Scott Carter: Good afternoon, TCGA.
My name is Scott Carter.
I'm going to be talking today about a lot of work that I did for my Ph.D. thesis, but
a lot of it is very relevant to TCGA.
So, basically, it's going to have three different parts.
I'm going to talk briefly about a method, a computational method that we developed to
infer tumor purity, pleuity [spelled phonetically], and absolute allelic copy numbers directly
from allelic copy number data, so either extemporary or sequencing.
And then I'm going to talk about some various applications of that, both to sort of design
genomic experiments and to actually interpret and hopefully learn some new biology.
So, one of the sort of caveats to a big characterization effort, like the TCGA, is that we're actually
always analyzing data generated from a population of heterogeneous cells rather than sort of
individual tumor nuclei.
So, you can see on my slide, you can think of a big tumor as having two, at least two
big populations, those of tumor cells and those of contaminating normal cells.
Now, the normal cells, typically, we think of as having a diploid genome, whereas the
tumor cells can have almost an arbitrarily complex seeming genome with very abhorrent
karyotype and very altered pleuity.
So, these two attributes dramatically affect the kinds of copy data signal that you derive
when you mash these things up and either run out the resulting DNA on a [unintelligible]
or do sequencing on those things.
So, we were motivated to develop a method that could actually sort of deconvolve these
two populations and give you integer estimates of allelic copy numbers fixed in the cancer
clone as well as to understand the tumor pleuity and the tumor itself.
So, this is a slide sort of giving an overview of the inference methodology.
So, at the top left, you can see the sort of collapsed estimates from all the individual
heterozygous markers in a given individual to give you what we call homolog specific
copy ratio or essentially allelic copy ratio that's smoothed across the genome.
So, you can see we infer two distinct copy ratio values for every locus in the genome
representing each of the two homologous chromosomes.
But, the challenge is sort of to take this as its input and to understand what the mapping
is of these sort of discrete-seeming levels in the data are to actual integer allelic
copy numbers.
So, you can see on the bottom there are sort of three distinct solutions that are essentially
mappings of those peaks you see in the data to integer allelic copy estimates.
So, on the top right of the slide, you can see that each of those three solutions corresponds
to a different combination of tumor purity and pleuity.
So, the question is to decide which of these three possibilities is the correct one, and
in general, unfortunately, it's sort of impossible to solve this problem by itself without it
bringing in some external data, because you can see sort of the blue and the green solution
fit the data equally well.
So, to break the tie or ties like this, we developed what we call a karyotype models,
which are sort of bootstrapped up from thousands of tumor samples that we analyzed and also
have some information coming in from cytological data, which are to say they're essentially
mixture models of what are recurrent cancer karyotypes, and they're specific to the tumor
type you're analyzing, to be sensitive, although that's not really necessary.
So, using these karyotype models, you can often break the score and get a nice prediction
about what the actual, true solution is without having to resort to cytological or sort of
facts-based techniques in order to understand the true pleuity value as well.
Again, one more caveat of this is that almost no tumor I've analyzed is truly clonal, and
even in terms of copy number.
So, if you can see here, if you look at the black arrows, there does seem to appear here
a subclonal gain of chromosome 2, which is explicitly modeled by our method as subclonal,
and the trick here is not to overfit these subclonal copy alterations by proposing a
much more complex karyotype than is truly warranted.
So to validate this we've done several experiments.
So, the purity validation is derived by mixing cancer and normal cell lines and then running
them out on SNP rays [spelled phonetically] and getting back the, more or less, correct
mixing fraction.
The pleuity analysis was done, actually, on some of the TCGA ovarian cancer primaries,
where we actually went and did a fax [spelled phonetically] on them to estimate pleuity,
and you can see it correlates quite well with our estimates, although there are a few samples
that seem to have been misclassified.
So, actually, before I move on, I just want to say we've been using this method at the
Broad's since, for a couple years, to select only the highest purity samples for whole
genome sequencing efforts to ensure that we have good power in order to detect alterations
in all those expensive experiments.
So, right.
So, that leads into my next point which is sort of about how purity and pleuity affect
sequencing type of experiments.
So, there's a strong relationship between purity and copy number.
I think Gaddy's talked about this a lot, where basically the higher copy number, local copy
number you are, the deeper you need to sequence to detect a mutation at one copy per cell
in that sample.
Similarly, it seems intuitive, the lower the purity of the sample is the deeper you need
to sequence in order to adequately find mutations, and those relationships are illustrated on
the slide.
Perhaps most interesting here is if you look on the far right here, so, if you wanted to
be able to detect subclonal events, say at 20 percent cell fraction, then you need to
sequence fairly deep.
But, on the other hand, these numbers that you're seeing down there are actually pretty
readily obtained with a lot of our whole exome experiments that we're doing currently.
So, now I'm going to talk about actually rescaling these estimates of allelic fraction that people
always talk about into estimates of what we call multiplicity, which is to say an estimate
of the number of mutant alleles per cancer cell.
So, this is data from the TCGA ovarian cancer, whole exome sequencing from a lumina, and
you can see on the left, we just combined all like 30,000 mutations that we detected,
and the allelic fraction distribution doesn't' seem to have a lot of structure in it.
It's kind of a smear.
And that's because there are a lot of copy number alterations in this tumor type, and,
more importantly perhaps, because they are very different purities, which totally obscure
these allele fractions.
On the other hand, using absolute, you now know what the integer allelic copy numbers
are for every position in the genome, and you know the tumor purity.
So, with that and the allelic fraction, you can rescale these raw allelic fractions into
what we call multiplicity estimates, which you're seeing on the bottom right of this
plot.
And, nicely, there's a nice peak that you see at 1.0, which is to say that the modal
sort of point estimate is about, you know, one copy of a mutant allele per cancer cell
for the clonical cases.
But, you also see an additional peak in this distribution on the left, which is colored
pink.
So, these, we surmise, are actually subclonal mutations in these tumors.
We think the distribution we're seeing here is mostly consistent with neutral evolution,
so it's sort of unlike the earlier AML data we saw today that had discrete subclones at
sort of high multiplicities or allelic fractions.
Okay.
So, this plot, I think, was important for us to just prove to ourselves that these subclonal
alterations are likely to be real.
So, the idea is that if these subclonal alterations were germ line contaminant or if they were
a machine noise, they would have a different fingerprint in the sense of a different mutant
allele spectrum, right, so the frequency of C to T transversions, or C to A, et cetera,
would be very different.
But, in fact, as you can see on the bottom left, they are very similar, which gave us
some confidence there.
In contrast, if you look at the plot comparing the mutant allele spectrum of tumor to germ
line SNPs, it looks very different.
So, this is sort of like a fingerprinting, and it shows that the subclonal and clonal
mutations in ovarian cancer have the same fingerprint.
So, the next thing we did was to try to actually see if we could use this multiplicity estimate
in order to learn something about, to classify what these mutated genes might be.
So, we took these 15 genes that we discussed in the ovarian paper, and try to understand
which were frequently homozygous, which was to say there was zero copies of the wild type
allele remaining in the cancer clone.
So, as you can see, a lot of the top genes, the top tumor suppressors, like p53 and NF1
and BRACA2 have a large proportion of their mutations rendering that gene homozygous,
so it's like there are no wild type copies left, whereas a lot of the oncogenes do not
have this property.
In addition, we were able to see that the p53 locus was often present at two or more
copies per cancer cell in ovarian cancer.
So, 60 percent of the mutations in p53 were actually amplified in this regard, and this
led us to believe that actually p53 is likely to be a very early event in these cells, since
very, very few other genes in the genome have this kind of recurrent fraction of amplified
mutant alleles.
So, now I'm going to talk about, the last thing is just inferring genome doublings in
human cancer development from these absolute copy number estimates.
So, I think genome doubling had been really widely speculated about for a long time, and
if you look at cytological data, like on the top right here, there's clearly a bimodal
distribution of pleuity, by, say fax or spectral karyotyping.
So, it stands to reason that a lot of these passage through a tetraploid intermediate,
for example.
On the bottom right, I'm showing the distribution of pleuity estimates for various different
tumor types using absolute, and you can see that it recapitulates qualitatively, the sort
of bimodal pleuity distribution.
But the additional information that you get from absolute allelic copy number data allows
you to actually look at an individual tumor sample and make a more precise determination
of whether or not it went through a genome doubling.
So, you can see here I'm visualizing both the low copy homologues on the left and the
high copy homologues on the right for the same samples.
The samples are sorted by pleuity from top to bottom, so high pleuity on the top, low
pleuity on the bottom.
You can see here this distribution recapitulates the sort of bimodal pleuity disturbiution
that you saw in all these cancers earlier, but I think more interesting is right at the
inflection point of the pleuity distribution you see a transition from low homologue copy
numbers of 0 and 1 to low homologue copies of 0 and 2.
and, similar, on the right side, you go from sort of 1 and 2 to 2 and 4.
so, this is even more precise evidence of the genome doubling, and we sort of formalize
this with a statistical simulation, sort of giving P values to this, but it's, I think
it's actually pretty obvious when you look at the allelic copy data.
So, now we can actually take, you know, a large set of 3,000 copy profiles that we've
analyzed with this method and try to characterize how common genome doublings are across human
cancer, and you can see it, on the top right, it actually varies from different cancers.
But, for example, the ovarian cancer data on TCGA, about 60 percent of the samples went
through at least one genome doubling event during their evolution, whereas things like
ALL and MPD don't usually have genome doublings, and the GBM samples from TCGA had a small
proportion, maybe 20 percent, that did genome double.
On the bottom right, I hope you can appreciate that it's not a simple sort of thresholding
by pleuity, right?
You sort of, you really want to use the allelic copy number data and make thresholds in that
space rather than from the total pleuity, because after genome doubling, you can see
that the peak of the genome doubled samples is about 3N, which means that they are, you
know, probably gains that happened, or, rather losses that occur prior to genome doubling
and I think even more losses that occur following that genome doubling event.
So, it's to speaking to that idea of which happens first, if you look at the patterns
of LOH in ovarian cancer, by chromosome arm, you see that actually, for a given arm, the
frequency of LOH in doubled samples and in non-doubled samples is nearly identical which
I think is reasonably good evidence that these antipleuities, chromosome [unintelligible]
antipleuities tend to occur early on in ovarian cancer, and specifically these LOH events.
And this is, but also amplification events as seen on the bottom here, and this is actually
a general feature of many human cancers that show this pattern.
Right.
So, these are many different human cancers that we analyze that show this pattern.
So, in general, genome doubled tumors have many more somatic copy number alterations.
This is sort of showing the sort of log-log plot of the somatic copy number length versus
their frequency, and you can see the straight line sort of fitting this power law model.
So, interestingly, the slope of the lines for each of the genome-doubled groups is very
similar, suggesting that somewhat similar mechanisms govern these things, but, like,
you have more DNA, perhaps, so that the rate of actually generating genome alterations
is higher.
So, in ovarian cancer, we are able to show that the number of mutations, so, for example,
clonal heterozygous mutations on the top increases as a function of genome doubling.
On the other hand, in the same bar plot, if you divide by pleuity, that effect totally
goes away, which is to say that the sort of mutation rate per base is probably the same
across the samples, and what you're seeing is just the effect of more DNA at risk to
be mutated.
On the other hand, clonal homozygous mutations tend to decrease as a function of genome doubling,
as you might expect.
After you double the genome, it actually becomes significantly harder to create a homozygous
either copy alteration or mutation.
And you can see on the bottom homozygous deletions in ovarian cancer, they tend to go down as
a function of genome doublings.
So, interestingly, of the 15 NF1 mutations that I, we saw in this particular set of 214
ovarian cancers, 13 of them occurred in the non-genome doubled set, in which case they
were all homozygous.
So, this sort of suggests that you're getting specific selection just on the recessive inactivation
of NF1 as expected for a tumor suppressor.
But, what's even more interesting about this is we didn't observe any amplified mutations
in NF1 in the genome doubled samples, which means that it's not that NF1 can happen early
and then become duplicated by genome doubling, as happens with p53, a sort of like NF1 mutation
seems to commit you to the non-genome doubled trajectory of evolution of ovarian cancer.
And, finally, just a few associations with the clinical data.
The patient age at diagnosis tended to increase as a function of genome doublings, which was
significant.
I'm not quite sure what that means, but it's sort of interesting, and it might have some
interesting relations to [unintelligible] biology, and there is a small but significant
association with time to recurrence and genome doubling with ovarian cancer.
So, that's it.
I'd like to thank Gaddy Getz and Matthew Meyerson and all my colleagues at the Broad, and all
the TCGA.
Thanks very much.
[applause]
Male Speaker: Thank you, Scott.
Are there questions?
Well, perhaps I'd like to start with a question.
Well, I actually had two questions, but let me start with this one.
I'm curious if you could comment on why do you see a power law between the number of
these difference in the length?
Scott Carter: Yeah.
I think there's a paper in press, I believe, currently.
It's Follenberg Godal [spelled phonetically].
I'm sorry.
I'm not going to get the name wrong.
But, I think, it's really, I think it's supposed to be related to the fractal globule three
dimensional structure of the human genome which would predict sort of the contact length
distribution would be power law distributed or closely power law.
Male Speaker: Interesting.
Okay.
And, also, my other question was can you comment on, I didn't quite understand, why is it that
the mutant allele spectrum would be different if the samples were contaminated?
Scott Carter: Right, because in general, germ line variation
always vastly exceeds somatic mutation rates, right?
So, you'd be swamped with germ line variance, and they have a very different mutation spectrum.
Male Speaker: Okay.
Thank you.
Cheers.
Okay.
Thanks again.