Matthew Myerson: So we have a couple of minutes for questions
for really the three outstanding speakers from this morning, so if there are questions
from the audience please come forward to the microphones.
Female Speaker: Yeah, I have a question basically for our
speaker and related to the TCGA efforts, how our speaker comments on the [unintelligible]
data for sample preparation and data preparation, and how the whole mostly this is like a work
lab [spelled phonetically] research is involved in this, and I'd like to hear the speakers
comments on how computational side of people are involved within the sample preparation
and data preparation.
And also, on the other hand, is for when they design new computational software and the
tool is like computational people and will work on this, like how the work lab research
are involved within this.
So second question is specifically for our [unintelligible] speaker, and relating to
the subgroup, molecular subgroup of the cancer, and I'd like to know specifically what kind
of computational software are you using for that?
Thank you.
Kenna Shaw: So I'll go ahead and take the first question
which was, I think, how basically the clinical side works with the computational side in
this project.
So TCGA, the way we run the pipeline is the front end of the pipeline with the tissue
source sites.
The tissue source sites are first involved very early on when we established a disease
working group.
So when TCGA determines that we're interested in providing this resource on a given tumor
type, we bring together experts that include pathologists, oncologists, biologists to study
specific organ sites; you know, if it's thyroid, we bring an endocrinologist, et cetera, so
the clinicians come in from the very first step.
They provide us all the guidance we need in terms of determining whether we -- what our
inclusion and exclusion criteria are, et cetera, and then that group works with us to acquire
the samples.
That same group is then brought back at the end when we start doing the analysis.
So the TCGA funded investigators, in terms of our data generating centers and our primary
data analysis centers, are not necessarily experts in every single tumor type that we
study, so we bring in those external experts back into the loop when we start doing the
analytical phase.
Now we also have -- every center does their own analysis on their own dataset also.
Plus, we have these seven genome data analysis centers, and each one of those groups actually
works with clinicians generally on-site at their own institutions to make the tools more
clinically relevant for the disease-specific parts of the data that they put on their own
specific portal.
So, you know, MSKCC cBio, which is also I believe having a workshop this afternoon,
they have their own computational suite; essentially, Firehose at the Broad also does the same.
And so a lot of these groups also have their own different interpretations and their own
different algorithms that they're using, and depending on what the right tool is for the
question at hand is when we bring those specific analysts in with the specific disease experts,
and again, right now, it's still one at a time.
For the PanCancer group it's a bit more of a challenge because we're going across many,
many different tumor types, and the clinical questions are obviously more difficult to
generalize.
And I'm going to turn it over to Lou for the second question.
Louis Staudt: Just real quick, there's many, many ways to
use profiling.
We -- and make cancer subgroups, so we happen to use a statistical model based on Bayes'
theorem, where we estimate, based on a signature of roughly 100 genes, the probability of being
one or the other type, so it's standard stuff.
Male Speaker: I have a follow-up question for Dr. Staudt,
too.
I'm not sure how plugged in to the current regulatory environment you are with diagnostic
testing and thinking about clinical trials and stratifying based on genotype or gene
signature, and how that interacts with new and proposed rules that would need to be for
testing, where testing would need to be validated before the trial, and so we're talking about
I think a real challenge going forward.
Louis Staudt: That's -- thanks.
I'm right in the midst of that, unfortunately; it's a difficult landscape developing the
diagnostics in genomics.
We're trying to do it for the distinction between ABC and GCB; you could just as well
try it for the various mutations.
I think the paradigm is that, for these target ages, you need a co-diagnostic that's developed
along with the drug, and they then are approved simultaneously by the FDA.
But to do that, because, by definition, you will be changing clinical care, the bar for
that quality of that diagnostic is extremely high, so it's going to challenge us to go
beyond what we consider "pretty good" in the lab to something that is really going to change
whether someone gets treatment A or treatment B. So thanks for bringing that up, and it's
not going to be easy but an important struggle.
Matthew Myerson: I think we're going to need to take the next
questions offline because we'll need to move forward to our next session.
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