Ian Watson: So, I just want to thank the organizers for
giving me an opportunity to speak today, and hopefully my talk warms you guys up in this
chilly auditorium.
[laughter]
So, my name is Ian Watson. I'm a postdoctoral fellow in Dr. Lynda Chin's lab, helping lead
the Melanoma TCGA Analysis, which is co-chaired by Dr. Lynda Chin and Jeffrey Gershenwald.
And I just wanted to put up all the author -- or all the people responsible for the different
platforms. This is quite a -- like many TCGA projects, a very collaborative effort. In
particular, I want to point out the efforts from Lihua Zou and Terrence Wu, who are data
and analysis coordinators. And also thank the Firehose Team. Our point people have been
Dan Dicara and Mike Noble. For all of the custom adjustments they've made to Firehose,
given that the Melanoma TCGA Project is different from every other TCGA project to date, in
that over 80 percent of our samples are from metastatic origins.
They originate from non-glabrous skin primaries; so that's the hair bearing parts of your skin.
We've excluded samples from the palms or the soles, and -- or sorry, the palms of the hands
and soles of the feet, as we know that they have different genetics from past studies.
Another criteria was no prior systemic treatment, and the rationale for this is that with primaries,
they're very small -- they tend to be nevi -- so you don't get a lot of tissue in order
to carry out all of the different analysis for the TCGA project. And most of the samples
goes for diagnostics. Also, if discovered early, melanoma is highly curable; however
the survival rate decreases significantly for regional disease. And what we're studying
here is the most common first sight of metastatic melanoma. So the majority of our samples are
from the regional lymph nodes, as well as regional skin and soft tissue. We also do
have a good number of primary and distant metastatic samples, which will allow for some
unique TCGA analysis in the future; however, I should note that there are only two cases
with match primary and metastatic samples. We stopped the data freeze at Batch 291. We
have up to 330 samples for a given platform and the rate limiting platforms for the integrative
analysis, like many TCGA studies are the RPPA [spelled phonetically] and low pass whole
genome sequencing.
So we know that melanoma has the highest mutation rate of any cancer today, and UV plays a significant
role, as demonstrated this nice figure from Mike Lawrence and Gad Getz, showing the comparison
of the mutational rates across all cancers. We confirmed this result in that we observed
a mutation rate of 17 mutations per mega-base, which I think is the highest reported for
any TCGA cancer today. The majority of the mutations are C to T transitions at di-pyrimidines,
which is consistent with the role of UV-induced DNA damage and mis-repair. We are left with
a problem of identifying driver mutations when we've identified over 200,000 single
nucleotide variants in our up to 300 samples.
So, how are we going to address this? Well, first we turn to MutSig, and we identified
42 significantly mutated genes with a Q value of less than 0.1. We also used InVEx, which
is a statistical tool, which also takes in consideration intronic mutations to try to
determine a gene's specific background mutation rate, and has been used in past studies at
the Broad Institute in collaboration with Lynda Chin, Levi Garraway, a talented computational
biologist, Eran Hodis, and myself in a previous analysis of exome sequencing data for melanoma,
and was used in a really nice study by Matthew Meyerson in another high mutation rate cancer.
So, what do our significantly mutated genes look like? We identified 13 significantly
mutated genes by InVEx, and these 13 genes were also found on the MutSig list. What gives
us confidence in this list is we identify the known melanoma drivers, such as BRAF,
NRAS, CDKN2A, P53, and PTEN. We also identified significantly mutated genes that have been
found in a few exome sequencing studies, including PPP6C, ARID2, MAP2K1 and RAC1. We also identified
for the first time to be significantly mutated melanoma-associated genes NF1, IDH1, and RB1.
So, they've been implicated in melanoma, but here we find for the first time to be significantly
mutated given our large sample set. We identified a novel gene, DDX3X, which is a RNA helicase.
So, this is our landscape figure. You can see the mutation rate at the top. The samples
are indicated in the columns. We see the mutation spectrum below, and as you can see, the majority
of the samples do have a high fraction of C to T transition, indicative of a role of
UV. One thing that's clear from the landscape is that we do see a high fraction of BRAF
hotspot mutations, which is known. A majority of them occur at the V600 residue. These are
mutually exclusive with hotspot mutations in NRAS, and the majority of them occur at
the Q61 residue. So, one of the major questions in the field is what is driving these BRAF/NRAS
wild type samples. And, for the first time, we've sequenced enough samples to really get
an idea of the landscape of mutation in this subset. One thing I want you to notice is
that these BRAF/NRAS wild type have a higher fraction of samples that lack the UV signature,
and in the samples that have the UV signature, we see an accumulation of these NF1 loss of
function mutations. So, the BRAF hotspot and NRAS mutations are significantly anti-correlated,
but also the BRAF hotspot and NF1 mutations are significantly anti-correlated. So, we
only observe one co-occurring loss of function NF1 mutation with a hotspot BRAF mutation.
So, what do these NF1 mutations look like? Over 50 percent of the mutations are loss
of function mutations, either nonsense, splice site, or frame shift deletions. When the NF1
mutations and the BRAF mutations do co-occur, they tend to co-occur with these exon 11 BRAF
mutations, which are weaker kinase activating mutations. Based on the relationship of these
MAP kinase driver mutations, we suggest that melanoma can be categorized into four genetic
subtypes, which include the BRAF hotspot mutations; the RAS hotspot mutations, as we do identify
a few H and KRAS hotspot mutations that are anti-correlated with the NRAS mutations; NF1
mutations, which tend to be loss of function mutations; and these triple wild type samples.
So, what's driving these triple wild type samples? So, by incorporating other data platforms,
it becomes more clear. So, again, based on our sample size, we're now able to do this
type of analysis, where we perform GISTIC2 analysis on the different subtypes. So, we
know MITF, which is a lineage-specific oncogene, is significantly amplified in melanoma, but
we only find this significant application in the BRAF hotspot mutant samples. And same
with the BRAF amplicon, we only find that in the BRAF hot spot mutants. When we look
at the NRAS hot spot mutants, Andy Cherniack identified a minimal common region that also
includes NRAS. And we see in the triple wild type, is you see this 4q12 amplicon, which
is only found to be significant in the triple wild type, and that contains KIT, KDR, which
is also known as VEGF receptors 2, and PDGR alpha. We also observed significant applications
in TERT, cyclin D1, and this 12q15 region often includes MDM2 and CDK4.
So, when we include these copy number alterations in the landscape, we start to fill out what's
driving this particular subtype of melanoma as we can see, the different significant amplications.
We also have to observe some cosmic mutations in known melanoma drivers, such as GNAQ, GNA11,
and KIT. And this is just another representation of the distribution of these amplicons. And
you can see that they're enriched in the triple wild type. We also performed fusion analysis
in a collaboration with the Harvard Medical School, Brigham Women's, and MD Anderson Cancer
Genome Characterization Center, where we performed low-pass whole genome sequencing. We also
incorporated deep whole genome sequencing, as well as RNASeq, using multiple callers.
I won't go into this entire pipeline, but on poster number 63, there's a description
of this entire approach.
We identified 221 potential drivers, and I'm just going to highlight two. So these are
the BRAF fusions that were identified. Recently in the literature, they've been estimated
to occur in a relatively high fraction; however, we only see them occur in about -- or we only
find two of them within all of our samples. What's interesting is the -- is one of the
fusions was only -- was found in the triple wild type, and the other one we don't have
overlapping exome data.
So, these are MAP kinase driver mutations. They have similar signaling outputs. We looked
at downstream signaling, so Fos-ful-MEK [spelled phonetically] and interestingly, we only saw
a comparatively elevated levels of Fos-ful-MEK in the BRAF and NRAS mutant samples. And,
only comparatively elevated levels of Fos-ful-ERK [spelled phonetically] in the NRAS mutant
samples, and we're currently trying to decipher some of the signaling pathways involved in
this through our integrative analysis.
So, what are some of the other interesting mutations we're identifying? So, we identified
this hotspot IDH1 mutations, and unlike the GBM mutation, it's not the R132H; it's the
R132C mutation, which is caused by C to T transitions; so presumably UV damage. The
Johns Hopkins group performed clustering analysis of our methylation data, and they've identified
this high SMP group. And interestingly, the IDH1 mutations clustered with this high SMP
group, as well as AR-2. This high SMP group also had less BRAF mutations. And, one of
the issues with our clustering analysis is, because we have metastatic samples from all
different origins, often they are a reflection in terms of our clustering subgroups of the
origin of where the tissue was procured; however, in this particular analysis we did not find
that relationship.
Another thing that's interesting about this is the GBM high SMP group is often associated
with -- and the IDH1 mutations are often associated with better survival. In our analysis they're
trending towards worse survival. One of the unique aspects of the TCGA project too is
how we do our survival analysis. Because we are acquiring the metastatic sample and not
the primary sample, we are doing both overall survival, as well as this TCGA survival, and
this is from when the sample is procured to the days to last follow-up and death. So,
this is how we're doing our survival analysis for the metastatic samples.
When we performed the RNASeq group, our RNASeq clustering analysis, we identified three subgroups,
but as I mentioned before, they are often a reflection of the sample where they were
procured. So, for example, we identified this lymphocytic immune signature score, or subgroup,
which was found to be enriched in the regional lymph nodes. We also identified a keratin
or epithelioid cluster, which was enriched in the primary samples, as well as the subgroup
that was identified, or that was distinguished by low MITF and low MITF signaling, which
is a lineage-specific oncogene. Interestingly though, the high lymphocytic group or immune
group, actually had the best survival, and this is consistent with other studies that
are -- been found in both the many different tumor types, as well as melanoma. And this
is in particular important, given that the immune therapies have had a major impact for
metastatic patients.
So, we dug deeper into this, and we had our pathology team, which was led by Richard Scolyer,
actually look at the samples and score for lymphocytic infiltration. So they're scored
from low to high, and you can see that there is distribution across the samples of low
to high, and even within the lymph nodes, there are samples with low lymphocytic infiltration.
And when you look at the survival, you do see that the patients with samples that have
high lymphocytic infiltration do better. And even when you just consider this -- only the
regional lymph node samples -- and again, this is important in the context of the new
immune checkpoint inhibitors, targeted therapies for the immune system and melanoma.
One of the other important findings that have recently come out of the literature in terms
of melanoma genomics is these TERT promoter mutations that Matthew was asking about yesterday.
So we do not capture the TERT promoter mutation, obviously, in our exome sequencing data; however,
the Harvard Brigham Women's, MD Anderson Cancer Center -- Genome Characterization Center performed
the PCR and Sanger sequencing on the over 100 samples, in which they also did low pass
whole genome sequencing, and we observed that the TERT promoter mutations occur at a similar
frequency that was reported in literature -- about 60 to 65 percent -- and that these
two mutations were mutually exclusive. The -- going back to Matthew's question yesterday,
we found only that the C228T mutation was associated with increased expression; however,
again, we're only looking at 100 or so samples. Another preliminary analysis that we did observe
was that the metastatic samples tended to have a higher fraction of samples that had
this TERT promoter mutation.
Here's just a high level overview of the pathways altered in melanoma. This was performed by
the Memorial Sloan-Kettering Group, and Chris Sander and Anil. And what we're demonstrating
here is that, in this and in our studies, this new MAPK driver melanoma, NF1, found
to be significantly mutated. These are the four genetic subtypes in -- as I pointed out
through the course of the presentation, we're finding certain driver events that are found
in certain subtypes. So, for example, the triple wild type driven by MDM2 applications,
cyclin D1, CDK4, TERT, and KIT. We also identified some significantly mutated genes that are
associated with particular drivers that I didn't get a chance to go into, which include
PP6C, for example, for the BRAF and NRAS mutants. And this is becoming clearer in our OncoSign
analysis, which I don't have time to show you today.
So, in summary, we've identified four genetically distinct melanoma subgroups, which include
the BRAF hotspot, RAS hotspot, NF1 loss of function, and triple wild type. Integrating
these platforms, we show that they do not signal similarly throughout the MAP kinase
pathway, and so each of these mutations does have different MAP kinase signaling. We also
identified the IDH1 hotspot mutation, and it clusters with the high SMP group. In some
of the analysis that unfortunately I wasn't able to show you today, was the incorporation
of OncoSign, the microRNA clustering analysis, as well, some of the primary versus metastatic
comparative analysis that we've done, as well as one of the main aspects of our project
that we're kind of continuing with is the genetic determinants of this lymphocytic infiltration.
So on that note, I just want to thank everyone on the manuscript writing committee that is
involved in trying to put together this marker paper. I want to thank the Firehose -- the
Broad Firehose team that have helped with all of the platform-specific analysis. And
as of April, this is all of the authors contributing to the project. So, thank you for your time,
and thank you to the organizers for giving me a chance to speak today.
[applause]
Peter Laird: We have time for a couple questions.
Male Speaker: Yeah, that triple wild type group is very
interesting. Apparently, the mutation rate is lower in that group. So, are they associated
with lower lymphocytic infiltration, and that will infer that -- because most of these are
the UV damage and the lung cancer, they are generally neo-antigens.
Ian Watson: Yes.
Male Speaker: So, I am wondering whether there's any correlation?
The other question is -- for example, that triple wild type apparently is using different
set of RTKs. And those are KIT, PDGF. Those likely would respond to [unintelligible] kinds
of RDK drugs. Do you have any data for that?
Ian Watson: I don't have any data for that, and I know
people at MD Anderson and a few other groups are looking into that. In terms of your questions
about the lymphocytic infiltration and triple wild type, I do not think that there was an
association in that case. I think it is due to the fact that they have a lower mutation
rate, but that is a good question, and I'll look into that and get back to you.
Male Speaker: Regarding the triple wild type, since they
don't seem to have a UV signature, did you look for some other type of signatures in
those samples? And did you look also in the -- maybe in the germline to find out DNA repair
genes would be affected?
Ian Watson: That's a great question. We are doing those
analyses, and we have looked at the mutation signatures that Michael Stratton identified,
and we also looked at chromothrisis, and depending on the chromothrisis color you use, we did
see some enrichment in the triple wild type samples; however, these seem to be complex
rearrangements, not your classical chromeothrisis. So, that is one potential aspect of what's
happening in these triple wild type samples in terms of genetic signatures.
Great, thank you.
Peter Laird: Thanks very much. Next, we'll here from Rehan
Akbani from MD Anderson on "The Pan-Cancer Proteomic Landscape of the Cancer Genome Atlas
Projects."
[end of transcript]