Juliann Shih: All right.
Hi, everybody.
My name is Juliann Shih.
I’m part of the Broad Institute’s GCC, or Genome Characterization Center.
And today I’ll be talking about somatic copy number alterations and aneuploidy events
in uveal melanoma.
So, this AWG actually started fairly recently, in October 2014.
And we’re having our first face-to-face tomorrow.
So, we’ll have a lot more integrative analysis after that.
And so, while today I will bring in some of the other platforms, this talk will mostly
focus on copy number analysis.
All right.
So, just a quick introduction to uveal melanomas.
The uvea is a layer of your eye right underneath the -- your sclera, which is the white part
of your eye.
And it consists of the iris, the ciliary body, and the choroid.
So, these tumors are rare, but they’re also the most common intra-ocular cancer and occur
at a rate of about 6 million per year.
So, what’s important to note is that they’re clinically distinct from cutaneous melanoma.
And over 70 percent of UVM patients actually develop liver metastasis as the first and
only site, despite less than 2 percent having clinically detectable metastases at time of
presentation.
These are also very molecularly distinct from cutaneous melanomas.
They don’t have BRAF mutations and instead have GNAQ and GNA11 mutations, as well as
about 80 percent of metastasizing tumors specifically having BAP1 inactivating mutations, as seen
in papers in the past.
So, currently, the most common prognostic test in the clinic is a 15-gene classifier
that -- developed by Castle Biosciences -- that predicts metastatic risk fairly accurately.
So, there has actually been quite a bit of work done in aneuploidy and uveal melanomas
in the past.
And in particular, this one paper from 2008 finds that about 50 percent of tumors have
this Chromosome 3 loss.
And you can see white denotes a loss, whereas black denotes a gain in this -- in this heat
map.
And so Chromosome 3 loss of heterozygosity is a marker for metastasis as well as poor
prognosis.
And they also found, over here, that higher-aneuploidy tumors tended to have worse survival than
those tumors with lower aneuploidy.
And so, compare that to the TCGA UVM cohort.
We have 79 tumors using -- looking at copy number with Affymetrix SNP 6 ChIPs.
And you can see that this overview looks fairly similar to what’s been seen in the past.
So, you have, you know, this -- about half of our tumors also have a loss in Chromosome
3.
And the patterns in Chromosome 6, as well as 8, also look very similar.
So, before I get back into copy number -- the copy number landscape of UVM, I’d like to
show some significantly mutated genes in this tumor type.
So, there aren’t a whole lot.
And underneath this blue line right -- underneath this blue line right here, you -- you know,
you start dropping off in terms of power.
But above that, we do have -- we do see GNAQ and GNA11 mutations in 73 out of 79 total
tumors.
And then, there’s SF3B1, EIF1AX, and BAP1 mutations.
And these have also been seen in previous whole exome studies
done on uveal melanomas.
So, I did hierarchical unsupervised clustering, using just the copy number amplifications,
as well as deletions.
And as you might expect, they separate very nicely into two main groups.
So, you have cluster one, which is on the top, which does not have loss in Chromosome
3.
And then, you have, on the bottom, cluster two or subtype two, which is defined by this
Chromosome 3 loss.
But within these two clusters, you can actually separate them further into 1A and 1B on the
top, where 1A is very, very quiet in terms of somatic copy number alterations, and 1B
is characterized by a gain in 6P as well as 8Q.
On the bottom, 2B is characterized by a very high gain in Chromosome 8Q, as well as just
overall increased aneuploidy.
So, there’s also a very striking somatic mutation pattern here.
And so, moving from -- so, moving from the left to the right, you can see that, once
again, 73 out of 79 of these tumors have either GNAQ or GNA11 mutations.
These are fairly mutually exclusive, although not entirely mutually exclusive.
And then, you have EIF1AX mutations, 10 of which are found solely in cluster 1A.
SF3B1 mutations, of which there are 18 -- 13 are found in cluster 1B.
And for -- and all 16 BAP mutations are actually found in either subtype 2A or 2B.
And this is -- this is particularly interesting because BAP1 is located on Chromosome 3, and
so all of these mutations are actually homozygous mutations.
So, here are a couple of clinical correlations to the copy number subtypes.
Epithelioid tumors, which tend to have poor prognosis and are found more commonly in subtype
2A and 2B, whereas spindle tumors are found more commonly in 1A and 1B.
Subtype 2 tumors are also -- are also found more commonly in the ciliary body as well
as the choroid, whereas subtype 1 tumors are found mostly in the choroid.
And most importantly, subtype 1A and 1B tumors have significantly better survival than 2A
and 2B tumors.
So, the difference here between 2A and 2B is, as you can see, not significant.
But there’s at least a hint of the suggestion that 2B, which is, again, characterized by
increased aneuploidy and a very high 8Q gain, may actually have worse overall survival than
cluster 2A.
So, as you can see here, there’s not much chromosomal instability in uveal melanomas
compared to a lot of other solid tumor types.
And it’s been suggested that this is because they occur in your eye, which is a, you know,
obviously very sensitive area.
And so people tend to catch these tumors early.
But also, with 79 tumors, we don’t have a whole lot of power to detect rare events.
So, these amplification peaks here are very large and actually [unintelligible] my arm-level
analyses.
But there are a couple of interesting deletions, specifically on Chromosome 3.
And so, the ones I’ve listed here are all exonic and found solely in subtype 2 tumors,
which means that they actually cause a homozygous deletion in these tumors.
And furthermore, they’re all mutually exclusive, including one additional BAP1 homozygous deletion.
But there -- unfortunately, there’s not a whole lot of literature out there about
those three genes.
Okay, so BAP 1 mutations, as I’ve mentioned before, are found predominantly in metastasizing
tumors and in our cohort are found only in subtype 2 tumors.
So germline BAP1 mutations are -- have also been previously found in families and individuals
that are predisposed to cancers such as mesothelioma and uveal melanoma.
But none of these are actually seen in our cohort.
So 12 out of 16 of the BAP1 mutations that we -- that we see are either frameshift, nonsense,
or splice site mutations.
And so, theoretically we should see a drastic decrease in BAP1 expression in these tumors.
And so, we started with a quick sanity check, looking just at the BAP1 expression between
cluster 1 and cluster 2 uveal melanomas.
And so, you can see here that the -- you know, within clusters 2A and 2B, which, again, have
BAP1 loss of heterozygosity, there’s a significant decrease in BAP1 expression.
But when we just zoom in on subtype 2 and look at the BAP1 expression by mutation status,
looking at those, you know, that are -- that are wild type or have a missense mutation,
versus the ones that should theoretically have a truncating mutation, it’s harder
to know what’s going on with these mutations.
For one, not -- you know, not all of these truncated mutations have a significantly lower
-- significantly lower BAP1 expression.
But if -- even if we choose to look just at this bottom half of the truncated mutations,
there’s still some evidence that there are some BAP1 alterations or silencing that we’re
missing in the -- in the LOH tumors.
So, we ran ABSOLUTE, which is an algorithm that infers tumor purity, ploidy, subclonality,
and whole genome doubling status, as well as generates an absolute copy number profile
for each homologous chromosome or allele.
And so, you can see here that the majority of UVMs are very high-purity, with a median
of .95.
And here I’ve plotted ploidy versus whole genome status.
And so, you know, most of these tumors are near diploid, with a medium ploidy of 2.02.
But there are five specific -- five tumors that are whole-genome-doubled.
And out of these, four of them are in subtype 2A and actually have Chromosome 3 isodisomy.
And so, here’s a quick figure showing you how this could arise.
So starting with this diploid tumor on the left, at -- for whatever reason, it loses
this one red allele, leading to a loss of heterozygosity in Chromosome 3.
And when it whole-genome-doubles, you get a -- an isodisomy of Chromosome 3 and a tetrasomy
in all these other chromosomes.
And lastly, I’d like to end on a sneak peek at what the overall landscape of the TCGA
platforms looks like in comparison with the somatic copy number alteration clusters.
And so, this is still a work in progress.
But it seems right now that methylation and messenger RNA expression correlate extremely
well with somatic -- with our clusters.
And in addition, looking at just the mutations, EIF1AX mutations are found, again, solely
in cluster 1A.
And BAP1 mutations are found solely in clusters 2A and 2B.
This is -- this is extremely striking because these clusters are determined solely on looking
at somatic copy number alterations.
And yet all of these other platforms correlate extremely well with this.
Okay, so, just a summary -- so, somatic copy number alternation clusters are consistent
with methylation and messenger RNA clusters.
And each of them is actually defined by particular somatic mutations and are very predictive
of what mutation will be -- would be found in a -- in any given tumor.
So, 1A was the most quiet in terms of copy number alternations.
They also have better survival and tend to have EIF1AX mutations.
1B was characterized by a gain both in 6P as well as 8Q gain, had better survival, as
well as SF3B1 mutations.
Cluster 2A was characterized by loss of heterozygosity in Chromosome 3, worse survival, and BAP1
mutations, specifically truncating mutations.
And cluster 2B also had loss of heterozygosity in Chromosome 3 but are additionally characterized
by high 8Q gain, as well as increased aneuploidy.
And this seems to have the worst survival out of the four clusters and tend to be also
BAP1-mutated.
Okay, so, what does this ultimately mean?
So, we know now that copy number subtype 1 and 2 look extremely different in multiple
platforms, including methylation and messenger RNA.
And we also know that Chromosome 3 loss of heterozygosity is highly predictive of metastasis
and worse overall survival.
And so, perhaps, in the clinic, copy number subtype 1 and subtype 2 patients could be
actually treated differently.
Going forward, this AWG will be integrating all of the different platforms into one cohesive
story about uveal melanoma that hopefully will have -- will impact patient care in the
future.
So, I’d like to thank Andy Cherniack for working with me on copy number analysis; Julian
Hess, who’s also at the Broad, for mutation analysis; and [unintelligible] for leading
the group, as well as the rest of the UVM analysis working group.
All right.
Thank you.
[applause]
Male Speaker: Hi, very nice talk.
Two questions.
So, number one, EIF1AX mutation seems sort of mutually exclusive to the SF3B1.
And one is initiation -- involving translation initiation when it’s splicing.
Do you know the -- any cross-talk?
Seems like mutually exclusive to me in terms of [unintelligible] distribution.
Juliann Shih: Right.
I’m not actually familiar with the biology behind this.
But yes, they are -- they are mutually exclusive, with one exception.
And they’re all found in the non-monosome A3 tumors.
Male Speaker: Yeah.
And the other thing is, it’s three mutation -- Chromosome 3 -- like clear cell kidney
cancer, there are multiple tumor suppressors lost in addition to BAP1, including VHL, PBRM1,
and SETD2.
You didn’t see those mutation in your cohort, right?
Juliann Shih: Right.
We didn’t see them significantly mutated.
It’s unclear -- I haven’t checked if there are, you know, just one-off mutations.
But we should take a look at that.
Male Speaker: Yeah, because when we saw a subset of clear
cell patients that had SETD2 mutation, and some of them had SF3B mutation, they’re
sort of mutually exclusive, so almost very similar to here.
So I don’t know whether there’s any correlation.
Male Speaker: I might have -- I might have missed this,
but can you explain a little -- a little bit more about the cohort?
Were they all metastatic samples that you were analyzing?
Juliann Shih: So, again, it’s difficult to tell because
within -- in the clinical data, there -- so, as I mentioned, it’s difficult to determine
whether a tumor is eventually going to be metastatic at time of presentation.
And so in, you know, the clinical annotation file, there are only about two or three metastatic
patients.
But you do see some of these patients eventually go on to die of metastatic uveal melanoma.
So, it’s kind of unclear which of these patients are actually metastatic.
Male Speaker: So, have you compared your classification
versus the gene signature that’s currently in the clinic?
And does it do better?
Juliann Shih: So, I haven’t checked that.
But the mRNA -- the mRNA group, I believe, has checked that.
And it looks pretty similar to what they see.
And so, you know, look -- just looking at the third bar to -- from the -- from the right,
those, you know, overlap very well with the somatic copy number alteration clusters.
Male Speaker: And finally, you mentioned that GNAQ and GNA11
-- there were a few co-occurring mutations.
Did you notice anything with regards to the type of mutations?
Were they the non-hot-spot mutations, or did those patients do worse, do you know?
Juliann Shih: I don’t know that off the top of my head.
Female Speaker: Thank you, Juliann.
I’m going to introduce our last speaker for this morning, Nicolas Stransky, and -- from
Blueprint Medicine.
He’s going to talk about the landscape of driver kinases -- driver kinase fusions in
cancer.
So, a couple of announcement.
We are going to have lunch after this talk.
And the poster viewing will start at 1:30.
We will come back to this room at hopefully 2:25.
[end of transcript]