Heidi Rehm: Okay, so we started -- we have two slides,
and we started with a discussion that sort of took off of this diagram or paradigm.
In terms of evidence generation is, in our minds, a very broad topic.
It touches on a lot of different things.
I sort of started with this paradigm of a general genomic application and thinking about
an indication, a genetic test, an intervention, an outcome, and then wanting to supply evidence
to support any given application along these lines.
I some cases you're starting with a healthy individual, and it's more a screening opportunity
so you may not have an indication.
But, on top of this paradigm, one question we all asked, or was brought up, was, is this
need for evidence generation any different in genomics than any other medical application?
And I think, in some regard, no, but in other regards, we all agreed that there was added
complexity in the genomic arena.
And that comes in a few different flavors.
One, in some cases, the indications can be incredibly rare, and therefore the approaches
one takes to generating evidence may need to be different because of the rarity of certain
phenotypes.
The other arena that I think makes genomics a lot more complex in the evidence generation
area is the complexity of the genetic tests and the genomic tests, given that the content
of them, the methodologies that are used, the interpretive process can be highly variable.
And so in comparing scenario to scenario to scenario, if you're all using a different
test or you're interpreting it differently, it's going to highly influence your outcomes
and the ability to generate evidence to support your application.
And so, you know, in some ways you can break down some of these components into the need
to have evidence surrounding a method, or the content of your test, or the interpretive
process, et cetera.
There's probably more commonalities to other medical paradigms in terms of ensuring the
definition of your indication, although there was agreement of the needs for standard phenotyping
tools to ensure your ability to, you know, compare group to group.
Also, discussion surrounding -- given the rarity of certain indications and phenotypes,
the need to aggregate data in different ways, and that could be aggregation, instead of
at the individual variant level, maybe aggregation by gene, by disease, or even category of disease,
such as all extremely rare diseases, and tackling them as a group.
So these are some of the discussions that we had surrounded a number of these different
topics.
Towards the end we moved on to trying to address Geoff's major aims of stating the priorities
and the opportunities and the next steps.
I wouldn't say we spent a lot of time deciding which of these three buckets to put certain
things in, so I'm primarily just going to go down the list that we came up with at the
end in terms of major areas of either priority or opportunity, or places to start.
So one of those is to simply catalog evidence-generating projects that are out there today, because
if you want to build numbers and data, knowing that somebody else is doing the same thing
is a way to help generate the evidence you need.
We asked the question of whether the IGNITE coordination center might be a center to help
catalog the different projects that are going on.
It was also brought up, you know, whether to catalog the availability of not only data
but specimens that might be shared across centers and things like that.
We also talked about the need to standardize tests so that you can compare apples to apples,
whether it be methodologies, defining content of tests, standardization of the interpretation
of the tests, ensuring understanding of risk prediction, are we talking about a variant
with high penetrance or low penetrance.
Also, defining evidence, ensuring evidence for gene-disease associations, for variant
pathogenicity, evidence for test validity, and for treatments utility.
And also pointing out that we shouldn't hold ourselves in genomics to a higher standard
than the rest of medicine.
We also talked about scenarios where we felt there was sufficient evidence already, but
seeing the challenge of how do you adopt genomic medicine applications that already have evidence
that may not be adopted already, and some of those touch on the education paradigms
in terms of training, making cases for economic benefit, engaging the physicians in the process
to ensure eventual adoption so that if they are identifying needs, they can then help
implement them later on in developing society practice guidelines.
We also went on to think about ways to share evidence in actual physical ways, so thinking
about patient data sharing, recognizing that there may be difficulty with actual centralizing
this data, but at least identifying countries or systems that are willing to enable access
to patient data through federated approaches, and so, enabling the ability to share evidence
generated in those systems across different entities, and actually, you know, needing
systems to capture the evidence facilitating a federated network while with standardized
APIs to enable sharing -- sorry?
A API is an application programming interface.
And just a computational interface to allow systems to connect.
It's -- yes, you build an API that allows another application to dock on to it and gather
data.
If you make your APIs standard, then those systems can interconnect.
And then in terms of discussing areas of overlap, so what are other organizations already tackling
and can we identify areas of overlap so we're not redundant with our activities.
So those, we did not do any voting on the prioritization of everything in this list,
but those were certainly the major areas we discussed.
Geoffrey Ginsburg: Thanks.
Any comments?
Heidi Rehm: Aravinda.
Geoffrey Ginsburg: Aravinda.
Aravinda Chakavarti: I have a clarification, I just wondered if
you can expand on this standard -- I'm sorry -- the standard should not be higher than
other areas of medicine.
I think I understand what you're saying, but some may take it to mean exactly the opposite,
that it might be lower.
And I'm just wondering what the philosophy was and -- in you thinking that.
Heidi Rehm: Well, you know, I think this came up, for
example, you think about the use of imaging tools in medicine: used incredibly widely,
but how much evidence is there that they improve outcomes when using ultrasound and other techniques?
And so I think the question was, you know, if we set our standard, you know, in order
to implement a genomic application, you have to achieve x level of evidence.
Are we setting that too high for us to ever practically implement these things?
That, I think, was the point.
Marc Williams: And I guess the point I would add to that
is it's not our call.
That it will ultimately be the people that are defining what they're going to reimburse
that will be setting the standards of evidence, and so that's why I think a point that came
up earlier, which is engagement with those people that are actually paying for services,
about defining what is, in fact, the evidence so we have a shared understanding is really
a much more important activity than self-defining what we think the standard is.
Because you're absolutely correct, but the reality is, is that because we're the new
kid on the block, and because the standards have changed from 20 years ago, we are being
held to a higher standard because everything at this point is being held to a higher standard.
Heidi Rehm: Yeah, I think that's a valid point.
Male Speaker: So, I have maybe a suggestion to the list
and see what you think.
And I was in your grip for a little while, but I wondered if you had the chance to discuss
expanding on existing evidence-generating projects.
So, we heard that Canada has 17 programs running that are obviously going to generate evidence,
particularly in that environment.
We heard about the PGx card in Estonia, as well genomic -- genetic -- Genomic Medical
Alliance this morning with certain ongoing projects.
Would there be any enthusiasm for thinking about those as a foundational set of projects
that could be implemented and generate evidence in other parts of the globe?
Heidi Rehm: So the question is, if we were to gather what
are those evidence-generating projects, you know, I don't think all of them -- all those
projects and the funded grants are all about evidence generation, but if we could catalog
which ones were out there and ask two questions, are there duplicates where we could pull groups
together to amass more data; or, is a project ongoing of interest to another country to
implement, which I think is your main point.
And simply by defining what these projects are allows a example of how to do certain
project that might be of use to help another country implement it.
Is that --
Male Speaker: Yeah, I think what I'm suggesting is that
we already have a number of data evidence-generating programs around the globe.
We heard a snapshot of them the last couple of days.
Maybe to your point, cataloging them, and then really having a strategic discussion
across this stakeholder community about where is there an opportunity to really accelerate
the generation of data that would provide the clinical utility and other, you know,
outcome measures that we aspire that they should have, or see if they reach those thresholds
that we're looking for.
Male Speaker: If I could just weigh in for a second, so
I think the idea behind number one there was to think -- rather than go out and create
a whole bunch of new projects, there was a strong sense that we're doing -- we're not
doing as good a job as we could at capturing all of the evidence generation that's currently
going on out there.
And so the idea of a catalog would allow us to think about where we should capture the
data from, and the idea of actually identifying countries willing to enable access to patient
data, even if it was aggregate data that said, "This is evidence," and that evidence could
be deposited in a centralized database.
The one thing that's sort of not on this list but we talked a lot about is the opportunity
for things like ClinGen to actually be part of this federation that Heidi mentioned, where
that data gets aggregated.
So, I think it's all about trying to do a better job capturing these projects that are
going on around the world already, rather than thinking about generating a whole bunch
of new projects, because I think there's a lot going on.
Male Speaker: I know, and so I think that's a great point.
And not to belabor what I'm trying to say, is if we took Patrick Tan's eye project that
we heard about yesterday, and, you know, began to see if that has relevance, first of all,
in other places around the globe, and could that type of genetic testing platform be seamlessly
implemented, and what are the -- if not, what the barriers so we can begin to understand
that, and also, at the same time, generate the data that makes a compelling argument
that these should be reimbursed and clinically adopted and part of guidelines, and all the
things that I think we want to see genetics do.
I'm just trying to push your catalog notion to a next step, and I'm not sure whether you
agree or not that that should be the case.
Heidi Rehm: I think I understand what you're saying, and
I think there's a couple different opportunities there.
As you catalog these things to, in some ways, get -- what is the interim answer, or is it
already accomplished and you have an outcome?
And for those that show a positive effect, you might want to then implement them elsewhere.
For those that aren't quite achieving an appropriate level of evidence, but it appears that if
you expanded the numbers, you might do that, and so that would be an argument to then replicate
that study in another environment so you can build, you know, additional evidence.
And then in other cases, the study is showing that there -- that it isn't appropriate, and
maybe you deprioritize those projects, you know, being executed elsewhere because you
already have some data to show it's going the wrong way.
So, by seeing not only what's going on but where things are, deciding how to prioritize
either implementing them as is, or adding more data by doing another, you know, larger
numbers, or deciding not to pursue an area.
Marc Williams: And I would just note that this -- these align
closely with two things from the first workgroup that didn't quite make the cut, but, you know,
aggregation of guidelines, and also the collection of the actionable variants, however you want
to define that.
So, that would seem to be very synergistic, and now that that's emerged from two different
workgroups, that may, in some sense, suggest that that's a valued activity.
Heidi Rehm: Yeah, I mean, I think it's -- in my own opinion,
it's incredibly important that we all agree upon the fundamental evidence for a gene-disease
association and whether variants are pathogenic because your outcomes will be so heavily impacted
by making wrong decisions, you know, in that early step.
And that is a critical fundamental resource that we all need to work towards.
Female Speaker: Heidi, this question is probably superfluous,
but did you consider the big picture sources of evidence?
And by way of example, I imagine that in the world of pathology, there is a lot of evidence
generation, which cuts back to the workforce question.
And secondly, if one looks at the example of the Karolinska Institutet and AstraZeneca,
who have just put all of their scientists under the same roof and said, "Please work
together," there is probably some evidence that is generated by industry as they manufacture
some of the tools that we use.
Heidi Rehm: Yeah, you know, we talked about this a little
bit in terms of recognizing that a lot of the evidence that is -- that we'll need is
sitting in healthcare systems and in other places, and this need to have a federated
system to have access to that.
But, you know, one of the reasons that I was a bit in turmoil in the first section about
the EHR fields being of critical importance versus variant databases, which is obviously
one of my loves, is that if we don't agree upon the fields that need to be in these data
sources that we can draw from, there's nothing to draw from, right?
You know, what are those fields, who enters in them, what's the standards for what we're
entering, and those are going to be critically important to actually understand our outcomes.
And absolutely, in terms of the sources of data and the existence of fields for those
sources of data are absolutely critical to everything we do, and I totally agree with
that.
Geoffrey Ginsburg: Great, thanks, Heidi and Rex.
Next up is pharmacogenetics -- pharmacogenomics, sorry.
[applause]