Andrew Gross: So, I'd like to thank the organizers for inviting
me to come out and give this talk. I think we have some really exciting data tying P53
mutation to heterozygous loss of 3p Chromosome 1. So really briefly, head and neck cancer
represents about half a million new cases a year, but its incidence is increasing due
to risk factors, such as HPV infection and alcohol and tobacco use. So we're starting
to understand a little bit of the molecular basis of the disease. Outcomes still aren't
very good with limited targeted therapies, and only about 40 percent, five-year survival.
So this study kind of started with that end to really gain a better understanding of the
disease, spurred on by our clinical collaborators. And from the methods side, we are really looking
for ways to develop, you know, new methods to integrate data costs across platforms,
and ultimately to ultimately try and isolate genetic interactions from a cancer cohort.
So, I'm going to go pretty quickly through this to try and stay in time, but feel free
to find me at my poster tomorrow; I can go into more of the details. We've tried to make
this analysis as transparent as possible, so the whole reproducible pipeline will be
available online. Everything from downloading the data to generation of figures and statistics
as soon as this paper reaches publication.
So [unintelligible] the cohort is very diverse, and to try and homogenize it for the kind
of analysis we're doing, we limited our patients to those who were under 85 and we, for the
majority of the analysis, we're only looking at patients that don't have presence of HPV.
So the study design -- again, I'm going to go pretty quickly, but feel free to find me
at my poster. We started out kind of a top-down, unbiased screen, and we wanted to try and
isolate, you know, what are the core sort of candid biomarkers that we could find in
this data set. And so this included frequently mutated genes, differentially expressed mRNA
and microRNA, and kind of recurrently altered copy number events, as well as clinical variables.
So after identifying these events we just ran a basic screen to try and find markers
that correlated with patient outcomes, and we identified about 60 of these, and then
kind we of went another step to finally look for associations among pairs of these prognostic
biomarkers. And so, as you can see here, P53 is mutated in about 200 of these patients;
we have heterozygous loss of 3p arm in another -- in 200 patients as well. And if you look
at the overlap of these two patients, this occurs about 70 percent of our patient cohort,
and this is much higher than you would expect by the marginal probabilities. And so at this
point we decided that this was kind of a good interaction to go after, because we know that
head and neck cancer -- P53 has well been understood as a kind of prognostic biomarker,
as well as an indicator of, you know, aggressive disease in this tissue. But what hasn't been
known is that this 3p deletion is very commonly co-occurring with this, and this happens in
70 percent of this patient cohort.
So one of the really nice things about TCGA is, while we were undergoing the study, another,
you know, another set of patients were sequenced, and so we were able to, you know, train our
-- find our associations in this discovery cohort, but then validate them in 111 -- and
now I think it's up to 170 -- new, recent TCGA patients. And indeed we see actually
an even higher effect size within this validation cohort of this co-appearance. So we implicated
each of these genes based on their associations with the prognosis. So the next thing we asked
was, you know, what's going on? Is the prognostic affect additive between the two, or do we
see some sort of interaction?
And indeed, while there is a small effect of each individually, the majority of the
prognostic effect is mediated by the patients that have both of these event together, such
as -- P53 mutant patients that don't have the deletion have actually pretty decent outcomes
with respect to this cohort, and so this was an interesting finding. Unfortunately, our
TGCA cohort doesn't have updated clinical follow-up for these recent samples, but we
were able to collaborate with Jenn Grandis and Ann Egloff at University of Pittsburgh.
And this is kind of one of the early exome sequencing studies that was published in 2011,
and since then they've kept really good clinical follow-up for these patients, and we were
able to recapitulate this finding that within these P53 mutations the addition with the
3p loss does confer a worse prognosis.
So, I'm sure a lot of you are thinking right now that really what we're seeing really is
this link between P53 mutation and chromosomal stability that's been described very often,
and this is what we thought originally too, but when we test for the co-occurrence we
see the co-occurrence of the P53 with the 3p loss is much more frequent than a number
of other -- than any other chromosomal alterations, including alterations that are more highly
deleted or amplified in the cohort. And in addition, we kind of did this experiment to
construct a very simple multivariate model. And so, what we can see here, these are the
hazard ratios in the multivariate model with these two -- with the 3p loss and general
chromosomal instability variable. And what we can see is that when you take 3p into account
there is no additional effect of chromosomal instability. This result recapitulates in
P53 mutated patients, but when we look at the wild type patients, this -- we see kind
of a reversals to this trend. So there's the kind of prognostic trend of 3p is actually
a general trend with chromosomal stability, so there's no additional effect of 3p in these
patients. And this kind of gives us further evidence for an interaction effect when we
look at these events with respect to prognosis.
So, so far we've only been looking at these HPV-positive patients, but we know that -- HPV-negative
patients, I'm sorry -- but we know that part of the mechanism of action of HPV is that
it integrates into the host genome, and the E6 viral protein binds P53, leading to its
degradation. So then we tested whether -- you know, what -- is there an effect of 3p in
these HPV-positive patients, which we know have P53 inactivated? And indeed, we do see
a relatively large prognostic effect in this -- in our cohort of HPV-positive patients.
So another question we asked is, we've done this analysis in head and neck cancer so far,
but we know that a lot of patients have P53 mutation, and a lot of patients have this
loss of this -- ths heterozygous loss to chromosomal arm, so we generalize these results? And for
this we did a Pan-Cancer survival analysis, and off the bat you can see that the survival
occurs for these different tissues are very heterogeneous, so we try to homogenize it
a little; we only looked at non-metastatic patients; we just -- we didn't look at patients,
such as glioblastoma, which have very poor out comes; or thyroid cancer or prostate cancer,
which have very good outcomes. And indeed, when we plug it into this patient -- this
cohort of about 4,000 patients, we do see a clear effect of the combination of these
events versus each individually, and this holds, even in a multivariate model, including
tissue types, stage, and age. So we think that that might be a broader -- this could
be a broader signal, at least within subsets of other tissues.
So we've kind of established this combination of events in head and neck cancer, and so
the next question we ask is, you know, given this clear sub-typing, can we further stratify
the patient cohort? So for this we repeated our screen. So we from feature construction
to this prognostic screen, and we see that amongst this poorly performing group, these
patients that with the combination of events, the presence of the microRNA mir-548k, leads
to even worse prognosis within these patients. So this microRNA is very interesting. So it's
not expressing normal tissue and it is induced in at least a fraction of tumors, and this
is how it got characterized as sort of a candidate biomarker. It's located at sort of a focal
copy number amplification, and the expression trends with the copy number gains, but when
we look at the prognostic effect, it segregates only with expression; so patients that have
the amplification, but don't have expressions -- so that would be these patients down here
-- they actually are in the good -- the well-performing group.
And so while we were working on this, we got really excited, because the another group
working on esophageal Squamous cancer recently implicated this gene from a similar, you know,
unbiased screen, and they did some really nice functional work in cell lines showing
that this does indeed have oncogenic potential. This paper was published in Nature, I think,
two weeks ago, and it's a really good read. So we think that this microRNA could have
broader implications for at least of variety Squamous cell carcinomas.
So finally, we talked a lot about these patients with this combination of events, but there
are about 72 patients that don't have these events going on that have relatively good
outcomes, so can we look into those patients and see what's going on with them. And so
for this we conducted a secondary association screen; we didn't really have enough data
to look at prognosis, but we can look for events that are specific to these 72 patients.
And indeed we do find that these patients enrich for a CASP8 mutation, which you know
have a role in apoptosis, as well as RAS signaling pathway mutations, including a number of HRAS
gain of function mutations. And again, these results validate, and the recent TCGA patient
--cohorts with even a larger affect size.
So in conclusion, we've shown that P53 mutation is frequently co-occurring with 3p loss in
the head and neck cohort. This leads to worse prognosis, and we show that in a couple of
different cohorts, as well as a Pan-Cancer cohort of about 4,000 patients. Amongst these
patients we find that the expression of mir-458k leads to worse prognosis, while the absence
of this kind of common driver event, we see other pathways that may be important drivers
in this tissue. So with that, I'd like to acknowledge this was really a team effort.
We had a lot of clinical collaborators that -- you know, a lot of people contributed good
insight, including Scott Lippmann, Quyen Nguyen, Ezra Cohen, and Ryan Orosco; Jenn Grandis
and Ann Egloff, who contributed the validation cohort; Neil Hayes really helped us get through
this TCGA data and understand the inner workings. This is the whole Ideker Lab, and specifically
the Cancer Team. Each contributed to this project, so Michel Choueiri, JP Shen, Matan
Hofree, Hannah Carter, and Trey. So with that, I'll take questions.
[applause]
Josh Stuart: We have time for questions. Rachel?
Female Speaker: I'm wondering if you looked at the difference
between disruptive mutation in P53 and non-disruptive.
Andrew Gross: Yeah, so we actually -- we did, and what we
find is that, while disruptive mutations did have a worse prognosis, the non-disruptive
were significantly different from wild type as well. And so we weren't able to get down
to that granularity into this pipeline, and we really did try and make this an unbiased
screen, so injecting that kind of prior information that you have for P53 kind of gives it an
unfair advantage against all the other significantly mutated genes.
Male Speaker: Hi, you have those -- some variety in your
other slides. Can you elaborate on -- do you see this in lung Squamous cancer?
Andrew Gross: So there's not a lot of data for lung Squamous;
we do see it in lung edema. We think that there's an effect, and in the Pan-Cancer analysis
we see small effects in a large number of cancers, so it's really hard to pull out,
you know, exactly where we see the effect in the Pan-Cancer context.
Josh Stuart: I had a question to follow up on Rachel's,
actually, really quick. How did you define, when you did the check on P53, damaging versus
non-damaging? How do you define those classes?
Andrew Gross: So we ran a few analyses on this. We looked
at different functional classification; so we looked at truncating mutations to the binding
domains, and there has been some published work that have built sort of a classifier,
as you know, disrupting mutations. So these would be mutations to the binding domains
that kind of have a polarity change, or something like that, or truncating mutations. And so
we did that as well, and that -- while that does add a small amount of information, that
would induce too many false negatives into the analysis.
Josh Stuart: I had one other question. Did you guys do
the reverse? Take 3p loss, and look for other mutants that also interact with 3p loss events
as far as their prediction of survival, besides the HPV?
Andrew Gross: Oh yeah, so you know, by far the strongest
signal is this cohort is this interaction of the P53 and 3p, and generally, anything
else -- any other correlations we can find can be explained away by this interaction.
So it's kind of such a big -- it's kind of the elephant in the room, and so anything
else --
Josh Stuart: -- examples to check other --
Andrew Gross: Yeah, yeah. To really get any further, it's
pretty hard to overcome this relatively large signal.
Male Speaker: Did you try to go after specific genes on
3p? I mean, put in a different way, do you have an idea of what are the genes individually
participating in this operation?
Andrew Gross: Yeah, so this is -- we would love to do this,
but in general we see kind of most patients have a full or the majority of the arm lost;
we don't really see peaks. There is a slightly higher fraction that have loss at the -- at
a fragile site, at the repeat 14, but we can't really break it down any further than that.
Josh Stuart: Great, let's thank Andrew one more time, and
all of these speakers for this session. That rounds us up.
[applause]
Josh Stuart: Matt had an announcement I think before we
adjourn, so let's wait for Matt.
Matthew Meyerson: All right, so in addition to thanking the
speakers, I just wanted to thank the session chairs, Han Liang this morning, and Josh for
this afternoon for great leadership of the sessions. And then two announcements. First,
the workshops begin at 4:00; they're all in the schedule packet, and they're running twice
both at 4:00 and 5:00. And the second announcement, there are several cancer-type analysis working
groups that will be this evening. If you've registered for one of those working groups,
Margie Scheth [spelled phonetically] was saying that everyone should meet in the cafeteria
at 5:45, and she will direct you to the room for each of those. And that's all.
Josh Stuart: You're excused.
Matthew Meyerson: We're adjourned.
[end of transcript]