Richard Gibbs: Thanks, Rick, and thanks for everybody to
listening to our tag team here.
This is how we see the big picture for the clinical translation and the different applications
that can be done at different scale of activities.
So we're on the left here, the smaller projects.
Here is targeted clinical testing where you might have really a predetermined reason to
go in and do detailed study; clearly affected families for example.
Here's where we'd moved that out into a healthcare setting where it's more generally available,
perhaps without as many priors to beginning the study.
And here's truly ubiquitous application of our methods.
The scale on the bottom here is a log scale: thousands, tens of thousands, hundreds of
thousands and eventually millions.
Now if we want to be out here you might say, "Why can't we be?"
since you just heard from Eric, and as you all know, that we're in this world right now
where we're doing research, and our $700 exome and $5,000 or so whole genome world, we're
doing disease discovery in projects of scale of around 1,000 to 5,000 samples, and indeed
we're already looking at cohorts with 10,000 samples or more.
And if we follow the future, as Rick has just pointed out, with the developing technologies,
we're going to move out here and be in this truly large scale range that we can extend
the studies.
But that is the research domain, and so we're talking now all about the clinical translation,
and there's something of a lag there.
So here we are more in the 1,000 to 10,000 sample range right now, ably doing projects
with a few hundred samples, and only in a few places getting this up into the thousands
of numbers.
So why is this running behind the research arena?
Well it's clearly because that there are different demands in the clinical translation space.
I think you all know the CLIA environment demands stable processes with 100 percent
precision; the research, 99 percent precision, just doesn't cut it.
That adds cost and reduces the throughput.
One has to deal with heterogeneous samples, not going to the freezer to get 10,000 samples
at a place, but getting patients in one at a time.
There's individual reporting and human input required at all steps.
So, there really are new challenges as part of the scaling -- the clinical genomics.
But it's not all bad news, of course.
We've got good experience now with porting activities from our larger research genomics
projects into clinical labs.
The optimal practice, we've developed those in the context of these high-throughput genomics
process.
We calibrate and stabilize the methods, document them, lock them down, declare we have standard
operating procedures, and then deploy those in a clinical environment.
And we're seeing a few examples of that now indeed happening.
So we could say, indeed, with some confidence, large-scale research studies do provide this
foundation for clinical translation.
What are some of the actual examples of those transitions and throwing the technology over
the wall into the clinical labs?
We have the whole genome and whole exome experimental techniques at the lab, and there's also been
really strong developments in the variant calling and validation data handling methods,
managing reference databases rationally and effectively in the clinical setting to look
for pathogenic variation, and bringing just all of the other tools that we've learned
for secure and proper management into process management phases in the clinical lab as opposed
to the experimental homes that they came from.
We've also been doing this, I think very effectively, in data sharing.
You're hearing a lot of talk about cloud and large network applications really that drive
us through this on the research end, and they're now bringing those into the clinical end as
well.
So we have further opportunities to accelerate translation by more lessons joined from the
research area.
You know, what's the current state?
Well, we're seeing -- we're seeing some of these activities going on in CLIA environments
partly supported by insurance companies but still being leveraged by these development
programs.
We're in a state now a fraction of pediatric cases are being solved, about a quarter of
them in our hands, but I think, generally, that number is around there in other standards
as well, and maybe 5 percent of cancer cases properly guarded by these activities.
So we have a lot of -- I guess the ceiling is high, is the point here.
We have a lot of room to transfer, further this knowledge over into our clinical environment.
I'd say globally it's only about 10,000 cases per year that are truly going through a clinical
diagnostic environment, bearing all the bells and whistles we have developed in the research
environment.
So what are some more opportunities?
Well, here's just a selection that come from us.
I think you can draw from Eric's talk and from Rick's talk other examples, but we certainly
have examples where sequencing a few hundred cases in a different context has shown us
that there are more genetics going on than we would have expected.
We did a study on cerebral palsy that showed that that 25 percent of the cases actually
had a genetic contributor, which begs the question: How ubiquitous are those genetic
compounders in what is sometimes regarded as not a genetic disease but may just have
a few Mendelian forms, maybe there are many more.
How ubiquitous is that, and what could you learn from a much larger study done with full
clinical annotation?
Another example of that is a real one, we have a few cases now where we have, through
de novo mutation identification, recognize new syndromes.
One case -- four cases with a de novo out of 2,500 in our local clinical cohort.
Now, that begs the question, "What's the real frequency of this disease?"
We're only seeing a fraction of possible cases.
There's a lot of filters.
If you extrapolate out the birth rates and overall likelihood of children that are in
this class that might have the disorder, there could be 200 or so of those cases here in
the U.S. and many hundreds worldwide simply not recognized especially as we extrapolate
that.
And what about building full genetic models by looking at various syndromic conditions
where we know that not every case can be solved as a simple Mendelian case, but this is not
necessarily the same as complex disease, but where we can find, though detailed clinical
annotation, deep sequencing, and adequate sizes, the kind of genetic data that can build
full oligogenic models that can deeper our understanding of the disease pathology, doing
genetics here somewhat from the ground up.
So all this becomes possible as we move into this additional clinical space.
So, where will we get samples for such things?
Well, they're not exactly on the shelf like the cohort studies and the case control studies
that Eric spoke about, but they're not so far away from us either.
We certainly have private healthcare networks that are large that are already being tapped
into in these large-scale ways, and actually, Teri, you cited some beautiful examples from
other countries.
I wish we could be -- I wish we could be counted up there amongst those examples, and there
are state screening programs.
At the family study level we certainly have advocacy groups and clinical treatment groups
that can bring us literally thousands of samples for these kinds of studies.
The clinical clusters of developmental delay, preterm birth, and families with multiple
different cancers, et cetera, et cetera, et cetera, also represent big opportunities to
aggregate.
Another opportunity that's represented from the previous slides -- the menu on the previous
slides is to extend these Mendelian studies to fully describe the mutations in loci known
to cause rare disease.
Eric showed the nice slide of the cancer genes where if you get enough mutations, you begin
to understand the function of the cancer gene.
We need, for these new rare syndromes; we need to aggregate the information of the mutational
spectrum from the hundreds of cases that exist so we can understand the relationship between
the kind of mutation and exactly how the disease manifests.
We can only do that by coordinating our denominator here.
And lastly, we can look most broadly at the sporadic cases to build our oligogenic models
in large sets of multiple patient admissions, such as regular screening for home medical
center admissions.
And that would be the very outer rim of our onion skin diagram with the largest input.
And that sounds like hand waving and extremely optimistic, but it's exactly also what was
shown in Teri's slides that's going on in the U.K. and in other countries, so we hope
we can speed to there.
So that's the end of our tag team, prior to discussion, and I guess we can take questions
now, if we still have some time left in our allocation.