Arul Chinnaiyan: Great, I think we can open it up for group
discussion and then maybe just to kick things off, I actually had a question for Robert,
actually based on his and we had a little sidebar on this in terms of control groups
in a randomized study and so forth.
He presented some nice examples of how CSER has been carrying this out in general, about
four different sites and so forth, but I wanted to hear your thoughts about how to think about
this in terms of cancer sequencing in terms of including control arms and in some cases,
considering some of the ethical issues of potentially not returning a potential actionable
results in those patient groups.
Robert Green: Well, I think I’ll dodge that question because
I’m not a cancer expert and I think part of the excitement of a CSER 2 would be to
try to create some creative solutions to that sort of thing.
But I will that I think that in cancer and other conditions, you have a lot of flexibility
about the way you design clinical trials.
You can design them for time lag, so, for example, you can design an intervention in
a control group instead of not having that intervention.
You can have it later on in time.
You can design different dosages of a particular intervention.
So, for example, instead of a -- I don’t know, a drug that’s tailored to a particular
cancer, you can give one that’s a little bit tailored and one that’s even more tailored.
So, there’s a lot of creativity in the way that you can, I think accrue health evidence.
That would be part of the challenge to investigators, in my mind, in a CSER [unintelligible].
Arul Chinnaiyan: Levi?
Levi Garraway: So, I wanted to ask about a topic that Robert
touched on in his talk and also, Gail touched on in her talk, but we haven’t actually
expressly discussed it.
And this is the issue of -- Robert, you were pointing out that one of the way to approach
the re-phenotyping in the setting of, for example, various and certain significance
is somehow one could go back and do either [unintelligible] phenotyping or even sometimes
bio-chemical based phenotyping, et cetera and Gail, in her talk, brought this idea that
there groups around now that are doing assays, you know, functional assays for variants.
And so this is something that raises a question that one could imagine, you know emphasizing
the E in CSER in 2.0, which is that there are context where there are variants upon
certain significance.
But they’re plausible and, you know, the context is right, but you don’t actually
know if they’re functional.
But there might be instances where one could, with the right collaboration, the right team,
do kind of a very focused functional assessment either of the variants itself, or if there
are cells or tissues from the individual where one could do an additional kind of deeper
functional screen.
So, one -- it’s sort of an appealing novel and innovative direction that one could go.
I guess maybe this is a question for NHGRI, do you guys have the appetite for that kind
of approach?
There’s certainly a lot of interest and there’s a big space out there where one
could explore the idea of more phenotyping, more functionally based phenotyping for variants
that are crying out to look like actionable.
We just don’t have the space and we may never -- and if we just wait for kind of the
clinical experience to grow, it may be a long lag before we learn about them.
Arul Chinnaiyan: Go ahead.
Debra Leonard: So, I think it’s really -- that’s a really
important idea.
We’ve taken the approach at UVM that if we do research on the clinical variants that
we’re seeing that we don’t know what they do, we’re much less -- much more likely
to feed that clinically useful information with the functional genomics research that’s
being done there.
And so, I don’t k now if there are ways to link the variants that we don’t understand
through the CSER project to functional genomics, parts of NIH more broadly even, which also
could get us to guidelines that actual clinicians believe in because they’ll be the variants
that they’re seeing in their patients and how to use them.
Carolyn Hutter: Some think -- I mean, some of the CSER sites
do this in a very limited way and obviously, there’s a lot of interest.
My understanding is the UDM program also has a significant functional component as well,
but it’s something that certainly would be of relevance to CSER going forward especially
in the context of the workflows you already have.
Something to explore.
Carolyn Hutter: Yeah, and Tim more broadly answered Levi’s
question.
You know, it is something I think we are definitely discussing and it’s going to be part of
the functional assay.
[unintelligible] You know, it’s going to be actually -- we already discussed as a topic
for the next GM meeting, is how to make connections with functional and functional work and I
think that’s -- you know, I talked before about the virtuous cycle that came out of
the discussion was the sequencing, but I think there’s another way you can think about
the basic research of that virtual cycle to also think about things very much at the functional
interface.
Arul Chinnaiyan: Dan?
Dan Roden: Well, that’s -- I’m a huge fan of this
because in the iron channel [spelled phonetically] world it’s, you know, the assays are easier
than most, if not many, but there’s also this sort of limitation in the sense that
you do have people who carry variants that are clearly functional at the in vitro level
and have no phenotype.
And there are also variants that we think clearly confirm phenotype, but the assays
that we have, unless you sort of start to really drill down and make mice or whatever,
you really miss the phenotype.
So -- but I’m 99.9 percent in favor of the idea that -- a hundred percent of the idea
that adding -- no, no, adding that phenotypic information is -- ought to be a helpful way
of going forward in terms of interpreting data.
Just with the caveat that it’s not perfect, but it would be a huge step forward.
I think the iron channel should be done first.
That’s --
[laughter]
Male Speaker: -- goes without saying.
Arul Chinnaiyan: Heidi?
Heidi Rehm: So, I mean, I completely agree that we need
more functional analysis.
I think the question is how to most effectively do it.
Because I think one off encounters with the patient leading to a one off functional study
may not be the most efficient, nor effective way to do it.
And one of the biggest challenges and very interpretations is interpreting literature
on functional analysis, especially when it’s been done on one or two variants, limited
validation of the actual assay to be a good predictor of the actual phenotype, et cetera.
I think one thing to do is to continue to share these variants or groups that have functional
approaches that if given access to large sets of variants that have actually been observed
in patients and the ability to go back to those patients and correlate their functional
studies with phenotypes back and forth, I think would be an incredibly rich exchange.
And so, I think we just have to figure out how to connect groups that are doing more
high-throughput studies that are well validated assays with the patients and the data that
they’re coming from.
Arul Chinnaiyan: Steven?
Steve Jaffrey: Steve Jaffrey from U Penn.
Different topic -- I want to give a shout out to some of the conceptual work that’s
been in CSER and emphasize the importance of that work in any future work that comes
out of this and specifically what I have in mind, on one of Robert’s first slides, you
talked about the different senses of clinical utility where channeling your own career there,
you know, certainly our sense – in broadest sense.
And one of the key questions we have is, so which of those senses should we take into
account when we’re deciding what to pay for with collective funds, whether they be
taxpayer funds or healthcare premium funds?
You know, the broader sense, anything that matters to somebody, you know, is that the
sort of thing that would sort of imply an obligation to pay for that with collective
funds?
Where on that spectrum should we be thinking about that?
And that is not entirely an empirical question and so that’s just an example of this sort
of conceptual work that I think we’ve been able to do in parallel with the empirical
work that has gone on in CSER and I hope we’ll be able to do in the future.
Arul Chinnaiyan: Robert, did you want to address that?
Robert Green: Yeah, no, not that in particular, but I just
want to say, building off of that, that two of the real key innovations, I think, in CSER
has been in the larger sense that Steve was pointing out, integrating LC constructively
with the implementation work.
And, you know, this is not L -- this is not your father’s LC.
This is not -- this is not sort of staking out positions in a way that could be construed
to just sort of be slowing down progress.
This has been a real working, interactive relationship that iteratively works back and
forth between the data and the policy.
And the second thing is Dan, building off of your comment, I think we have the opportunity
to really transform the entire notion of phenotyping from an 18th century phenotyping, which is,
you know, tapping on the knees and listening to the heart, to a 21st or 22nd century phenotyping,
which is true systems biology approach to the entire biological spectrum of disease.
And CSER won’t do that by itself or without a ton of help, but we could be one of the
points of that particular sphere.
Elaine Lyon: So, again, in my laboratory world, the reason
I’m doing this test is to help with the diagnosis.
Sometimes it’s to confirm a diagnosis.
Sometimes it’s to rule out, but in the clinical utility world, where does the value of the
diagnosis come into place even if there is no treatment.
There is not, I mean, I don’t know how to look at outcome studies if there’s not really
a treatment, but to come up and say, “Here is the diagnosis, therefore, we know there’s
no treatment.”
You know, at what point does that become valuable and what are the challenges with the genetic
diseases is that the outcomes are not always on the patients, but the outcomes are on the
family members.
So, how do you pull that together to demonstrate value?
Euan Ashley: So, I think and we’ll hear from Mike after
lunch, I think, and he can give you a very personal example, but from the perspective
of genetic disease as a practitioner and also from the undiagnosed diseases network, I think
we place great value on simply solving the puzzle, because -- not because solving a puzzle
is the end.
But that solving the puzzle was the beginning and is the beginning of the next search as
to learning about the biology of the disease and learning about the biology of the disease
takes us directly towards therapy.
So, I think the huge value in that and thinking about CSER 2.0, then I think that should definitely
be part of it.
And one last comment also, I think maybe some of us whispering over here, but we do have
much more power now with saturation, with the genesis type experiments and [unintelligible]
to do much more high proof functional studies.
They’re not the be all, end all, but we definitely can -- that take us much further
in terms of the disease in a dish along with stem-cell technologies.
To Robert’s point, we can measure people at times relatively cheaply and we -- but
we could not do just a few years ago.
So, I think that would also be something to consider strongly.
Robert Nussbaum: And I just wanted to follow-up on the comment
someone made earlier and I forgot who it was.
I apologize.
But I’m wondering whether it would be useful and interesting to have more emphasis on comparative
effectiveness between what we think of as genomic testing and what we think of as the
rest of medicine.
I mean, it’s very surprising, for example, how -- what the positive predictive value
is of a cholesterol level.
It’s really nowhere near what people think.
And I think there are many other things that we routinely do in medicine that are as good
or worse than what some of the positive value is from genomics.
So, maybe some comparative effectiveness between genomic and non-genomic medicine would be
interesting.
Arul Chinnaiyan: Sharon?
Sharon Emma Plon: Yeah, I just wanted to get back to the phenotype
issue and the re-phenotyping.
I think we just have to -- I think in CSER 2, it will be extremely important to have
some -- based on phenotypes agreed upon in advance of something we didn’t do in CSER
1 because of patient populations are so different.
I think if we’re going to re-phenotype people, we have to be very careful of the variance
we pick.
So, just from the example that Robert gave, some of those are incredibly well documented
variants for frankly just phenotype a few more CSER patients won’t be helpful.
Whereas, there’s others that associate with very severe inherited diseases where phenotyping
those individuals would be very helpful.
So, I think there is process of prioritizing the re-phenotyping that would really maximize
our knowledge of CSER patients and not be redundant with other studies.
Arul Chinnaiyan: Katrina?
Katrina Armstrong: So, I guess I was interested in the panel’s
comments.
One f the things that has really appealed to me about CSER over its history has been,
I think often we’re seeking to improve medicine.
Let’s say, either focus on developing the perfect tool, so we have the perfect test,
or we separate that and focus on how are we going to use it in clinical practice?
But the reality is, I think, as you’re suggesting, so much of how good the test needs to be depends
upon the clinical decision that we’re going to make with it.
And so much of this is really an iterative potential process and I guess I was interested
as you all think, maybe even your echo comment.
You know, how do we think about maybe the future of CSER is continuing in one of the
programs to really sit in that interface, recognizing that you are continually trying
to make the test or the tool better, but there’s actually understanding the application of
that in clinical practice that is critical for us to be able to make that tool better.
Because the perfect tool, to be honest is never going to exist and we’re always to
make tradeoffs of where do we need to focus on making the tool better?
And where are actually at the point that it can already inform the decision we need to
make in clinical practice?
I’d just be interested, I think Robert – both of you -- commented on that, but do you look
forward, do you see that as an opportunity for CSER?
Euan Ashley: What -- I mean , I think you said it very
well.
I think we have to balance the two and I think actually we’ve been doing a -- what we -- you
because we’re trying to really about CSER here, so I think the people in the room are
not part of CSER, have actually been doing a very good job of that.
I think paying some attention to the technical aspects improving that -- making sure that
process is iterative.
And then finding new places to apply this to then, casting those and feeding it back.
Actually, I see that as a major success of, you know, as I was reading through the packet
and obviously many of the papers I’ve read and were aware of the good works of the consortium.
But I think Bob’s point is also a really good one and sort of reflects the point we
made earlier that I think we can hold ourselves to too high a standard and that comparing
to standard tests that are currently used on in practice and accepted by other people
will make the narrative easier.
But I think just is more realistic.
Female Speaker: Can I ask?
You brought up the broader societal good as a measure of clinical utility versus proving
clinical utility at the individual patient level.
What does this group think?
Does this group think it’s worthwhile investing resources on the broader societal clinical
utility given the state of our so-called healthcare system?
James Evans: I would just have to say, I think we have
to be skeptical of the notion of social proofing.
There have been an awful lot of ideas that have been socially proofed, or proven, if
you will, that have turned out to simply be wrong.
And that’s where the primacy of evidence has to be maintained.
And I think it’s interesting to observe as things get taken up by society, in general.
But that doesn’t always result from a sensible uptake.
And five, 10 years later then the things that were embraced are found to be lacking.
Robert Green: I’m putting it a different way and I know
I’m going to get ribbed for this later, CSER is the conscience of the bio-tech tsunami
that is coming.
[laughter]
[end of transcript]