Ben Raphael: All right, well, thank you.
Thanks for the opportunity to present our work today.
So, one of the first analysis challenges when you sequence a new cohort of cancer patients
is to identify significantly mutated genes that are mutated in more patients than you
would expect by chance.
This is not such an easy task.
There's many subtleties.
A number of individuals in this room had developed very sophisticated methods for doing the statistical
test appropriately on a gene-by-gene basis and then applied these tests to TCGA and other
large datasets, thereby identifying a number of significantly mutated genes, and I put
a list of a few of the more recent studies from TCGA.
And while these genes seem to be enriched for cancer-causing genes, these do not seem
to be the complete list of cancer genes in these samples.
These lists are relatively short, and what I'm not showing you is the long tail of genes
that are mutated at, you know, modest or rare frequencies but don't pass this threshold
of statistical significance that one would use.
Now, there's many reasons for this mutational heterogeneity, but one of them is that genes
don't act on their own but rather act together in pathways, and when you look at the mutations
in known pathways, you indeed see such clustering of mutations in pathways.
Again, showing you some examples from some of the TCGA studies, and thereby showing that
we can recover some of the cancer-causing genes by knowing something about the biology
and looking at how they interact with other genes.
But as the datasets get larger, my group has been interested in -- they're developing ideas
that allow us to drill a little deeper into the data, not restrict ourselves to only known
pathways but rather to sort of reduce the amount of prior knowledge we put into the
analysis, thereby possibly identifying novel pathways or crosstalk between pathways, or
perhaps use something about the topology of these interactions.
So, of course, as we reduce the amount of prior knowledge and perhaps go to, you know,
a noisy whole genome interaction network or even try to look at all combinations of genes
and whether or not they're significantly mutated, we're going to face this statistical problem
of increasing the number of hypotheses, so it would be very hard to obtain statistical
significance if we couldn't even do it for a single gene.
So we've developed a couple of algorithms that allow us to use some additional information
and some tricks to kind -- to get the statistical robustness.
First, you looking at subnetworks of interaction network, an algorithm called HotNet, or alternatively,
going sort of deeper into this "no prior knowledge," but we use this notion of mutual exclusivity.
I'm just going to tell you today about HotNet, and to just briefly recap what the goal is.
So, we assume that we've taken our mutation data, collapsed it at the level of genes.
We have a whole genome interaction network in a very simplistic format here where we've
got genes or proteins and an edge between two genes.
If there's some known interaction between them, we could, of course, use varying amounts
of prior knowledge in constructing such a network.
I'll tell you the ones we used in the analysis in a couple of minutes.
And so the goal is to then identify connected subnetworks that are mutated in more patients
than we expect.
So you can see how this sort of generalizes, a little bit, this idea of looking at known
pathways, where if we think of this network as say, the superposition of all known pathways
then we're sort of simultaneously trying to pick apart these pathways but allow for crosstalk
or interactions between them, or perhaps novel subnetworks that we don't a priori.
So, in developing such a method, there're really two components, and there's this interaction
between these two ideas that needs to be dealt with.
One is that, you know, genes can be significant on their own.
They have, you know, a frequency mutation or perhaps some score, maybe some significant
score that you use, as well as the topology of the interactions between them.
So we'd like to identify, for example, cases where the individual genes might not be significant
on their own -- they're mutated at moderate or rare frequencies -- but they're very highly
connected, and perhaps distinguish those from cases where we do have genes that are, you
know, connected to each other.
Maybe they're even highly mutated or highly significant but that connection is through,
perhaps, some high degree node, which isn't so surprising, then, that we would find such
a connection.
And biological networks have this sort of uneven topology.
So, we need to simultaneously account for both mutation frequency or score and the network
topology.
So one way we can model this -- has this nice physical interaction that I can describe as
thinking of the mutations as sources of heat on the graph.
So the idea here is that we have this interaction network.
We heat up the genes in proportion to their frequency, or their mutation score, or some
notion of significance at the gene level -- here, you can use, you know, a variety of different
scores -- and then that encodes the mutation part.
And then to encode the topology allows that heat to defuse over the edges.
So this encodes both a local topology, not just nearest neighbors but neighbors of neighbors,
and it does this in a very continuous way.
So now we have this distribution of heat on the graph, so how do we get the subnetworks?
Well, we need to break it apart, and the way we break it is by removing cold edges, and
then we have to do a statistical test on top of that, which I won't describe but has been
published previously.
So that's HotNet.
And in this study, what we wanted to do was apply HotNet in the pan-cancer setting; so
to go across multiple cancer types.
And the idea here was that what we would like to find in doing so is, perhaps, you know,
universal subnetworks that were mutated across all cancer types, maybe sort of with equal
frequency somehow, or perhaps ones that were mutated only a subset of cancer types, or
maybe even cases where, you know, the subnetwork itself has mutated across all cancer types
but the individual genes within that subnetwork are mutated with some cancer type-specific
biases.
Okay, so, we didn't know which, if any, of these things would arise, and so we decided
to go and look.
And the story I'd like to tell you for the next six minutes or so is that we took a very
noisy interaction network and we took all the mutation and copy number data, threw it
into HotNet, and magically we got the right subnetworks.
Unfortunately, although the algorithm is nice, it doesn't yet perform magic.
We can't take all mutation data and copy number data.
There's just too much noise there, so we have to do a little bit of filtering.
We try to do as little as possible.
So for single nucleotide variants, we don't have to restrict to just significantly mutated
genes.
We can go really far into this distribution, if you will.
Here, we used a cutoff of only .8 percent frequency of mutation, and that reduced some,
you know, really low frequency mutations.
Copy number aberrations, of course, are much, much trickier because they're -- tend to be
large, they include many genes, and really identifying the target gene is a really difficult
challenge.
And we ended up using GISTIC max peaks that were predicted for the individual cancer types,
and then merged those together in a pan-cancer analysis.
The interaction network, though, we are able to use sort of a very noisy interaction network,
as I'll show you.
So with some modest filtering on the mutations, here's what we obtained.
So, in total, 1,984 samples across nine different cancer types here.
Colorectal, I've combined as one.
Breast cancer, I've split into four expression subtypes so there's a number of samples of
each type -- and 765 genes after we removed these low frequency mutated genes.
You'll see, you know, some of the usual suspects have either high SNP -- single nucleotide
frequency or high copy number frequency there.
But then there's also sort of this big schmear of genes that, you know, are very low frequency.
So the first interaction network is a very noisy one.
It's this iRefIndex.
200,000 interactions, over about 14,000 proteins.
This sort of incorporates -- there's a lot of different interaction databases so it has
a lot of, you know, false positives; presumably also, a lot of false negatives.
Note that there's no sort of temporal context here or subtype context so this really is,
you know, a very noisy thing, but we were able to pull out 11 subnetworks with at least
three genes, and the P-value is at least 10 to the -2.
We haven't sort of finished all the permutation tests, presumably much lower than that.
So, here's a list of these subnetworks, and I'm going to show you a few in the next few
slides but, you know, not surprisingly, you know, some of the usual suspects again.
P53 signaling, PIC3CA, EGFR and other receptors, RB1 -- this is a DNA damaged subnetwork that
I'll show you -- P10 cohesin, I'll show you in a second as well as a few others.
So, just to sort of show you some the data.
It's sort of a very dense plot so here's the genes, their frequencies across the different
subtypes, color-coded by subtype, as well as whether it's a single nucleotide variant
or an amplification in that sample.
This is a PIC3CA and RAS.
What we also do is we sort of compute whether there's enrichment for individual cancer types
in this subnetwork.
These P-values, you should take with a grain of salt because we have thrown out some of
the mutations.
We filtered the mutations, but it does show at least some trend of maybe some cancer-specific
biases in these subnetworks.
And then we also do see whether these conditional -- on the number of mutations in this cancer
type, whether there's any gene-specific biases.
Here, you see that NRAS is sort of preferentially mutated in AML, while KRAS is preferentially
mutated in colorectal.
The Luminal A subtype of breast cancer with a PIC3CA mutation.
I wanted to also note that, you know, there is a strong pattern of mutual exclusivity
but there's also co-occurrence that's allowed here, and, in fact, although PIC3CA and the
RAS genes are often thought to be mutually exclusive, there is some evidence that they
do co-occur in particular samples and we see that here in endometrial and colorectal.
Here's this DNA repair network.
Same type of story here centered on BRCA1.
These two genes here are actually Fanconi anemia genes, which are genes that infer a
genetic predisposition to this DNA instability disease, so it's sort of nice to find them
here.
Very rarely mutated, would not be sort of found on their own and seem to be across cancer
types, although there is some enrichment for breast cancer and ovarian for this subnetwork.
Here's cohesin which falls out, and, in fact, we get -- this subnetwork is exactly the cohesin
genes and no more.
Five, so this is the canonical cohesin complex involved in sister chromatid adhesion as well
as more recently seems to be implicated in more general gene expression roles.
We see, again, some gene-specific biases for -- in AML and a little bit in GBM for individual
parts of the cohesin complex, and perhaps some cancer-specific biases in different breast
cancer subtypes for this complex.
Another one, really fast, these Polycomb group genes.
This subnetwork has a few sort of strange things that are driven by copy number but
what we do see here is this -- two components of this PR-DUB complex involving BAP1 and
ASXL1.
Gene-specific -- little bit of gene specific tendencies for mutation in ASXL1.
BAP1, of course, recently shown to be an important gene in renal cell carcinoma, so not surprisingly,
there is a kidney cancer enrichment for this subnetwork.
We then went and did another run on an additional interaction network.
This one is a little more focused, tends to be literature-curated interactions, so a smaller
number of interactions here, only about 40,000.
We pull out a larger number of subnetworks, 20 with at least three genes.
Again, a good P-value.
Here's the list of those.
Many of them are the same or similar, not -- you know, exactly the same but similar
type of story.
Here's one with ARID1A and PBRM1 that I didn't note before.
This is actually -- a version of this is currently in Couric and this was identified mostly in
kidney cancer.
NOTCH was in the OV manuscript identified by HotNet.
And here's a nice one that's new.
SLIT/ROBO signaling.
Here it is.
We see, again, most of the SLIT.
We see both the SLIT lig and the ROBO receptor and this SrGAP1, the Rho GTPase, so we're
getting sort of the complete part of this signaling network.
Not too much enrichment here across cancer type.
Seems to be, sort of, across different types of cancers except for AML.
Hardly anything in AML in SLIT/ROBO.
Interestingly, we were, sort of, doing this analysis a couple months ago without knowledge
of a paper that, of course, many of you now know; some in the audience contributed to
this paper that came out in Nature just two weeks ago that highlights SLIT/ROBO signaling
as important in pancreatic cancer.
Again, some of these same genes, although also including ROBO3 in that analysis.
So, we're pulling this out of the TCGA data types.
Again, purely computationally, not in any directed fashion, and so we sort of view this
as, in some sense, a validation of our computational predictions.
So with that, I'll conclude, and I'll just say that, you know, in summary, we took HotNet,
which had been previously applied on individual cancer types.
We applied it in this pan-cancer setting.
We uncovered some evidence for, perhaps, you know, these individual types of things that
could happen.
A little bit of gene specificity.
Nothing, of course, this dramatic, where the gene is exclusive to a cancer type, but perhaps
some such tendencies.
Of course, this is only a first, you know, analysis.
The mutation data needs a lot more QC.
We're still struggling a little bit with copy number aberrations and how to do those.
Get better background models for the single nucleotide variants.
We haven't done anything with expression and, of course, it's important to incorporate expression.
Methylation, we'd also love to incorporate as well.
But, you know, each of these data types requires itself some analysis.
You know, you can't just all go into the algorithm and pull out the magic.
Dendrix, I didn't describe for mutual exclusivity so that, you know, will be something to come.
I'll conclude with the acknowledgements.
My group, Fabio Vandin, Eli Upfal, all of my colleagues contributed a lot to the algorithmic
developments.
And my students, Max Lesierson will have been doing a lot of the analysis.
For the AML data, we've been working a lot with the AML group at Wash U, and I didn't
describe the AML results in particular, but that curated data went into this analysis.
The algorithms are available.
We encourage you to download and use them if you'd like.
We'd also be happy to collaborate and work with you in tuning them to your analyses.
And with that, I'll conclude.
[applause
Raju Kucherlapati: Thank you.
Thank you, Ben.
Questions?
Matthew.
Matthew Meyerson: Sure.
So, Ben.
Really fascinating talk.
I think one of the really interesting things is, you know, this sort of novel generation
of new candidate pathways, which looks like it's, you know, corresponding in some cases
to, you know, other recent studies.
And I just wonder if you have a vision of how to move from definition to validation
of these pathways through orthogonal datasets, which would both, you know, bring more power
to the approach and also allow you to refine it.
Ben Raphael: I mean, in brief, I get asked that question
-- I don't have a good answer.
I'm not an experimentalist.
We love to validate some of our predictions.
We've talked to individuals about ways we might do that.
It's a tricky business because, you know, there's all sorts of crosstalk between these,
and we pick out one component, and the experiment that you would do that would say it's somehow
the pathway or the subnetwork that's important as opposed to the individual genes is a challenge.
So, likely -- I mean, you, I'm sure, could give a better answer than I could for how
to do this, and I'd love to, sort of, talk to you about it more offline.
Matthew Meyerson: If I had a better answer, I wouldn't ask you
this question.
[laughter]
Raju Kucherlapati: One more.
Female Speaker: Yeah.
Computationally speaking, I think this is very nice model.
It's very similar to the -- like, when they -- when people analyzes gene-regulated network
across different species and also, like, protein-protein action network.
So, the -- regarding to the model -- on the graph model side, so, since this will be handling
very large graph, right, so very large, huge graph, I think a specific graph property,
like related to small degree or even, like, [unintelligible] approach could be applied
to kind of improve the efficiency side.
Ben Raphael: Sure.
So, yes, the graph is large -- it's really not that large when you talk to people that
do, you know, Internet graphs and stuff.
This heat diffusion encodes a lot of the types of things you're talking about in terms of
small degree, and, you know -- Small-world's parallel, all that type of business.
It's -- you know, in many of the approaches that look at those type of graph topologies
can be modeled in terms of the heat diffusion or random walk.
I mean, they're really just different ways of looking at the same types of things.
Female Speaker: Yeah.
Especially for the small server networkers like [unintelligible] approach.
It would be very --
Ben Raphael: Yeah, yeah.
Now, I mean, there is some issue of scaling that we're running into, that as we make the
-- as the datasets grow, so that the difference between the highly mutated genes, like p53,
and the low mutated genes, that, you know, difference gets larger and larger then, you
know, there do become some issues in how you deal with that sort of difference in scale.
ASnd that's something that's come out of this pan-cancer analysis that we hadn't really
had to address before when we had 200 samples.
Female Speaker: Okay.
Yeah.
Thank you.
Raju Kucherlapati: Ben, thank you.
I think, unfortunately, we have to move on.