action through to
eligibility for a clinical trial. And you know we have trials in oncology right now
where any non-synonymous or other likely functional variant in the FGFR1 gene, makes you eligible
for a trial even if it’s a VUS. And so, you know people don’t look at page seven
of the VUSs, and they’re missing out on a trial application — opportunity for the
patient. So something — you know that would move it totally — a VUS would not be in the
clinical utilities section based on our logical thinking, but yet there would be a clinical
option for that VUS patient. So, I guess if we over bake this cake we will wish we had
pie. No, I don’t know — yeah you can know where to go with that, but we need to do more
work on that. Teri Manolio:
Yes, Marc? Marc Williams:
Yeah, and that’s exactly what we’ve done in our patient phasing genomic test report
that paired patient provider phasing genomic test reports, is that we have linked in there
information about — specifically for variants of uncertain significance or for a clearly
defined clinical disorder for which we haven’t found a genomic variant — information for
the patient and the provider about how they could be involved in clinical trials of the
research connecting to specific investigators. And so I think they’re really is an opportunity
to crowd source that type of — so if I had a list of people that said for this particular
gene send me your poor, your tired, your whatever — wealthy. That doesn’t work in central
Pennsylvania, I’m sorry to tell you Howard. But we could easily then embed that in a report
and then measure to see how that is being used. So I think, again, that’s a type of
solution that could be broadly implemented across systems where we could test how would
that democratize the process. Teri Manolio:
So I guess I’m not as clear as I should be. When we were talking about the evidence
to move something from a VUS to something else, I wasn’t saying that being a VUS is
bad or that we shouldn’t report it or whatever, it was should we agree as a field on what
evidence is needed to do that? Is it a mouse model? Is it an IPS cell that completely recapitulates
the phenotype? You know whatever it might be, or is that a fool’s errand because it’s
going to differ so much from each different setting? Howard Jacob:
I wouldn’t say it’s a fool’s errand, but I’m not sure it’s our errand. It seems
to me that groups like ACMG and AMP, and I guess Deborah’s not here any longer, you
know they have groups that are, you know tasked with doing that. And then there are gene specific
groups that are sort of tasked with doing it. So I’m not sure we would need to accept
that as our responsibility. I think we could accept the responsibility to say what — there’s
a lot of folks out there and probably most of the data associated with those folks is
not being captured in any sort of systematic way, so that the groups that are trying to
do their work are doing it with inadequate data sources. And so how could we facilitate
the data associated with patients receiving VUS? That’s how I’m thinking of it. Liz Worthey:
I think that’s true, but I think if we’re the representatives of the genomics element
— the technology people — then it would be good for us to at least engage them in
a dialogue to make sure that the ACMG have whatever they might need from us to make the
decision better. Peter Robinson:
Yeah, I mean think my point’s pretty well aligned with Liz’s, is that although I agree
ACMG will be the ones who end up defining the criteria because that’s what will guide
what’s done in clinical practice. We do have a responsibility to make sure the resources
we build are built in such a way that they can use that. And it’s definitely one of
those things that — I mean XAC is — we’ve done our best to make the data available,
but there’s many ways in which I think it could be improved to make it more accessible
to clinicians. And I think for the community as a whole, making sure that we have that
conversation very carefully with ACMG and with clinical groups — not just with the
sort of the scientific side of that — about what do they actually need to make those clinical
decisions and then how could we make those resources better? That would be extremely
useful, we can’t defer all responsibility to them. Teri Manolio:
And just before I call on Dan and then Calum, one thing to think about is whether ACMG and
AMP cap are including basic groups or data generator, data integrator folks in their
working groups. And my impression is probably not to the degree that they might. And is
that something that we should bring the message — those of you high up in the ACMG — not
looking at anyone in particular, but those of you who are, might bring that message back?
That GSE’s groups are being put together, I mean, I think they’ve been very good at
engaging the pathology community in that. They may not have been as aware of the need
to bring in basic science. Did you want to speak specifically to that point? Marc Williams:
So I think they are aware of the information, but the difficulty that they’ve had is that
engagement piece because it’s not the home for most of the folks that are in this. And
so there may be a matchmaker, if you will, function for this group to try and facilitate
that. Teri Manolio:
So we would ask those of you who have those kinds of ties maybe to make those suggestions. Howard Jacob:
Yeah, I think they’re certainly aware of it, in principle. I’m not exactly sure they
know the best entry into that system. And so I think meeting half way on that they would
— my prediction is they would be very amenable. Teri Manolio:
Great. Gail, just on this point and then we’ll get back to — Gail Herman:
Yeah, no just on the — so one way would be for some of the more basic computer scientist
people who were trying to implement this and use it clinically to join our organization,
come to our meeting, join our workgroups. We can certainly encourage the laboratory
groups to, you know include you but I think if you’re there it would be well received,
and if you speak up you’re much more likely to get involved, you know and be asked to
be on a work group. Be careful what you wish for, they’ll put you to work. But no, I
think it would be great. I think, again the most important thing is everybody talking
to each other, so you need to hear what issues they have and they need to hear what you can
do. So I would — email me and I will give names to people. Teri Manolio:
And so just to dispel any misperceptions that I certainly have encountered in talking with
people about the college, the college is open to people who are not geneticists, not physicians,
not PH.Ds — Gail Herman:
Absolutely! And it’s even really cheap, but anyone who has an interest in genetics
and genomics can join. We have different categories, but affiliate scientists, things like that
so we would welcome you guys. I mean if you could come and you do software, you know do
a demonstration at one of our meetings. I know ASHG is a lot easier, but at either one
because some people go to both, some just go to ACMG. But to have more involvement from
this group and hear what the clinicians, the laboratorians need would be really great and
important. Teri Manolio:
Good. Dan, have you forgotten what you were going to say by this — [laughter] Dan Rogen:
Almost. You know it’s filtered through Marc, but I’ll remember it in a second. So, three
comments, one was actually a question for Marc and that was, for most VUSs do you report
them or do you not report them? I understand the report the VUSs where there might be a
clinical trial, that might be really interesting, but in general our inclination is to sort
of avoid those. Marc Williams:
Well the clinical laboratories generally will report a VUS that meets some threshold that
they think should be known about. Now in our internal project, in our tech zones we are
not reporting VUSs — Dan Rogen:
Right, but that was the question, I mean I know — if we send off an IN channel — panel,
we get a VUS back — Marc Williams:
It doesn’t mean we’re not attending to them, but we’re not reporting them — Dan Rogen:
Attending to them is a — sounds like a huge time sink. So that was one comment, the second
comment is that I think this sort of business — Teri’s question of you know, do we need
to set up criteria for what it is that moves a VUS from a VUS to something else falls into
this black and white versus shades of grey question. I think there will be variants where
you could probably, if you try hard enough, demonstrate some function that is different
from reference. And in some person that might be enough to be the last gene in an oligogenic
or polygenic trait that pushes them over to having the trait. So, yeah I think it’ll
be very hard to sort of ever say, you know it is or is not a VUS. What we can say is,
you know across the spectrum of activity this looks like it is really bad, or sort of bad,
or a little bit bad, or not bad and we can’t find any badness at all. And then the third comment was in response
to Gail. That it seems to me that as work groups get formed and go forward — and I’m
not volunteering for anything — I know. So you need to have at the table, not just the
pathologists, I mean it bothers me that the pathologists are driving this because in many
ways the, you know their expertise is in sort of generating the high quality reproducible
assays, but — and delivering them into the flow of clinical care. But when it comes to
the genetic expertise, the basic science expertise, the informatics expertise needed to aggregate
all that and then the content expertise. So I’m, you know the cancer guys, or the craniomyopathy
guys, or the arrhythmia guys, all those people need to be at the table. Perhaps chaired or
organized by the pathologists, but I’m not — I think that it’s a daunting task and
that’s why I’m not volunteering. But I think there are many constituencies, and I
don’t want us to forget the content experts because at the end of the day that’s going
to be a big part of this. Gail Herman:
Can I just make one indirect response? I’ve been talking about AMP, and the Association
of Molecular Pathology. And yes, while the pathologists are important, when we’re talking
about the molecular folks, those are boarded through pathology but do an extra year or
an arm molecular program, which is two years. It’s changing. So talking about molecular
people for cancer, and if it’s somatic, more often AMP will take a lead and they will
involve the tissue pathologists and our organization. But we’re clearly talking about geneticists
and molecular geneticists taking the lead on these. Teri Manolio:
Okay, great. Calum? Calum MacRae:
I was simply going to say that if you think about the spectra of criteria that we’ve
used over the last 25 years for inferring causality with variants. It’s something
that actually encompasses the entire community that we want to engage in this space, from
the clinicians who might be managing a family and look at segregation all the way through
to the fundamental cell biologists. And so I think finding an infrastructure that allows
you to engage those communities, leaves open the spectrum that Dan pointed out of, you
know a grey zone where there may be functional inference for one particular prior context,
but not for all. And then even just thinking about what Howard said, and imagining that
actually — in many ways some of this will be empirically defined as the genomes move
into clinical practice, in terms of exposures that we’ve never measured before or conditioning
variables that we haven’t even thought of. And so, in many ways that’s — the infrastructure
for doing this is at the core of what we’re trying to achieve. And so thinking about how
it’s set up initially so that it does all of those things in a graded and simple, incremental
way is actually not an easy task. But I think the first thing — as I was thinking
about it — that needs to happen because it’s something that doesn’t even happen in our
specialist centers, is an infrastructure for efficient bidirectional exchange, a phenotype,
a [unintelligible] genotype. I think that actually is a rate-limiting step. And if you
had that, then the people who are at either end of that infrastructure could vary depending
on the resolution and the granularity of the question. But fundamentally, that is a necessity
that we won’t be able to move beyond. Teri Manolio:
Great thanks. And we will get to, sort of our third topic is people, which is the interactions
amongst us. So, the — I have a feeling then on our third bullet, in terms of criteria
for evidence to move variants to clinical utility, you’ll tell us that that’s even
more the domain of these other groups, ACMG and AMP. Am I wrong on that? Yes, Calum? Calum MacRae:
So I may have — as I want to do — overemphasize one part of what I’m saying over the other.
I think everybody needs to be involved in that. I think having your — for example if
you think about just the fact that there are criteria we could lay out — I mean, there
was a paper I think in 1989 in Nature Genetics, looking at the criteria for inferring causality
with a Mendelian disorder, you know that’s something that still holds true today in clinical
practice. That’s what we all use, that’s what I use with every family that I ever see.
To the point that I won’t even open the genotype until I have enough individuals to
actually generate a LOD score that’s sufficient for that family, but I can make clinical decisions
based on it. But in addition to that, I think having the average clinician understand the
spectrum of evidence that underlies a genotype/phenotype correlation is important. So I think it is
important for us to set up what those criteria are, and then have everybody collaborate around
essentially generating the data to fulfill those criteria for every variant. Does that
make it — maybe I’m restating what I’m saying too generally, but that’s what I’m
trying to — Teri Manolio:
No, I think I follow you, but it sounds like you’re more in the camp of yes, let’s
try to define what those criteria are. Where we heard earlier for the evidence for moving
variants from one functional class to another, was something that we as a community shouldn’t
do, we should see that to our professional societies that are sort of, you know vested
in doing that kind of work. Calum MacRae:
So I would just say that I think that if we do that, we’ll wait forever because — not
because of anything other then the number of, you know n of one classifications, we’re
going to — we need a system that classifies. And my point would be simply that that system
is the healthcare delivery system, and not a series of professional bodies. That’s
all. Female Speaker:
Just along those same lines, I think we look at the guidelines that came out from the ACMG
around how to determine whether something is pathogenic, so that many groups are interested
in that question. The ACMG put it together. I know for instance informaticians at the
various sites are building the tools that will allow us to test those guidelines. The
hypothesis can come back with recommendations on how it changes. That can happen really
slowly sometimes, which I think is Calum’s point. If we as a community can work out how
to make that happen faster, that triggering from one group to the next, which may mean
all the groups coming together. Then that’s what we should do, so that there’s lots
of questions around what those guidelines actually are. But a big part of the work is
how to get people to come together to do that process faster. Female Speaker:
Just another quick comment in there. So when you look at the Caesar data, for example where
there’s the — quite a bit of inconsistency between annotators. It’s all about the basic
research functional validation data where we see the most of the discrepancies in that
functional data. So if we can include the basic researchers in that process, like you
just mentioned, that would be really key to building better classification systems for
the evidence models in that particular part of the ACMG guidelines, which are really problematic. Teri Manolio:
Yes, Wendy? Wendy Rubenstein:
So since we’re talking about the ACMG interpreting sequence variants guidelines, I, you know
read them a number of times. And then I looked back at them recently with another lens, which
was to say, how much does phenotype count? So if I know lots of HPO terms and I’m pretty
sure that my patient has this condition, but it’s been described as a VUS and I, you
know enter all those terms into ClinVar, will that help? There’s really not much, if any
role that I could find in, you know for clinical phenotypes. And in retrospect, maybe that
makes sense because it’s laboratorians that are making these determinations with the data
they have. And maybe that comes back to the issue of this collaboration that we’re talking
about where people have these, you know different accesses to different types of information
that need to be integrated. Gail Herman:
Maybe I can — Bruce is going to have to come in here at some point too because I don’t
run a lab. I found — I’ve tried to get through that whole document several times.
I’ve gotten a little further each time. It’s very complicated, but the one point
I will make and I think I made it yesterday is, the bar was set incredibly high because
we’re going to test healthy individuals. And if you start monkeying around — and it’s
meant for people that may not have any phenotype whatsoever, and obviously the phenotype and
other stuff will help, but I think well — I’ll just stop there. That it was set so that it
would work in normals and to call something pathogenic, you had to have, you know stuff
way over to one side. That doesn’t mean that the clinician, or someone else couldn’t
say, “you’re calling it a VUS but based on everything I know, it’s pathogenic.”
So, Bruce I don’t know if you want to add anything there? Male Speaker:
Yeah, no I don’t think I have anything to add to what you just said though. Teri Manolio:
All right, so I know Melissa and Liz want to comment on — sorry Wendy and Liz want
to comment on that. But I’d really like to move to our second topic because we have
three and we’re already, you know 20 minutes to three. So, you’ll have your chance when
the manuscript comes out and we can debate it in email. So Carol, anything else on that
one? That’s enough. Carol Bult:
Nope, moving on to the — Teri Manolio:
Moving on to phenotype. So in terms of phenotype, I think we all agree that there’s a need
for deep phenotyping and those with unusual genotyping, sort of the genotype first approach.
How best to do that. I think you heard Daniel say and others, you know it would be awfully
nice to come to agree to some standards that we could come up with on that. Is that something
that we should consider as a group? Might be, you know something we’d like to commission
a small group of, you know diligent volunteers in their copious spare time to do? Or could
we task some group that NHG or IR already has on going with doing this? Yes, Mike? Male Speaker:
I think one way to move the ball on that would be to potentially work with some journals
that might be interested in, for instance, taking on the task of inviting a group to
submit a paper on the minimal phenotype for x, you know right now the ACMG list has mostly
cancer pre-dispositions and cardiovascular disease. So there could be an oncology journal
or a cardiovascular journal that would say, you know “we’d invite that.” And then
that would go into, you know peer review, get submitted and people could then react
to what a group of authors had done. But, I mean that way you wouldn’t have to do
all the organization from this end and it would get out in the public, there being some
debate and you’d end up at a better place than we are right now. Teri Manolio:
Great. One challenge with that is that would be — it sounds like starting more work with
the phenotype then with a genotype. So if you don’t really know what you’re — the
genes that Daniel was talking about are a loss of function variants in genes that have
no known function. So you’d need everything and nothing, you know — Male Speaker:
Right, you could start that with a common condition. But these genes with unknown function,
I mean that’s going to be a series of 10,000 run offs. Teri Manolio:
Great. Calum and then Melissa? Calum MacRae:
So I mean, I would sort of turn Mike’s comment on it’s head in a little way. I think that
is necessary, I think it would be very useful for the siloed conditions. Because I think,
for example, you know most folks that see vascular disease don’t think about TGF-beta
disorders also involving lung — to let people go to the operating room and then suddenly
they realize, “oh I wish I had done their pulmonary function test before I send them
to the OR.” But the converse is true for the gene first, where I think you need — this
is where I think Nancy’s point about universal phenotypes alone. The other thing that’s
quite interesting is, just even hearing Howard talk about tumors, somatic tumors, may be
an assay that is being continuously studied in a very clear therapeutic context where
you also have universal perturbations. That might be one assay that is universally available
for all pathways. What happens to this pathway in a tumor setting when you hit it with a
particular agent that targets this target’s ACE inhibitor. But I think in addition to
that we need assays that are — I would hesitate to venture that we need a minimal set of some
assays to cover the fundamental biological axes, or at least a subset of those in a way
that could advance us a little bit farther that’s not driven by the particular clinical
silo that you happen to end up in. Teri Manolio:
And before I call on Bruce, Rita could I just ask you if you could ask the hotel — they’re
doing a great job out here with trimming whatever it is they’re trimming, but if they could
just give us another hour, that would be great. Thank you very much. Yes, Bruce? Male Speaker:
I certainly see the rationale for having some standardized deep phenotype. I guess the part
that makes me a little bit uneasy is what do we mean by deep? In particular, I’m thinking
that as a clinician you often don’t know what you’re not looking at until after you
have a specific question addressed to you. So the phenotype that you provide for any
particular individual may still ignore some of the critical things until somebody has
actually identified a gene and then they go back and they ask you, did you look for this?
And you might never in a million years have thought to look for that. And so I worry a
little bit that we’ll send people off on an exceedingly time consuming and labor intensive
process, and still not get what we really want. And I think the power may really be
not so much in deep phenotyping as in this notion of iterative phenotyping. Keeping some
kind of two-way conduit to the clinician, so that as questions come up they can be answered.
I think in the long run — it reminds me of a term that was used by one of my mentors,
something called unproductive diligence. Where you spend tons of time doing something because
someone tells you it’s worth doing, and it’s actually not and what’s much more
useful is targeting questions. But you don’t know what those questions are until you know
the target. Male Speaker:
I’m not sure if this is completely obvious, but I think it’s important for everybody
to understand how phenotype information is being analyzed in systems such as HPO and
Monarch and others. And it’s not just a matter of capturing phenotypes from your patients,
but the resources and the database basically act as a sort of pryer or a pattern against
what to search. And for instance, as a result of the issues with OMIM, we’re now completely
struggling to keep our database up to date and on the other hand — well for instance
100,000 phenomes is depending on this to some extent, and further diagnostics. And I think
as we move out into sort of common disease precision medicine, it’s — there really
needs to be a way of defining how — what models are actually useful for the community
— how to find a sustainable model because it’s really not that trivial. Female Speaker:
So just to follow up on a couple of those comments because I think it — you know one
of the goals we had for the phenotype exchange standard was that you could really put anything
in there and decide, depending on what community you’re in, what kind of focus area you have,
you know how deep is deep for you? But that you would then also use that contextualization
of all pheno-packet data out there, all the phenotype data that’s been made available
to help define that you know, what that next phenotype is to look at based on the pryers
that are in the system. And so — but I think the one thing that we hadn’t really thought
so much about yet was how to do that iteration part because I think that’s also really
critical. So I’d like to think more about that. Secondly, we have been working with journals,
and in fact the format was designed first with that in mind. To be especially exchangeable
in the context of the journals and the clinical labs where the sort of just basic level information
that we want about phenotypes coming out of these — in these two particular use cases
where it was so critical computationally and we just aren’t seeing it. And so now, the
ideas that the labs and the journals would be providing it, and we have some journals
that are already piloting some early versions of that, so there’s a lot of interest from
the journals already. Teri Manolio:
Great. Moving on to the second one, just one brief last comment on this, Calum? Calum MacRae:
So I was just going to say, I agree with everything that’s been said. It’s a tough thing to
actually execute, but if you take away the one thing that we’ve learned from genomics,
it’s that lack of bias and comprehensiveness are actually incredible valuable. And so anything
that we do has to reduce that bias. Everything that we talk about in the iterative or clinician
based phenotyping is really just re-enforcing the biases of the past. Teri Manolio:
Well said. In terms of Eomer phenotypes or clinical phenotypes, you know it’s sort
of a desiderata, I think, desideratum, is for clinical phenotypes to be more data driven
models of clinical features rather than, you know designed for billing. I think any of
us who work within a medical record system know that that may be difficult to achieve.
I think one question for us though is, is there some way to potentially through NLP
or other approaches, the kinds of phenotyping information that would be useful in trying
to functionalize without necessarily relying on billing codes or other things? And that’s
something that I know eMerge is struggling with. Maybe not quite, you know formatted
quite that way but at least something that we’re working with. What do people think
of this? Is this just sort of so naive that we shouldn’t even put it down, or is it
something that we should try to strive for and think of ways to approach this. I see
Marc isn’t there, he usually jumps up at comments — Male Speaker:
A colleague of ours from Monarch tutors [unintelligible] at the Garvan Institute has developed a text
mining system that can take, for instance an op-ed article or a discharge note and extremely
reliably extract HPO terms. And basically what this does, it pops up a window such that
you can say yes or no. So the entire process of actually capturing the phenotype of this
is gamified a little bit, and maybe it might take you a minute if you’re sort of familiar
with a disease. So it’s actually not that difficult to do. Teri Manolio:
So this might be something that could be implemented say in interested sites that, you know wanted
to refine their phenotypes. I mean, it would have to be an extra step I would think. Yeah? Calum MacRae:
Or in collaboration with societies, you know I can see of that was set up there might be
certain journals or certain lead areas that were pitching to interested members of the
American Society of [inaudible] or the European Society of [inaudible] that was not a — that
was supposed to be a blank. And nothing else, don’t read into it. So you end up getting
gaming expert curation gaming as opposed to, let’s get all the medical students involved.
And they’re like, you know all right where’s the brain again? Yeah so, you know it’s
an opportunity to really reach out and that might get us more involved with some of these
societies that are hard to get into. The cardiology folks have everything solved, but they might
be one to get involved with this. So there’d be some opportunities there. Teri Manolio:
And we’ve heard a lot about in the past couple of days, about patient derived phenotyping.
Were you even thinking that that might be something that we need to have an entirely
separate meeting on, as to how to engage patients in doing this? It seems like there are heads
nodding around the room for that. I understand, Peter I think I heard you speak a few weeks
ago about some patient derived phenotyping information that you’re building into HPO.
Did I hear that correctly? No, could you? Peter Robinson:
I just assumed I had such a loud voice that everybody would understand. Melissa and a
few other people at Monarch led a project to translate the HPO into the [unintelligible]
and so this is available, I think now already on our website. And so we’re talking with
a couple of patient organizations, I’ve been talking with a few in the UK and I think
Melissa has here in the states and we’re hoping to pilot this by the end of the year
in at least one site. Teri Manolio:
Okay, anything more we need to say on that one then? It seems like people agree it’s
a good idea. And then, you know as we’ve heard many times the need for common vocabularies,
mapping across vocabularies and across databases and resources. Something that I think we as
a community can endorse and be positive about, but would have to happen between each individual
vocabulary and resource. And it sounds like it’s on going now. Are there ways that you
can think of that we can help to stimulate that? I mean, obviously, you know funding
always helps but are there — you know is there an organization of oncology groups that,
you know we could approach and say, “gosh we really hope more of this goes on.” The
National Library of Medicine I know is leading efforts to do this kind of thing. Male Speaker:
The UMLS actually just imported the entire HPO, so that’s been an important step and
we would also welcome more contacts with the NLM in the future. John Madison, who’s at
Keiser Permanente has also been speaking with people at SNOMED as a part of sort of a ga4gh
issue. And I’m not entirely sure if we can do this without being basically swallowed
and digested completely, but I think it would be great to have HPO being mapped into SNOMED
in such that you can use this in an EHR setting. Have the sustainability and etcetera that’s
necessary so it’s not an academic project, but still be able to extract easily and then
use this for biological translation and research. Laura Rodriguez:
Sorry just a couple follow on comments. So, you know I think one thing we were talking
a little bit at lunch is that it would be great to have more interaction built in to
the people who are actually using the vocabularies. So the Mendelian Centers, you know the UDN,
lots of other resources are using these vocabularies, but we often don’t get a lot of feedback
and so having a greater interaction would actually improve those resources for everyone.
The other thing that may be something that NIH in particular can help us and others with,
is the licensing issues because these are all open resources and there’s often many
dependencies and things like that. So, you know if we want to try to keep the data available
on the ontology integration available, that there’s actually a lot of complexities that
are really hard to overcome on an individual basis, but the resources collectively could
take of. Teri Manolio:
So I’ll point to my colleague in the policy area Laura, just in terms of licensing in
that. I don’t think we have anything other than kind of a moral-suasion argument that
we can do. And sometimes, you know the core tutorial languages we heard about some years
ago, but there’s probably not much more other than that that we can do. Female Speaker:
Correct, is the short answer to that question so — Teri Manolio:
On the other hand, there is something the journals can do, so Karen do you want to maybe
comment about that? Female Speaker:
Well what I was going to say is that I think that common vocabulary, I think doesn’t
just include discoverability, but as been said before, that there needs to be — I think
for it to be useful some kind of quantitative standard. So whether it’s, you know — or
just if , so for example, if somebody reports in a model organism or I would say even in
a human, something in a basic science journal, a variant associated with a phenotype or a
set of phenotypes, you know what is the basic information that needs to accompany it for
it to go into a database and actually be used? Rather then you know, I appreciate now it’s
being lost in PubMed in a sub-optimal way, but I think creating lists and database without
something where you can then use it quantitatively — or isn’t going to be that much more helpful. So, what’s the standard by which you say,
“was there severe phenotype or not?” Is it a percent different from [unintelligible]
and how do you say, you know not only what phenotype but what background of, you know
strain if it’s a model organism or, you know probably demographic or ethnographic
information is important too for humans. So I thought it was not just a common vocabulary
for discoverability, but a standard set of information that then becomes mineable. Teri Manolio:
So something that might be considered, and this was considered certainly by a group of
journalists early on the GWAS era is what, you know what would be the standards for data
deposition? And if there are each of these databases, if there are particular things
that they have to have. Presumably, that’s somewhere front and center on your website
or elsewhere, but maybe not. I don’t know. And I wonder if that may be something that
would be worth the journals sort of helping us to promote? To encourage — because you
already are very helpful in saying, “we’re not going to publish unless you’ve deposited
your data,” but then there has to be sort of some agreement as to what that minimal
data set should be. Does that make sense? Female Speaker:
Exactly, some kind of model. Teri Manolio:
Okay, I do want to get on to the last topic so we’ll let Mike have the last word on
that. Male Speaker:
Yeah, I’m wondering if genomic medicine 10, or 11, or 12 should be an invitation to
editors to figure out standards that they could promote. I mean, editors across biomedicine
have pushed lots of changes by having standards in all different ways. So it might be something
to consider. Teri Manolio:
All right, and — Carol anything else on that? Carol Bult:
Nope. Female Speaker:
So the last topic — the last overarching topic that Teri and I used to, sort of, integrate
all the discussions over the past day and a half was this topic of bridging the gap.
And there’s two slides on this topic. So some of the themes we heard were related to
data sharing and resource integration. And I think one of the — one of the things I’ve
heard over and over was somebody would say, “Oh, we need a standard for this,” or,
“Do we need a resource for that?” And somebody would say, “Well, we’re building
that,” right. So a lot of things actually exist and one of the challenges really is
awareness of what’s out there, how to use it, how to access it. And this relates to
variants but many other things, as well. So there’s building those data-sharing mechanisms
and resource integration, but increasing the awareness about what’s out there. And so,
ideas on how to achieve that goal would be welcome. So are there any thoughts on that
particular aspect? Oh, yeah, Rex [spelled phonetically]. Male Speaker:
So typically, one of the problems with this is — it’s not a knock on the people that
develop them but, you know, they develop them for their own use or because they thought
it would be a good idea but didn’t really put much thought into the interfaces, the
quality of the interfaces, the usability of the site. So I think, you know, thinking about
some way of helping make them usable is really, really important. Because I think often what
happens is you go to this site and it’s not really clear how to get the data out and
it’s not really clear, you know, how to do the searches. So I think that should really
be high on the list to think about: how do we ensure usability and accessibility? Teri Manolio:
Okay, anything else? So other topics on bridging the gap, you know, going back to the very
first talk, Howard’s talk about understanding the perspectives of the two communities of
basic scientists and clinicians, and you know, and how it opened up — this up. It was, you
know, what evidence about a variant would be appropriate for a clinician to really consider
it, to put it in a medical record, to take it serious? And this lead to a lot of discussion
about the differences in mindsets and cultures in the two communities. And that is — that
is one of the big gaps that I think exists for us moving forward. And that led to this
discussion about, you know, the requirement that a clinical study is the only acceptable
evidence that I’ll take versus what Les had raised about, well, we’ve got to — we’ve
got to embrace the ambiguity. We need to — we need to have this conditional probabilities
and the predominance of evidence for what a variance does — variant does and what it’s
clinical relevance might be. And that seems kind of a tough nut to crack. So thoughts
on what the role of this community could be in helping facilitate these cultural gaps
that we have? Howard and then Calum. Howard Jacob:
I think the — some level of clarity of what the evidence is would be useful. I think that
guidelines have their use. But when you’re — it’s really the acuity of the problem
that helps drive this thing. So if I’m asked, ‘How do you treat colon cancer,’ I can
tell you the NCCN guidelines and we helped write them. If your mother has now — it’s
now at least third-line therapy where we’re post-guidelines, we will have a very different
discussion offline. And so, the level of evidence needed for frontline, you know, generalized,
not a — not a real patient in front of you is very different from the acute action. And
I think even in Howard’s example, you know, if there was a specific need that one of the
nephrologists had to make a change, it would have been a very different perception and
use. But it was more just in a general term. So I guess I don’t know how to — how to
put that into some words on there, but we need to present enough evidence that when
one has acute need one can synthesize a way forward. But if we go to the point of making
too many guidelines, people will forget that there is that flexibility that happens in
patients who are, you know, beyond our general approach. Teri Manolio:
Calum, and then Howard, and then Liz. Calum MacRae:
I was going to say something, you know, almost pathologically identical to what Howard said.
But in — [laughter] But for a semantic variance, we’re seeing
exactly this with, you know, the VUSs in — now that I think about it, in Laminasee [spelled
phonetically]. There are clinical trials for people who are in maximal therapy, have a
genotype that may not be completely interpretable in — that’s positive for a Laminasee variant
that looks like it’s either pathogenic or likely pathogenic. We’re in clinical trials
that target those types of pathways. And so, I think there are incremental steps by which
we can genomics into to the clinic. I do think that — just to add one other thing,
which I always seem to be doing this afternoon, I see, is one of the ways of actually bringing
everybody around the table is to put everybody in the same place. It’s almost like Esperanto
for translation where you basically say, “Here is — here is a new phenotype.” And that’s
— that may be all I’m really saying when I suggest that we have some of Nancy’s axes
be the traits that we think about. If we everybody who looks after patients began to think about
it, how different conditions are related by their unifying biology, and everybody in the
model organism community began to think about those uniform biologies in terms of the diseases
which they impact, that itself might be a small and incremental step. Teri Manolio:
All right, Howard. Howard Jacob:
So yesterday’s conversation, and Les’ and Bruce’s conversations about use cases
for — I’ll use that word; they didn’t say that — of how do you deploy this clinically?
So in today’s meetings while, you know, I’ve been listening and paying attention.
I’ve also been looking at some of the things that are out there and are not out there.
So for example, the ACMG has got these guidelines. So if, you know, if you have TSH, and you
have a gene mutation that’s — you have different levels or you have different family
histories, there’s actually algorithms that then say, “This is how you should use it.”
And I think one of the challenges we have as a community is we haven’t put some use
cases together or a checklist, whatever we want to call that, that then gives some directionality
around this. It’s so open-ended. “Well, you could use genomic sequencing for X or
you could use it for Y.” And I just wonder if it’s not maybe a time to put some stakes
in the ground and put some things out there about: this would be directionality and how
you could — you could use this tool with the ambiguities built into this. Because I
think right now that’s a challenge, is how do we get that. Now, I don’t know how you
do that. You know, that could be, you know, you get into the level of guidance on this.
But maybe that’s a paper that comes out from this group: is a position paper on things
to think about around that, how you could deploy that. But I think otherwise, it’s
just — I can’t figure out how to solve the problem because it’s so open-ended.
You want to do this for cardiology, you can do it for cardiology. You want to do it for
rare disease, you can do it for rare disease. So I don’t know. Bruce has his hand up so
he’s probably got a good idea. Teri Manolio:
Do you mind, Liz? So Bruce and then Liz? Male Speaker:
[inaudible] Teri Manolio :
Okay, so Liz and then Bruce. Liz Worthey:
I found myself in an unusual spot where I’m in complete agreement with Howard. [laughter] It doesn’t — it doesn’t happen very often. Male Speaker:
[inaudible] [laughter] Liz Worthey:
It would be something stronger. Well, yeah. Male Speaker:
[inaudible] Liz Worthey:
So just to say that guidelines tend to try and bring all possible occurrences into one
set of guidelines. And what then happens is the guidelines get implemented, and they get
broken, and then you work at what really works and finding some use cases to test some of
these guidelines is probably what’s necessary. And what we could do is, again, bringing people
together focused around those use cases. Female Speaker:
Bruce? Male Speaker:
I think part of the point that I was trying to make yesterday and I think Les was, too,
is that physicians are not unaccustomed to working in a world of ambiguity. The problem
seems to be that, at least sometimes and for some clinicians, they don’t pick up on the
ambiguity that is reflected in a genetic test report. And it seems a little bit incongruous
because, I always tell physicians, a variant of unknown significance means it’s a variant
of unknown significance. How much clearer do you need to be? And it doesn’t seem to
sink in because somehow I think they just don’t — they almost view that as fine print
and don’t read that far into the report. So I think the issue here isn’t trying to
reduce the ambiguity. I mean in the long run it would be great if we can do that. But in
the short run, I think what we need probably is to be clearer in terms of the way reports
are written and probably even more standardized because, you know, as the consumer of various
reports — like, no two is the same. And you never know quite where to go for a while until
you get oriented to the style of that report. And maybe we need to copy a little bit more
from the radiology clinical correlation as recommended, or some words to those effect.
A variant of unknown significant somehow is translated by the receiving clinician, if
they ever even read that, as technical jargon, even though it seems like common English that
they just figure someone else will worry about. And so, I think a lot of this can be alleviated
by being a bit blunter and clearer in how the reports are worded. Teri Manolio:
Howard? And then we’ll move on. Howard Jacob:
I just came up for the title of the paper, “Embrace the ambiguity.” Teri Manolio:
Yeah, I asked — Howard Jacob:
No, I’m sorry, at some level, this genetic determinism that, you know, the specificity
— the functionality in all this that we have to jump over this extra bar. But I can’t
think of a paper, Bruce, that talks about genomics and using this clinically that has
articulated what you and Les have both said with great clarity over the last day and a
half. And I think that needs to be captured somehow so that you’re — so why your — why
you say physicians are comfortable with that. I think when they’re facing a patient at
that moment in time, obviously. But when they start thinking about genomics, somehow it’s
— whether it’s the exceptionalism or what, but it gets — it gets lost. And I think — I
think what’s obvious to you, and to Les, and to Gail is not obvious to the people that
aren’t practicing medicine with those. Male Speaker:
I’m glad to know I presented ambiguity clearly. [laughter] Teri Manolio:
I — well, I wonder, you know, falling on Mike’s suggestion for a subsequent genomic
medicine meeting, you know, do you think that the various big labs would be willing to get
in a room with us and talk about what standards there might be? I mean, obviously, they’re
in competition with each other. But it’s also to their benefit to have things interpreted
correctly. So is that something that you think is viable or is that, again, a silly idea? Female Speaker:
I was just going to say that the [unintelligible] group actually convened six different labs,
like, last fall to do exactly that, just to have that first discussion about those things.
And everybody was really positive about it and agreed about, you know, sharing data.
I think the challenge that they had, though, was — you know, we hadn’t yet defined some
of the data structures for the phenopackets yet, was what clinical data they were willing
to exchange at the patient level is just really challenging. Teri Manolio:
Well, and not even that, I mean, I think the issue that was raised here was just they report
them in different ways and you have to learn how to read, you know, each report differently.
And so, could we agree on something like that? Dan? Daniel MacArthur:
So I think the labs have — I mean, as you say, are very willing to get in a room and
discuss these things. Amahadi Rem [spelled phonetically], for instance, convened a number
of meetings and work with them within the SMG context as well as part of the — coming
out with those variant interpretation guidelines. And they’re certainly — they’ve also
been actively participating and in depositing [unintelligible], you know, with varying success.
It seems to me engaging them is so important because so much of the clinical tests — so
much of the active interface between what we’ve been talking about in the last two
days and patient care will come through those clinical labs that engage labs are absolutely
critical. And it’s also — there hasn’t been as
much discussion of this over the last few days but there’s — there was some mention
just then about the, you know, people’s ways of dealing with the VUSs — my biggest
concern is not the way that clinicians deal with the VUSs. It’s the fact that at the
moment, I think, that the clinical labs are still systematically over-reporting pathogenic
variance. And I’ve seen so many reports where it’s, you know, there’s a variance
being reported back as pathogenic or likely pathogenic that is just clearly too frequent
in [unintelligible] in other populations to be pathogenic. There’s a lot of work that
needs to be done to push — to push against that. And part of the reason I think this
is occurring is there’s a strong commercial incentive for clinical labs to have a high
diagnosis rate. They’re judged based on their diagnosis rate. And so, it was always
be tempting to relax those criteria a little bit more clearly. So again, we can’t set those criteria here
but I think we can help to push things in the right direction and make sure that the
clinical labs are very aware that people and watching and that people are paying attention
to the reports that are going out there. And that there are standards that can be assessed.
And there’s evidence that should be used. And you shouldn’t be ignoring, for instance,
large variant databases when interpreting variants that will result in incorrect assertions. Teri Manolio:
Yeah, that was kind of reflected in Bob’s presentation to, that exact point. So Howard,
and then Mike, and Bruce. Male Speaker:
[inaudible] Teri Manolio:
Sure. Male Speaker:
— which is, aside from the commercial incentive to have high diagnosis rates, I wonder too
if there’s a concern about liability if they fail to mention something that someday
is found to be pathogenic. So there may be a bit of both that drives this. Teri Manolio:
Howard then Mike. Howard Jacob:
So I think that — I think getting the labs together is a good thing. I think there’s
some good strategy around that. We’ve been doing this with Baylor which has been very
informative. But I’ll tell you that the problem set is not so much around that. And
I think there — and I — and Daniel, I agree with what you’re saying about this. But
there are errors being made in medicine. And I’m not saying that we shouldn’t try to
minimize it. But the average physician makes 62 missed diagnosis a year, according to the
OIM. So the challenge is that how do we balance that piece off? And so, what I’m getting
at is I think the labs can all get together. I think we can have some discussions. I think
we can look at reports. I think we can figure out how to exchange data. But that doesn’t
get to the point of somebody using it in a clinical setting. The use in the clinical setting to me is the
hardest part is about, how do we get this in a — in a utility? I think that what we’re
talking about in terms of getting the labs together is the easy part. Because I think
we can look at data. We can make exchanges. I’m confident that your group, and our group,
and the others that are doing that, we can come to some standards around that. But it
doesn’t get rid of this ambiguity of how do you deploy it at the — at the patient.
And I don’t know how to convey that. But to me, that’s what I’m trying to figure
out, is how do we capture that, and less about just decreasing error. Not to say we shouldn’t
decrease error, but I don’t see that as the biggest problem, in my opinion. Daniel MacArthur:
So then we’re probably in agreement. I think the — everyone understands mistakes are made.
And also, variant interpretation is just hard even if you have lots of information at your
fingertips. There are many, many cases that sit on the edge, you know, [unintelligible]
once or twice in [unintelligible] and you just have no idea what to make of — make
of that variant. And I have no problem with clinical labs making a call one way or the
other when the evidence — when the evidence is actually ambiguous. I guess what I do have
a problem with is clinical labs that continue to ignore evidence basis that exist out there
and, you know, have — continue to do so despite a year and a half of that evidence base existing
out there, continuing to give bad diagnosis that are flagrantly wrong when all you have
to do is look at public data to see that. Teri Manolio:
Yeah, Cal? Calum MacRae:
I was just going to say I think — Teri Manolio:
Then Mike. Calum MacRae:
— some of this — oh, sorry, Mike. You were ahead of me, weren’t you? Mike, why don’t
you go? Female Speaker:
Yeah, okay. Male Speaker:
I’ll take it. So I’m really worried about your manuscript, Howard, of Embrace the Ambiguity,
because I think that’s the top of the list served for insurance payers and it puts us
even further back in relation to some of the conversations we had. I think — and it would
be going outside of genomics a bit, but I think that one of the future meetings should
have people in the room that can describe the parallels between the ambiguities elsewhere
in medicine and in genomics. Because, you know, again, it comes back to we are like
other areas in medicine. We are just more introspective, I think. So that was — Male Speaker:
That’s embracing the ambiguity. Male Speaker:
Okay. [laughter] Male Speaker:
So that was actually going to be my point, is I think the problem is not that we’re
ambiguous about the VUSs, it’s that we’re not ambiguous about the pathogenic variants.
The fundamental problem is that for years we’ve made it seem like you don’t need
to know any genetics, you don’t need to look at anybody else, no need to understand
the phenotype or the genotype correlation. All you need to do is look at the test result.
And so, everybody thinks they’ve got — every physician thinks they’ve got it covered:
it’s either pathogenic or it’s of uncertain significance. And in fact, you know, my biggest problem
is seeing people who have what look like they should be pathogenic variants and they have
different diseases, or they have diseases that segregate from the other side of the
family. I mean, that’s what we’re actually seeing and I think that’s a good thing.
I mean, I think it tells us that it’s more complicated than it initially appeared. But
I think the way that the field is responding is by trying to either back out of this or
go full-throttle forward with the wrong metrics. I think what we need to do is to engage, as
Mike said, the medical community. And there are hundreds of tests where the — maybe not
hundreds. There are several tests where the market is $200 million or more in the U.S.
in areas for where there’s zero data that there’s any benefit. And that — those tests
got into the market by simply showing one narrow area of benefit and then suddenly it
has expanded. So you can see why the insurance companies are wary of this. But by the same
token, these tests got into the market because they showed some fundamental delta and outcome. And that’s why I think, to some extent,
we’re all saying the same thing from slightly different angles. We need to be more involved
in clinical care. We need to communicate more precisely the ambiguities or the quantitative
precisions around our statements. But we’re — I don’t think we’re going to get there
by either making pathogenics more definitive or VUSs more ambiguous. I think we need to
make the process very clearly something that has to involve the whole medical community. Teri Manolio:
I think that was a great summary. I hope we caught that on the tape. So in terms of other
bridging activities on this slide, I think we’ve addressed this one of — increased
awareness of resources and standards that do exist. The idea of needing, you know, expanding,
like, the matchmaker concept to other domains was raised in the last panel. And one of the
thoughts we had was — I don’t know how many of you are familiar with InnoCentive
— the InnoCentive challenges. So it’s a website where people can post, “I need somebody
to write me a code that will go from genotype to phenotype,” right. And you put out a
challenge, and then there are solvers — there are people who want to, sort of, engage in
that. And it’s a little more detailed than Matchmaker which is based on, “I got this
— I’m interested in this gene or this variant.” It’s — imagine that you are a clinician
and you’ve encountered this variant and you want somebody to build you a mouse model
or you want to know if there’s anybody who wants to work collaboratively. It’s kind
of a neat site if you haven’t seen it. That’s another sort of model we could think about
for bridging these gaps. Last — oh, yeah, Howard. Howard Jacob:
I wanted to mention to the NHGRI staff in particular that this would be a good opportunity
to reach out NIGMS and to NSF and other worlds that contain the other end of that bridge.
It was, you know, I think it was with amusement that the information came out about a certain
amorous website containing lots of men and fake profiles for women. And when that website
went live and people started losing their jobs over it, that information came out — and
I just don’t want us to build a site that is full of genomics people and has no basic
scientists and — on the other end to match. [laughter] And I’m afraid that we are heading toward
that sort of thing and need to be very thoughtful about it. Teri Manolio:
That’s something I can’t get out of my mind now so… Howard Jacob:
Take a left at Ashley and Madison. [laughter] Teri Manolio:
Melissa. Melissa Haendel:
This is much less of an exciting comment. So I just wanted to say that the NSF phenotype
research coordination network, which is a five-year NSF-funded program, just ended.
And the program — the PI on that project has actually now moved to NSF as a program
officer or division director so — and is extremely keen on developing these phenotype
exchange standards for across all of biology. So I think that’s a great timing and opportunity
in particular. Teri Manolio:
Last slide. Well, actually we have, like, 20 more slides but I don’t think we’re
going to make it through them all. [laughs]. I — are we going to make these slides to
everybody afterward? Because you know, what you guys are giving us feedback now is terrific.
But I’m sure that upon reflection there’ll be additional nuances, or subtleties, or not-so-subtle
comments that you’ll have no these. And they’ll be available. And it’s really
helpful because it’s helping us, sort of, frame how we’re going to write up this gathering
as a publication. So the last slide we had for the, sort of,
overarching topics or overarching themes to emerge from the past day and a half or so
of discussion — so it has to do with enhanced interactions, fostering those interactions.
Some of this we’ve already covered. I will start with the last one. We — one of the
things that I think is going to be really helpful for bridging the gap is, sort of,
having kind of what Gail was talking about earlier, is having people come to, like, ACMG,
and AMP, and all those things and having people go to the different meetings. Actually, in
the past we have tried this. So we have had a hard time getting workshops on some of these
basic resources, for example, at ASHG. They just — they just don’t get through that
review committee. Somehow it is being felt that they’re not relevant. So having somebody
in those organizations to help open the doors is really useful. So Gail has done that for
ACMG for getting workshops, you know, [unintelligible] of databases there. So I think this is where we have to sort of
help each other. And those of us who helped organize and run conferences in basic sciences
need to invite clinical people to attend those as speakers and participants. And I think
that could help foster interactions more. So this whole last slide here is about enhanced
interactions with some bullet points that we heard from the discussions. Are there any
other insights or input on these general topics based on what you’ve heard or discussed
over the past day and a half? Female Speaker:
Yeah, I don’t think I did have any particular additional insight into that. I mean, I just
— you know, I’m aware of efforts, you know — I think that many people in this room also
know, you know, at Baylor there’s a large fly screening or disease. And you know, there’s
a paper [unintelligible] a lot of zebrafish, you know, looking for disease. I think there’s
also a center [unintelligible]. So I am aware of centers that maybe they’re actually all
part of the undiagnosed disease network that was raised here. I actually don’t know.
But I do think that it would be fun to identify meetings that are held on the model organism
disease screening and invite clinicians or vice versa if there is a clinical genetic
meeting to which there could be invitations for model organism disease phenotyping and
screening investigators. But I guess I don’t know enough specifics to make specific suggestions. Female Speaker:
I appreciate that. And maybe I could just stimulate my colleagues at NHGRI, particularly
in our division of basic sciences, particularly those who are working with, say, the ENCODE
Consortium. You know, is there an opportunity or some value in having clinicians come into
your meetings and having your folks come to clinical meetings? I mean, you know, have
you — have you given — I’m sure you have given some thought to that and how we might
facilitate that. Teri Manolio:
Yeah, Mike. Oh, and then Elise [spelled phonetically]. Female Speaker:
That’s okay. You can start. Male Speaker:
So from the Division of Genome Sciences at NHGRI speaking about ENCODE, we’re having
our second annual users meeting. We had one last year. It’s open to anybody. We invite
everybody. You know, so clinicians are welcome just like anybody else. We do outreach events
at things like ASHG and at Biology of Genomes. Last year we had one outreach event; I think
we did this with the Common Fund Roadmap Epigenomics. And this year we have two scheduled at ASHG
and Vancouver. And those are — you know how ASHG works: you have to get into the meeting
first. That’s very difficult for NIH personnel. And then once you’re at the meeting, you
have to apply to go to the workshop. Those sell out very rapidly. But there will be two
of those if people are interested. Teri Manolio:
Elise? Female Speaker:
Yeah, two comments: one is ENCODE primarily focuses on noncoding regions and much of the
past two days have focused on coding variants. So I mean, while we’re definitely interested
in that, it’s not the major focus. I also want to bring attention to — there are some
conferences that do try to bridge genomics, genetics, and human disease. There is a meeting
— I don’t have anything to do with this but the Allied Genetics 2016 conference in
Orlando in July have sessions on — with a number of different [unintelligible] organisms.
And some of those sessions are — specifically have some topics related to disease. So that
may be of interest to people as well. Female Speaker:
Dan? Oh, Gail? Gail Herman:
The other thing would be with a, you know, travel more expensive and it’s hard to go
places, is to do webinars. And that’s worked really well where we can get CME grade. I’m
not sure how easy — I’ll bet we can do it. Jean [spelled phonetically] is good at
stuff. But to get CME, we got it for the mouse one is — to develop a webinar we do that
case conferences which are great. It be nice if the places that did that brought in model
organisms. And I think they do where they can. But then, if there’s something either
using databases or other model organisms that you can relate to variants or human disease,
doing a webinar is I think a really good way to go because you can watch it later if you
can’t do it at that time. Teri Manolio:
Mike and then Cal. Male Speaker:
A lot of the epigenomics projects, including ENCODE Roadmap Epigenomics, do post materials
from outreach events, video, or slides, whenever we had available. It’s beyond my expertise
to know anything about what it would take to convert that to something that you could
get — you call it CME credit or who would do that. But you know, I mean, we are — we
do try and offer these materials. And we get that there’s only so many people that can
travel and get into a particular meeting, whereas by offering it as web-based it’s
more probably accessible. Teri Manolio:
Cal? Calum MacRae:
I was just going to suggest, sort of, nontraditional venues as well. You know, there are industrial
conferences where huge investments are made in healthcare delivery and care redesigned
that might be impacted by what we’re talking about here. Similarly, biomedical engineering
conferences where they’re — folks are very much more thinking about how to move stuff
into the clinic at, you know, mechanical or biophysical parameters into the clinic at
scale. Those types of things I think would be conferences that would benefit from hearing
what our needs are and what things we can bring to the table as a community. Teri Manolio:
And I might just note that probably your average clinical geneticist would not see a data users
conference as being something that, you know, really sort of relevant to what they do. On
the other hand, they may have some useful insights to provide in terms of the kind of
clinical problems they’re struggling with. When they come across, you know, a VUS and
who knows what it means now — granted, those are encoding regions because they’re in
genes. But there also, you know, others that are coming up that are not in genes. And maybe,
you know, not necessarily a user’s conference, but even at an ENCODE [unintelligible] committee
where you’re, you know, looking for priorities or whatever to hear from those groups. And
also have your investigators, you know, come to some of the meetings that we have might
be useful. I don’t know. Yes, Emily. Emily Hillman:
Hi, I’m Emily Hillman. The National Society of Genetic Counselors also has created and
is actively — is committed to continuing to create some of these types of resources
for practicing clinicians. So very much on the clinical geneticist level, not — I don’t
think we’re talking about [unintelligible], how the discussion is going to day. But there’s
an online course that will be available for purchase for any clinician that has continuing
education credit for genetic counselors. That’ll be coming up very soon, specifically around
these issues, around variant interpretation, what are the different databases that are
available, how do you use them. And the society is going to continue to work on resources
like that. So I think while that right now is very much geared towards clinical issues,
directly for, “How do I solve this problem for patient in front of me,” I think there
may be opportunities within NSGC to think more about. Genetic counselors certainly are
interested in thinking more about, “How do we feed this back into the lab and feed
this back into research?” Teri Manolio:
Yes, Liz. Liz Worthey:
We could work together to make a course on all of the things we’ve talked about in
the last two days and put it up on Coursera for free, let people use it. Teri Manolio:
Great idea. Mike? Male Speaker:
Yeah, I — we looked into, I think it was, Coursera for our materials and the impression
we got, perhaps erroneously, is that there’s a pretty high bar to getting something into
Coursera. You have to have something ready as an actual college-level course with test
materials and so forth. And you know, it gets the issue of who’s funded to develop this
and who has the time to do it. But it would be a fantastic idea to offer something of
that depth. Teri Manolio:
Yes, Eric. Eric Green:
I don’t know if this is the right time to jump in or not but I’ll just do it since
there was a lull. So I think some of these ideas are terrific; they seem all potentially
having a very valuable incremental role to, sort of, help address some of the issues we’ve
talked about over the last two. Just — everybody just try to be a little more audacious for
a couple minutes and think about, you know, if there is going to be a — some sort of
a larger research initiative in particular or someway to stimulate this on a larger scale
— obviously, this would require resources; I can’t make any promises. But it would
awfully nice to have some audacious ideas incubating, being thought about every once
in a while, so if there is an opportunity we would be poised to have something in front
of us that has been discussed a little bit. And if people have any immediate ideas that’d
be great to sort of thresh them around a little. But also, I want to stimulate people to think
about this as you’re traveling back or over the next few weeks. Because I think the problem,
from what I can glean from hearing the discussion or the problems from the last couple days
is not going to go away easily and it’s not going to take this series of small incremental
fixes to totally solve. It’ll probably partially solve. I just tend to think there might be
some unique opportunities to put a consortium together or some way to structure grants to
stimulate the kind of discussions we were stimulating here. So I don’t know if anybody
has any immediate ideas. I’d love to hear them but I also want to get people to not
go back from this meeting thinking, “Well, audaciousness is dead because there’s no
room for it budgetarily [sic].” I’d rather here the ideas than not hear them. Female Speaker:
Well, I’ll just jump in. So I don’t think this is actually overly audacious but I think
that these are really big problems. And you know, the idea of getting clinicians to cooperate
and, you know, put in valuable information that we can all use. So the real idea is to
go beyond, kind of, who’s in the room and who we typically know to go to industries
that are really interested in this. You know, you talked about precision medicine. I was
out at Accenture, you know, and they just all want to know how to get involved. And
I think they do have tools that can scale, as we like to say, and I think that it also
— I know that there’s a payers conference coming up and I got myself invited to it.
But I think it probably isn’t enough just to have payers and genomicists in the same
room. So kind of think about, what does this ecosystem take? Like, who are the broad set
of players? Because I actually don’t think we can solve it on our own. Eric Green:
Just to clarify: so you mean to get them in the room for the discussion — Female Speaker:
In the room. Eric Green:
— or get them as part of research networks, or get them to help fund some of this, or… Female Speaker:
Well, I don’t think it necessarily is going to come all under the rubric of research.
But certainly, NIH and NHGRI could have a really important convening opportunity. But
I could even imagine a consulting firm like McKinsey being tremendously interested in
just, you know, getting into this conversation. So something big like that. Eric Green:
Public-private partnership kind of endeavor is what I’m sort of hearing. Female Speaker:
Yeah, yeah. Teri Manolio:
So I think — I think the challenge here is that we don’t know where the next big innovation
is going to come from, right. So — but if we put — if we solve our data sharing issues
that we’ve talked about several times during this meeting and all this event meetings talking
about this, sharing the genotype and phenotype data in a way that is open enough — protecting
patient privacy but open enough that anybody with an idea can jump in and start to develop
it. So that we don’t have to think, well, maybe it’s this person that we need to bring
in or that person. If we make the data available in a way that allows anybody to come in with
a good idea, to me, that’s where our — that’s where the innovations are going to come from.
And if we keep these as silent resources or closed consortium, I think it’s going to
hamper our ability to make big advances. So Monty [spelled phonetically] talked about
UDN and the success that that small group had when they were able to share data within
that consortium. But that’s not open to everybody. So I think the big audacious goal
is to make — really go about making the infrastructure — to make the data as available as possible.
Dan? Daniel MacArthur:
I’d love to see — we talked quite a lot about what Doug is doing and I think support
for really doing that properly, taking the hundred most clinically impactful genes to
— defined by some external group — defining that very clearly, this is the gene list that
we need to go after, that will have the biggest impact. And then, funding support for systematically
developing [unintelligible] functional essays for each of those genes, assessing the impact
of every possible variant within those genes, and making that data available in a — in
a resource that is completely available to anyone who wants to look it up would be transformative
for a bunch of clinical applications. And the key thing here is doing that systematically.
It’s very clear that a lot of this work is being done but [unintelligible] in others
and will happen in many different ways. But the idea of building that in such a way that
the people think about exactly what they need to do to validate those essays, engagement
with the clinical community to make sure that there’s clear contact with the way that
the data was produced and the types of information is actually needed for clinical interpretation,
and then making sure that — there’s that unified resource where the data is displayed
in a way that everyone can access would be phenomenal. We would love it. Male Speaker:
That captures what I was thinking. And I think the key pieces are the, sort of — sort of,
beforehand crafting what we — what we all believe are going to need to be the, sort
of, bars for validation and quality so that the data is actually, you know, useful to
the clinical community. Because I can continue to produce one-off data sets and others in
other labs will do that, too. But unless there’s, sort of, centralization of goals and standards
and then dissemination, it may not have as much impact as it could. Female Speaker:
I — just, you know, taking away from some of the conversations that — this isn’t
an innovative idea. But in terms of the challenge, it may be considered to have some audacity.
But I understand that it’s really the numbers that are limiting and the populations that
we actually have sequence for. And so, I mean, if I’m taking, I mean, what Cricket said
for the first day, more of a free-for-all to really find a way. Like, can we get many
more people and many more populations sequenced? And of course, that also goes with having
a good database like with Exec [spelled phonetically] and so forth. But I mean, that to me is probably
less incremental just to get the numbers that we need for the statistical power. Daniel MacArthur:
Yes, I mean, I think that there is — in terms of just sheerly building up numbers in particular
populations, that will happen to some extent actually through obviously the common disease
consortium, so long as there is clear work that’s done in advance so that to make sure
consents are consistent with data sharing and so on, which I hope will be done. But
I think you’re absolutely right that there are some populations right now where there
is just not a clear path at all towards getting large-scale collection of those samples in
the system. And in particular, I’m thinking of Middle Eastern samples which are just dramatically
underrepresented in our — in our reference populations compared to how often we see them
in the clinic. And there are many other samples that I think we could — we could really do
a much better job of systematically including in large-scale sequencing consortia. And it
may just be a matter of helping to guide — for some of the common disease consortia, guide
them towards favoring particular resources that include populations that are currently
underrepresented in our — in our databases. That would help a lot. Teri Manolio:
So Peter and then Cecilia. Peter Robinson:
One of the biggest areas of clinical need is what about the other 75 percent? I mean,
if you look at clinical exomes and genomes that — the yield is about 25 percent in big
studies. And I think a lot of what we’ve discussed today is an incremental improvement
on the paradigm of Mendelian coding disease. It’s completely necessary and wonderful
but I think we should also think about what other models might there be. One lesson that
you can learn from Mendelian disease is that probably a genetic burden. So merging into
oligogenic inheritance might be important. I think that’s one very good hypothesis
for the other — part of the other 75 percent. Another thing is that nobody’s really looking
that hard at noncoding variation. And we’ve started to look at that but by detailed biocuration
we’ve found 450, I think, validated things in UTRs, promoters, et cetera. It’s really
— there’s really little to — it’s very difficult to validate these mutations. And
I think it’s an open question: are they more common than we think or are they as rare
as we think? And are very, very preliminary results on a small number of genomes with
an enormous confidence interval is that we could probably get you another 5 percent with
stuff that we know now. But I think those are two areas that would be more than an incremental
improvement. Teri Manolio:
Cecilia? Cecilia Lo:
Yeah, I would second that. I mean, I think noncoding definitely will have important roles
to play but will be challenging. But with whole-genome sequencing data accumulating
I think that will come. But I also would like to support finding innovative ways to pursue
complex genetics. Because I think there’s a lot of the unsolved ones, perhaps, are due
to more complex disease. And I think we — and I know it’s very challenging but I think
we need to find ways to really deal with that question, how to model it, how to interrogate
it. So to me, that would be transformative because I think there’s a lot of human disease
that’s really more in that category than simple Mendelian genetics. I’m preaching
to the choir; I know — I know everybody knows that. And the other thing I wanted to just comment
is in terms of modeling, you know, the — I don’t know, the minimal set of genes that
are of high value and interest. Cell-based assays are really important and they are definitely
very informative. But I think depending on the gene and the phenotype, there is a place
for animal modeling. And so, I would also suggest that a subset of those genes should
be systematically looked at in animal models and with crisper technology. It’s very possible
that you could actually interrogate every base if you wish to, not that it’s necessarily
going to be informative. But there is a way to that. And so, I’d like to propose that
animal modeling should be part of the toolkit, again, depending on what the gene and the
phenotypes are that one is after. Teri Manolio:
I think everyone’s running out of steam but — Female Speaker:
Oh, I still have something to say. Teri Manolio:
Okay, there’s one and then two. Female Speaker:
I was curious if there is interest in this community for more efforts at humanizing laboratory
animals like the mouse. I mean, there’s the certain, you know, system — [unintelligible]
system. But I mean, that I think — I’m wondering if people think that that’s another
worthwhile endeavor in this community to do better or more — potentially more human-relevant
modeling of variants. Teri Manolio:
Calum and then Melissa. Calum MacRae:
I was just going to say I think — with all due respect, I think the best place to model
humans is in humans. I think that’s the fundamental — that’s essentially what we’re
hearing, is there isn’t enough phenotopic spectrum in an average clinical evaluation
to get you past a single-gene panel, far less a genome. So if we’re not actually doing
stuff in people, we may as well not get started. That doesn’t mean — I mean, I work in model
organisms. I think it’s very important that — a lot of the complexity can only be worked
in model organisms. There are just not enough — there are not enough people on the planet
to do some of these things. I really think, you know, we have to be quite careful about
what we try and bite off. Actually, to Cecilia’s point, there is — one
of the most interesting things, and it may even be an audacious project, would be to
actually understand the generic architecture of more than two diseases. Because at the
moment, we don’t really know the genetic architecture of most diseases. And those that
are — where it has been worked out, like, for example, Hirschprung’s Disease or then
to Chakravarti’s work where a decade before he started, people thought it was an environmentally-driven
condition. And now it’s essentially very clear that it’s a result of an interaction
of only three genes. So can you imagine how few genes might be involved if you can already
detect a heritability signal. So I think there’s a lot of space that we
haven’t explored and part of the reason is the assays are not available or the conditioning
variables are completely unmeasured. And you know, at the moment, we measure four conditioning
variables: tobacco, alcohol, weight, and a couple of other sort of rather toxicologies. Teri Manolio:
Melissa, then Rex. Melissa Haendel:
So two points: one is in response to that last comment. I think we’ve, you know, done
some work in collaboration with the comparative Toxicogenomics database and with NIHS just
to define some of the data structure needs around the exposome. And there is some work
going on in that area that’s really fundamental to a lot of, you know, the — at the interaction
level. So that’s — so that’s one thing that I think is really deeply lacking. The second thing is just, you know, can we
better choose the models in which to, you know, do these types of experiments? Because
even with, you know — even with new, you know, genomic-editing technologies, it still
costs a lot of money and time to do the analysis. And you know, those collaborations that we
want to try to match-make between, you know, clinicians and basic model organisms, we need
to pick the right model organisms. And we have lots of data to help us determine which
model organisms but we aren’t really effectively using it to make those decisions. Teri Manolio:
Rex? Male Speaker:
I just wanted to amplify Calum’s statement. In the preparation for the panel that we had
from 1:00 to 2:00, one of the topics that came up that we didn’t really get to in
our actual panel discussion was the role of modifier genes. Because we’re not going
to figure any of this out until we start really get our hands around modifier genes. So I
think in terms of thinking about audacious goals, maybe something to help us think about
how to better — do a better job of thinking about modifier genes. At the meeting that
actually Melissa, and Calum, and I were at last fall where they talked about the phenotypes,
and Peter and some others in the room, you know, one of the bioinformatics folks there
actually made the point that there might not even be enough compute power in the world
to start to do all the kinds of para-wise — or combinatorial computation that would
need to be done in order to really handle thinking about contributions three or four
variants in even the same pathway, much less three or four variants in different pathways,
all of which I think are likely to be important contributors to human disease. So you know,
that’s a pretty audacious goal to start to think about how do we tackle that problem. Male Speaker:
Just to segway from Rex’s comment, I mean, one of the things that I think we mentioned
a couple of times during the discussion is the use of drugs as both a conditioning variable.
But also, there — you know, drug trials are amongst the most rigorously phenotyped cohorts
that actually exist because they have been very intensively monitored by external CROs.
And so, that does three things: it a) gets you the exposure; b) gets you little hanging
fruit in terms of time series; and c) it gets you into the clinic. So finding a way to have
genomics be part of the next step in introducing new therapies or new therapeutic trials I
think is perhaps something that is more politically audacious than it is financially audacious.
But it may be a good first step in some areas. Teri Manolio:
Cecilia. Cecilia Lo:
And one other thought is that, again, I don’t know if this is audacious, but the idea is
if we are to get basic scientists and clinicians working together, I think ultimately you go
where the money is. And so, is there an application process at NIH where that is a requirement?
You know, if you want to be able to apply, you have to put together a program where the
basic scientists very clearly integrate it with a clinical enterprise. I think that that
would really go a long ways to really furthering the same. Male Speaker:
So we’re looking into that. I mean, by the — on the [unintelligible] we’re familiar
with this at other institutes or people you know at your institution who are involved
in grants like that? Because that was one of the things we were talking about internally.
We just want to — there’s 26 other institutes that may have tried this. Teri Manolio:
Mike? Male Speaker:
I mean, some institutes like NIAD at least used to have a program years ago like that
where there are program projects that would require clinical components and basic Science
components together, or translational components and basic Science components. And I don’t
remember what the specific opportunities they have are today but they have these kinds of
opportunities. Teri Manolio:
Yeah, and Deborah Leonard [spelled phonetically] who I’ll channel because she had to leave,
did lean over and mention to me that MCI also requires this in some of their comprehensive
cancer research centers. So there are some models for it. I was reluctant to suggest
it because you worry about, you know, the government telling you what kinds of, you
know, groups to put together. On the other hand, if we can incentivize it in a flexible
enough way that you can then model or fashion that to meet the ends of the Science that
you want to pursue, then that’s probably a very useful thing. So thoughts on that,
you know, as you’re heading back or whatever would be useful. Female Speaker:
Actually, the Department of Defense is now funding a lot of biomedical research and this
is what they do particularly well. This is something that they encourage and, in fact,
you know, there’s — a lot of the programs really is — they’re packaged to really
encourage the integration of basic Science with clinical translational efforts. And so,
I think that this might be a model to look at, as well. And in a way it’s different
from NIH PO1s [spelled phonetically] where you are required to have a clinical component
because in a PO1, each project is separate. So you have basic Science project, and you
have a clinical project, and so forth. In the — in the realm of the DoD, they have
PO1s like that but there are very, very few. Mostly, they’re just equivalent to RO1s.
And so, you have to put together an RO1-like application where you integrate basic Science
with clinical, you know, research. Sort of — it’s a — it’s a nice way where you
really are forced to dovetail and really make the clinical research and the basic research
come together. And so, I think that that model seems to have worked well for the DoD. And
I don’t know whether there are constraints to whether NIH may or may not be able to implement
that. Male Speaker:
Constraints usually haven’t held us back at our institute but we’ll work on that. [laughter] Female Speaker:
Teri? Teri Manolio:
Yeah, so at this point, you know, people are getting kind of tired. I appreciate everyone
staying to the bitter end. This has been a very intense two days and it’s really been
extraordinarily useful to use. We hope it’s been useful to you. And one of the things
that was mentioned on here was, you know, the opportunity for informal conversations
in various places and hopefully some of those have happened. It’s a little known fact
that we actually incentivize national airports to have flight delays so that you guys all
talk to each other. [laughter] And as our budget gets better, the flight
delays get worse. So at any rate, many thanks and I’ll just turn to Carol for the last
word. Carol Bult:
And just adding my thanks. You know, you never know when the organizing committee — so Howard,
and Teri, and I, and the genome medicine working group would get together and talk about this
agenda and we were throwing out names. And you know, a lot of times, there were names
like, “Well, we don’t know this person really.” So you’re pulling together in
a room a group of people, many of whom you already know each other. But for most of you,
you might be setting next to somebody you’ve never met before. And the dynamics of that
are always an unknown. But you all have really just — the conversations have been really
great, the contributions. And we really, really appreciate your willingness to come into this
group, and open your minds, and state your opinions in such a constructive and positive
way. So thank you all and safe travels. Oh, well, again, Rita, Taji [spelled phonetically],
Ellie [spelled phonetically], the video crew, everybody for putting this all together. It
would not have been possible without you guys for sure. So thank you. [applause] [end of transcript]

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