Angela Brooks: Okay.
Good morning.
Thanks to the organizers for allow me to speak about this project.
Oh, let me go over here.
So I‘m postdoc in Matthew Myerson’s lab, and I’m going to talk about a project that
I’m co-leading with Alice Berger and Xiaoyun Wu.
I’m also in collaboration in Jesse Boehm's lab.
So, recent cancer sequencing projects has done a really great job of identifying many
novel cancer genes.
However, we’re still underpowered to detect all of the functionally relevant genes as
described in a paper by Mike Lawrence and Gaddy Getz.
And -- whoops -- what’s shown here, each of these dots show the mutation frequency
of each of these tumor types and how many samples have been sequenced.
And so, for many of these cancer types, we’re very underpowered to detect the relevantly
-- the functional mutations in these genes, particularly those that occur at a very low
rate.
It’s even more of a problem when we’re trying to identify the specific variant in
a gene that is functionally relevant.
And so, this is an example of EGFR, which is a very well-characterized cancer gene.
It has many hot spot mutations, many clinically-relevant mutations, but what’s shown here are all
the mutations that’s been observed across many tumor types that have been sequenced.
And there are many mutations that occur in only one patient.
So the question is, are these also functionally relevant?
Are they also doing this to more function as these hot spot are very well-characterized
mutations?
And so, with Jesse Boehm, we’ve been thinking about, how do we experimentally, in a high
throughput way, functionally test the function of many of these mutations?
And so, given all the somatic variants we’re identifying from these cancer genome studies,
we can create specific reagents [spelled phonetically] to test their function, and then put these
reagents through a host of assays, some of them being very pathway specific.
But also, we’re interested in gene agnostic assays.
So if you have no idea what the function of this gene is or what pathway it’s involved
in, we would like these types of assays to give us a more, broader picture of the function
of these mutations.
And so, to test this approach, we decided to look at mutations among adenocarcinoma.
Not only is it one of the leading causes of cancer deaths worldwide, but it also has a
very high mutation rate, and so identifying those mutations among adenocarcinoma that
are functionally relevant above the background mutation rate, just purely computationally,
is a challenge.
So our approach was to take mutations, observed in adenocarcinoma, from two papers, including
one from the TCGA project, and create an ORF library of both the wild type versions of
47 genes, as well as missense and indo variants that were observed in these genomes.
And from this ORF library, we infected A549 lung cancer cells, and in multiple replicates,
and then after 96 hours, ACE gene expression [spelled phonetically], using this approach
called L1000, which is a reduced representation of the transcriptome.
It only profiles 1,000 transcripts, but the benefit is that it’s a very low cost, and
so since we’re asking many, many variants in high replicate, that helps with the cost.
So how are we predicting the functional impact of these mutations?
We are taking each replicate introduction, and here I’m showing a heat map showing
the correlation or how similar the gene expression changes are between multiple replicates of
a wild type worth of this gene, ARAF.
And so you can see in the heat map, mostly red, meaning that the gene expression changes
are very similar.
When expressing this mutant version, V145L, also very consistent gene expression changes.
And then when comparing the mutant in wild type replicates together, also very similar
gene expression changes.
So we predict that maybe the mutation isn’t doing anything to the function of the gene
since the gene expression changes are similar.
In contrast, another mutation in ARAF, this S214F, the mutant ORF version also gives a
very strong consistent gene expression signature.
However, when comparing the mutant in wild type gene expression changes, they’re very
different.
And so in this case, we would predict that this mutation has a functional impact.
So, for each mutation, what we saw is that these three sort of heat maps can be represented
as distributions.
And so using a Kruskal-Wallis test, we can then ask, does there appear to be a change
in the distributions, and using this test, and then doing FTR correction, we then for
all of our variants, can predict which ones are likely to be functionally impactful versus
not.
So there’s one other feature we saw from comparing these gene expression signatures,
and that was a direct comparison between the wild type and mutant signatures.
So the top two rows I’m showing, the ARAF case, here is a case for STK11 where, when
introducing the wild type ORF, very strong consistent signatures, but introducing this
mutant does not give as consistent of signatures, and so we think that it’s losing function.
And then conversely, with this beta-catenin mutation, the wild type does give some consistent
signature, but the mutation actually increases the consistency of the gene expression signature.
So we represent these two features on this plat, which we’re calling a sparkler plat.
So the X-axis shows the corrected p-value of that test to determine if there’s a functional
impact, and so anything that falls below our FDR cutoff would be predicted to be inert.
And then, for the comparison between wall type and mutant, things that look like a gain
in function in the mutation are shown with a positive score, and those that are predicted
to be loss of function gets a negative score.
So then when we look at this visualization gene by gene, we see these interesting patterns
with no oncogenes, no tumor suppressors, and then we can look at other genes of unknown
function.
So many gain-of-function mutations or change-of-function in these known tumor suppressors, and I’m
calling out the specific mutations.
For these known tumor suppressor genes, again, many, many loss of function predictions with
our approach.
And then finally, with genes with unknown function, some maybe have no mutations that
are produced to be impactful, and then some that may have a functional impact.
So how do we know these predictions are correct?
We have a set of benchmarks that -- benchmarks of mutations with known function and so we
were very accurate with those.
We also did an approach to assess our false positive rate, so we had a very high replicate
experiment where we did 21 replicates of a subsample of these ORFs and then if we simulate
a smaller study where we just take eight reps from the same ORF and predict how many times,
or determine how many times we falsely determine something has an impact when we’re actually
sampling the same ORF.
So from the simulation, we were able to see that our false positive rate from the simulation
was very accurate to our expected false discovery rate.
Also, we’re looking at correspondence with genetic hot spot mutations.
So this is one example.
In FBXW7, which is a known tumor suppressor, these two mutations here were predicted to
not have an impact, whereas this one was predicted to be loss of function.
And when you look at these mutations and the frequency of them here in cosmic, the one
mutation that has a predicted functional impact is right near a hot spot, and so you would
potentially predict that that would be more functional than these other mutations.
Another way that we’re assessing, if a signature is, or how correct these predictions are,
are to look at what the actual identify of the signatures are, and compare the signatures
between different alleles.
And how we’re doing that is, one approach is through clustering.
So if we take all of our ORFs and just cluster them together to look at transcriptional classes,
we do see some known connections such as with KEAP1 and Nrf2.
And then we see this larger cluster here, and if we zoom in on that, many of these ORFs
in this cluster are known driver mutations in the EGFR/RAS pathway.
And so, any mutation that we predict to be functional, and is present in this pathway
gives us stronger support that they are indeed functional.
So within this cluster, we see rare variants that, again, these variants were observed
in very low frequency across all the adenocarcinomas that were identified, and these were all predicted
to have a functional impact, and also cluster with known activating mutations in this pathway.
And so we can predict that these may also have a similar functional impact.
So it wasn’t that all rare mutations in these genes clustered in this pathway.
So, for example, for EGFR, there was one mutation, this H1129Y, that also was predicted to have
a functional impact, but was not clustering with the rest of the mutations.
And so, we’re currently investigating what this unique signature is.
And so what I don’t have time to talk about is other orthogonal assays that we’re doing.
I have one example on a poster today, but we’re also taking this ORF collection and
putting it through a host of other assays that are more pathway specific, but also potentially
general assays.
And so doing this type of impact phenotyping is really important to really understand what
the function of all these specific variants are, and also, at a broader level, if we don’t
know what the gene function is, it’s important to try not fuse mutations into functional
pathways.
What’s powerful about this unique expression approach is that it’s agnostic.
We don’t need to know the actual function of the gene, but we’re just comparing it
in wild type, so do we see a difference between the expression changes that are induced upon,
or with the mutated version?
And we think that this approach can be used for any gene.
Again, we’re interested in expanding this to other variants, not necessarily cancer
somatic variants.
But -- and then also an interest of mine is splicing, and so I’m interested in expanding
this to look at splice variants.
So with that, this was a huge effort by many people at the Broad, and I’d first like
to thank Alice Berger and Xiaoyun Wu, who are co-leading this project, Jessie Boehm
and Matt Myerson, and Todd Golub's lab has also helped a lot with this project.
As a shameless plug, I’m starting my own lab at UC Santa Cruz, so there are many positions
open, and I guess I’m a little early, but I can take your questions.
So, there will be more time, I guess, for questions.
[applause]
Male Speaker: Nice work.
The question I have is when you do these gene expression profiling, obviously you’ll get
the [inaudible] that these associated through the mutation that discover a positive in your
assay [spelled phonetically].
Can you go back to the tumors, to the primary tumors [inaudible] those mutations and see
whether you see an enrichment or that of particular seniors during those tumors [spelled phonetically]?
Angela Brooks: We haven’t done that analysis yet, but that
is exactly what we hoped to do.
I did a very superficial analysis of that, and we didn’t see the same type of signature,
but I’m trying out other approaches.
Male Speaker: And another question.
Obviously, you look for expression changes.
I mean, there is a likelihood, in your opinion, that the driving mutation may not necessarily
influence a gene expression, or do you anticipate that any driving mutation will either directly
or indirectly, at one point, will affect the gene expression?
Angela Brooks: So, the short answer is I think that all mutations
won’t affect gene expression.
We may see changes at the methylation level.
We were only assaying gene expression.
We tested actually a splicing factor mutation and didn’t see big changes, so we think
that’s because it’s affecting splicing and we weren’t able to assay that.
So we’re hoping that this concept, it actually of the wild type mutant comparison, but maybe
with other assays.
So if it’s affecting methylation, maybe you can use those same concepts, but for different
assays.
And the one we are testing out now is self-profiling, so imaging different futures of cells after
introduction of these ORFs, and then seeing if we can use the same approaches, yeah.
Male Speaker: So, on a related note, I can imagine there
could be some very either cell-type specific mutations or very time-dependent changes in
gene expression.
So I was wondering, I mean I’m sure I have lymphoma as the mutations that won’t score
in epithelial cell line [spelled phonetically].
So, how did you look at that?
Or another way of saying it is, did you -- you had great results, but were there any hot
spots or anything you were pretty darn sure was going to be a functional thing from some
other point of view and didn’t score?
Angela Brooks: Actually, for everything that we knew about,
we did see a functional affect.
However, what we’ve now started to do is take these into different cell lines.
So what I was showing was data from an A549, which is a lung cancer cell line.
It has background mutations in some of the genes that we assayed.
And so putting it in a different context, we have our primary lung cell lines, a different
lung cancer cell line, and we actually do see some differences.
So one example is in KEAP1.
So A549 has KEAP1mutations.
So, yeah, the cell line itself has KEAP1 mutations, and so we still think we’re reading off
a loss of function mutation because the mutant version can no longer rescue the wild type
function.
But then when we put it in a different cell context with KEAP1 wild type mutations, we
actually may see potentially dominant negative effects, but we are still seeing a functional
affect, but maybe the direction of the affect changes.
Male Speaker: What about the -- how are you going to deal
with real lineage-specific effects, or things that require the presence of other oncogenes
that are mutated, but are not present in your testing?
Angela Brooks: Yeah, I mean, we really understand that there’s
a lot of context effects.
And so, when we assay them, we’ll just have to take those into consideration.
All right, thanks.
Katherine Hoadley: Thank you.
[applause]
Our next speaker is Hsin-Ta Wu.
He’s from Brown University, and he’s going to tell us about "CoMEt: A Statistical Approach
to Identify Combinations of Mutually Exclusive Alterations in Cancer."
[end of transcript]