Ari Hakimi: Good morning. Thank you very much for this
opportunity to present here. It's very exciting for me. I'm a surgeon working with Dr. Hsieh
-- James Hsieh at Sloan-Kettering, and we're part of the Kidney TCGA project -- we've had
the distinct opportunity to work with Chad Creighton and Richard Gibbs on this project.
And we're going to present some of our work that we've submitted as a companion paper
to Journal of Clinical Oncology related to some of the clinical correlations that we
can get from the -- from some of the sequencing data.
So just for background, RCC is the sixth-leading cancer cause of death in the United States.
There are several malignant subtypes of which clear cell has been the focus of the initial
TCGA project, although there are -- papillary and chromophobe also in the pipeline. Nearly
one-third of these tumors present with metastatic disease, and these tumors are characterized
by their -- the fact that they're chemo and radiation resistant with the urgent need,
then, to identify novel pathways for treatment.
In the past two years, there's been an explosion of sequencing data, largely from the Sanger
Institute, that has identified several recurrent mutations, some of which we've talked about
extensively this morning. VHL was really the only known gene to be mutating kidney cancer
for many, many years, due to the pioneering work of Marston Linehan, et cetera. But in
the last two years, several chromatin modifiers have been identified, including PBRM1, SETD2,
and BAP1. And the fascinating thing about these genes and the reason why we were interested
in them initially, even before the TCGA project really started to confirm this, was that these
were all located on 3p21.
And you know -- in fact, VHL is on 25, but these three other genes are -- really reside
within the same locus. This is really an unparalleled phenomenon to have potentially four tumor
suppressors lying in the same region. And the reason why I say they're tumor suppressors
is because one of the hallmarks of kidney cancer -- this is from the TCGA project -- is
the fact that 3p is lost in 90 percent of the samples. So the fact that you have four
genes in the same region that's lost 90 percent makes this a very intriguing topic for us,
and therefore we pursued any clinical ramifications of these.
As I mentioned, PBRM1 is one of the ones that was reported in Nature in 2011 and this is
a classic tumor suppressor in that you see mutations that really span the entire length
of the coding regions of the gene. And this has been shown in this paper, which is really
just defining this as an event, as being associated with tumor proliferation, but really made
no assessment of the clinical ramifications of this gene.
The other thing that was intriguing to us was that this -- more and more papers that
were sequencing from other institutions were reporting these genes being frequently mutated.
This is from the BHN [spelled phonetically] Genomics Institute, which also highlighted
these four genes, and as you see from a couple of the presentations this morning, these are
significant, regardless of how you analyze the data.
The other thing that was interesting is that these are all part of the histone and chromatin
modifying family, and one of the interesting things from a therapeutic standpoint is that
some of these genes now -- they're designing specific drugs to target mutations in these
pathways. So potentially, besides just being a prognostic marker, there could be treatment-related
design based on these alterations.
In terms of what's been known about kidney cancer and clear cells specifically, VHL mutation
as I mentioned before is really the only known frequent event until about two years ago.
And in terms of its -- the utility as a prognostic marker, there's really been no evidence to
suggest that a VHL mutation alone, or the location of the mutation, [unintelligible]
mutation has any bearing on prognosis. And that has to do with the fact that VHL is probably
lost upwards of 80, 90 percent. Obviously, it depends on how pure the tumor samples are
and how deep you sequence, but you see mutations very frequently, certainly upwards of 60 percent,
and the methylation rate is about 7 percent. So couple that together, it's probably a fundamental
event of carcinogenesis as opposed to a prognostic marker.
So what we did initially was we sequenced these four genes in 188 tumors from our own
cohort; this was distinct from the TCGA. We used this as a discovery cohort. We looked
at pathologic correlations, association with tumor size, grade, and stage, and we looked
at clinical correlations. And then we validated this data with much larger dataset than TCGA,
which was 424 samples with whole exome data available.
Now one thing I want to point out here, which I think is very important for anybody that's
going to be looking at any TCGA project in terms of clinical data, is to really understand
-- and this is where the key is, I think, involving both the computational biology,
the clinicians, and pathologists -- is to understand what is an event. So a lot of the
tumors that are sequenced in TCGA -- certainly, some of the earlier projects like GBM and
high-grade ovarian -- most of those patients were dying from kidney -- from their respective
cancers. However, the TCGA project for clear cell was a very well-balanced cohort of patients.
These were a lot of patients with early stage disease, intermediate stage disease, as well
as high grade -- as well as advanced stage disease.
So when you actually look at the number of deaths from overall survival -- 144 patients
died in the cohort, but when we actually sat down -- and I sat down with one of our biostatisticians
to work on this -- about a third of those were non-cancer related deaths. So it's very
critical to understand in terms of identifying events that are associated with poor survival
is to really understand what is going on in that -- in that particular analysis. Anyway,
but our two cohorts were very well-matched in terms of clinical features. There were
more events in the TCGA dataset. Our tumors tended to be -- have less -- slightly less
follow-up and less cancer-specific mortality.
This is an overview of the mutation between our two cohorts in terms of the frequency
and the patterns of overlap, and you can see, obviously, as I mentioned before, 3p is lost.
This is where all the genes lie, and the fact this is an RACGH [spelled phonetically] showing
that these -- at least one copy is lost in about 90 percent of the samples. The frequency
of overlap was very similar between the two cohorts in terms of VHL being mutated at least
50, 60 percent. VHL -- PBRM1 are in the 30s, and SETD2 around 11 and 80 percent respectively.
And the types of mutations were also similar in terms of the spectrum of truncating versus
missense mutations.
This is our cohort of the 188 showing the very similar patterns to what's been shown
previously, that these -- although there is some clustering in BAP1, they tend to be across
the -- spanning across the genes in these -- just, again, the hallmark of a tumor suppressor.
So in terms of associations, this is something that we noted right away in terms of our cohorts.
This is showing associations with size, stage, grade, necrosis, lymph node metastases, and
mets. And these are all the classic factors that we use as clinicians in terms of prognosis.
These are the -- these are what are in our multivariate models.
And we looked at -- right away we saw some striking associations with one particular
gene, which is BAP1. We noted that BAP1 was associated with essentially every single poor
prognostic factor: large tumor size, higher tumor stage, higher nuclear grade, and met
metastasis presentation. Interestingly, in our PBRM1 cohort we saw -- sorry, in our MSKCC
cohort, independent cohort, we saw an association with advanced tumor grade. However, what was
interesting about that was that that was associated primarily with small tumor size. So in small
tumors -- in clear cell, tumors that are less than four centimeters; about a third of them
will eventually become invasive, even at the small level. And what we found in our cohort
was that the PBRM1 mutations were occurring in those patients with the invasive tumors
in the small level, and the actual frequency of a tumor invasion in the TCGA cohort was
much lower. And we think that has to do with the meticulous dissection that our pathologists
do at our institution in terms of upstaging patients in that smaller cohort.
But we also noted, though, that BAP1 was associated, as I mentioned, with all these adverse features.
And this was reassuring because a recent report in Nature Genetics found that by -- Jim Provoles's
[spelled phonetically] group from UT Southwestern found that BAP1 was associated with high tumor
grade, and actually further validated the fact that this is a very important mutation
in terms of prognosis. Now, many of you may have known that BAP1 is a critical player
now in other cancer types, most profoundly in uveal melanoma, which really defines the
poor prognostic group of tumors. It's also been showed by Mark LaDonne [spelled phonetically]
and others in mesothelioma as a critical player. So this was exciting to us to see that we
were able to recapitulate our findings.
As I mentioned before, the associations with tumor invasion in small tumors -- we found
that not only PBRM1 in our cohort but also any mutation in any of these genes -- BAP1,
SEDT2 -- were associated with tumor invasion. Now, in terms of survival -- this is our MSK
cohort -- we saw associations with poor survival for BAP1; very strong association even though
we had relatively few events. We then looked at that cohort in the -- we then looked at
the TCGA and we found very similar associations not only with BAP1 but also SEDT2. And again,
if you will look at overall survival alone, there is not a strong association with SEDT2,
but when you pare it down and you actually look at cancer-specific survival -- and again,
this is something I really want emphasize -- you definitely see a strong correlation
now with SEDT2 mutations and poor survival. And this is really the first time in kidney
cancer that we're able to report a gene being associated -- a mutation being associated
with poor survival.
We also found -- we also were able to -- with the help of Kim Rathmell, and our biostatistician,
Irina Ostrovnaya, we were able to look at time to recurrence -- these are patients that
have no evidence of disease after surgery but then develop new disease. We've had a
very striking phenomenon where these patients are much more likely to have -- these tumors
were much more likely to have a SEDT2 mutation. So we now think that this -- that tumors with
BAP1 or SEDT2 mutations are critical for tumor progression and they're also associated with
poor survival.
Finally, with the help of Boris Reva from our computational biology department, we did
a pan-TCGA analysis to understand what are the additional events that tumors acquire
as they progress. And I look -- and I show you this box plot here showing that as tumors
progress -- and these are the staging system that we use by tumor size -- they acquire
-- the number in parentheses here is the median number of tumor suppressors. And we defined
those as genes that are truncated -- mutations that are truncated mutations in the setting
of a copy number loss. So high functional mutations -- and Boris actually has a poster
on that in -- which I encourage everyone to see where he kind of talks about this concept.
But we looked at the additional acquisition that tumors have as they progress, and what
you can see is that there's just an additional one to two to possibly three mutations that
tumors acquire from a truncating tumor suppressor standpoint as they progress: as they increase
in size, as they increase in grade, as they increase in stage. And what this shows here
is that the number of times those mutations are populated by BAP1 or SEDT2 goes up.
So what I would suggest is that -- this is the fold change, and this is BAP1 here, and
this increasing tumor grade and increasing tumor stage. What I would suggest here is
that this analysis shows that the additional events are relatively few and they're probably
populated in a large part by these genes on the 3p. And these are probably critical drivers
for tumor progression and ultimately leading to cancer-specific mortality. And obviously,
these genes have just recently been reported the function of them other than the fact that
they're involved in chromatin modification and remodeling is relatively unknown, and
we're actively working on that -- on those components in our lab.
So basically we've shown -- we've confirmed the frequency of these novel mutations in
ccRCC. We've shown that they're associated with adverse tumor features, pathologically
and survival. Further studies with longer follow-up will be necessary to assess the
clinical impact of these additional mutations.
One thing I'd finally add is that in terms of adding to a prognostic model, which is
one of the goals of a biomarker study, because BAP1 and SEDT2 are so linked to adverse tumor
features that are already in our models, they're unable to add to a specific model. We are
currently now, and we have some exciting data which we're working through right now in terms
of mutation and copy number, to look at additional factors we can do controlling for the current
prognostic factors to add to the models: to theoretically add a new mutation, a new gene,
a new loss or gain, into the current models. Thank you very much.
[applause]
I would just like to close by thanking this really multidisciplinary group that have worked
together on this project in terms of the surgeons, computational biologists, medical oncologists,
biostatisticians, and all the members of the TCGA. Thank you very much.
Richard Gibbs: Questions? [inaudible]
Male Speaker: Nice talk, thank you. One question about your
tumor association table, where you listed of the association of the mutations in different
genes with the pathologic features, and the first thing -- that T stage -- you said a
higher stage associated with, like, three or four genes. But for the T -- I mean size,
larger than five centimeters only the last one was associated. I'm confused by this result
because if higher stage is associated with multiple genes, then when the size larger
than five centimeters, it certainly has a higher stage.
Ari Hakimi: So that phenomena we saw was really only in
the small tumor. So we saw that tumors under four centimeters were acquiring invasive characteristics
in our -- in our MSKCC cohort. And that was really looking specifically at tumors under
four centimeters. We didn't --
Male Speaker: Now --
Ari Hakimi: -- so that was -- that was the distinction
that I made --
Male Speaker: I think now I understand it. Thank you.
Female Speaker: Do you --
Male Speaker: One more over here.
Female Speaker: Do you have any information on treatment and
do you know how they impact the survival data in the -- based on the mutations?
Ari Hakimi: So that's an excellent question. As my PI
James mentioned a little earlier -- James Hsieh -- we are currently working with the
TCGA group -- the clinical TCGA group which we founded to capture -- we have about 100
patients that have gone off for targeted therapy and that's one of the first things we're going
to look at, obviously; that would be a huge factor.
Female Speaker: Thank you.
Ari Hakimi: Thank you.