Chai Bandlamudi: Hi everyone, I'm Chai Bandlamudi. I'm a graduate
student at Kevin White Lab at the University of Chicago. First I want to thank the organizers
for giving me an opportunity to present my work. I'm going to talk about the work that
we've been doing to identify recurrent fusions from the TCGA cohort. So the first part of
my talk's going to be on the approach we take to identify these fusions. And then I'm going
to talk about some of the results and some of the functional validations we've done on
this.
So COSMIC maintains a database of known fusion genes. If you look at the kinds [unintelligible]
similar to the some of the power [unintelligible] distributions we saw earlier today. So COSMIC
maintains a database of known fusion genes. About 1 percent of them, less than 10 percent
of them should have a frequency of greater than 1 percent so, which is kind of interesting.
So there is an interesting -- a few interesting things from this, is that if you look at the
tissue specificity, some of these known recurrent fusion genes for example, tempertstuERG [spelled
phonetically] is very prostate cancer specific and some of the fusions like DEFGFR3 tag are
-- you see across multiple different tissue types. So there are a few future directions
like, how is this distribution going to change with larger sample sizes and more sensitive
methods to detect fusions?
So the basic -- there are two pieces of evidence that require -- that we need to identify,
to make a fusion call. So the reads that are mapping to two different genes, these are
called discordant reads and the reads that are mapping to the fusion junction, these
we call as anchor reads. And the specificity of this anchor read is determined by this
anchor length, which is a minimum of the two overhang regions, the left and right. So this
is the anchor length. So as you know, one of the main challenges in the fusion discovery
is the high false positive rate, and this is primarily because of misalignment of this
read to this fusion junction. And this can happen in several different ways: first is
if this read aligns to, this read that's mapping to this junction can align to one of the two
genes of the fusion, incomplete, and also it can align to another gene called gene C,
or it can align to unannotated transcriptional locus. And these things, it's very incredibly
hard to computationally identify these sorts of artifacts. So we attempted to control for
some of these artifacts and we implemented this approach in a method called MOJO.
This identifies fusions between canonical [unintelligible] junctions. So the basic approach
is similar to most of the other methods that are out there. But [unintelligible] in the
way we filter for these various artifacts and also the approach we take to align, to
identify these candidate fusion genes. So this is on -- I have a poster out so I'm not
going to go into detail about this, please feel free to come by.
So obviously is like, how are we going to evaluate this? So the way I evaluated MOJO
in comparison with nine other published methods using a compendium of 18 cell line transcriptomes.
These have validated fusions and so I -- there's a list of 137 -- 131 fusions with, that are
known with experimental validation and I'm comparing these with some of the other published
methods. And I want to make a point that all the parameters are consistent across all these
methods. So if you look at some of these methods that show similar sensitivity despite that
you see like there's a significant discordance between the types of fusions they call.
So we are showing like very good sensitivity but what about false positive rate? So in
the false positives, I'm going to call these as low confidence fusions because we don't
have evidence to say that these are like false positives. We don't have experimental evidence.
So you'll notice that some of them share, some of these calls are shared by more than
one of these methods, these could be polymorphic or these could be new fusions that haven't
yet been validated. But you see a lot of fusion calls that are singled into -- that are specific
to each of these methods. And I should point out that some of these methods are designed
to identify fusions involving intronic regions and breaks in the middle of exons, therefore
larger search space and like, more number of calls.
So what I'm showing to the right is a false discovery rate plot so the anchor length is
a proxy for like, confidence in a fusion call. So the increasing number of anchor reads,
you expect to gain more specificity but it's, as I say, it's a proxy -- it's a weak proxy
because you can have a fusion call with only one anchor read because it's like low expressed.
So we've taken this algorithm and ran this on nearly 7200 tumor transcriptomes. One thing
I want to point out is that median runtime across all these methods is around four -- between
four and five hours for an 18 paired and read sample. And these are run on a machine with,
each [unintelligible] has 24 [unintelligible] and 32 gigs of memory. So our -- so despite
our efforts to control for like false positives, since we are looking at large number of samples,
inevitably we are going to see some recurrent fusions that are false positives. So the way
we accounted for that is as follows: so we ran MOJO in the high sensitivity mode, requiring
only two discordant reads and one anchor read of 10 base pairs. And then first, the way
we do singletons is, singleton calls, the one without the recurrence is requiring two
anchor reads, two proper anchor reads. We define this as an anchor read -- one of the
anchor read mapping to the junction with the largest overhang, mapping to the other gene
that its paired read maps to. And for the recurrent fusions we pulled the reads across
all samples and we look for this sort of uniform distribution of the fusion junction. And we
filter out reads that are biased to the gene A or gene B, these sorts of most likely alignment
artifacts. And then we require similar like, how one fusion call with high stringency or
high confidence fusion call. So we compile these fusion calls and then to account for
some of the annotation artifacts in the human genome and transciptome, and also to take
out some polymorphic fusions, we ran our algorithm on 1800, nearly 1800 normal transcriptomes
in the genotype and tissue expression database. And we'll filter out all calls that overlap
this and similarly with the TCGA 674 paired normal transcriptomes.
So we identified close to -- sorry -- we identified 15,600 high confidence somatic fusion calls.
So are we capturing all the known ones? So I compiled a list of fusions from the list
of different marker papers and we are capturing most of them. The only one that we missed
was, it's a non-canonical fusion that has a break in the middle of the exon. Interestingly
we are also capturing some of the fusions with more -- with much higher sensitivity.
For example, FGFR3, TACC3, we are finding it at 20 percent frequency in the [unintelligible]
significantly higher than what's been reported before. And some of the other fusions in this
tumor type, we are also finding it at some higher frequency. It's interesting to note
that 11 out of the 26 different tumor types we analyzed have at least one fusion -- one
FGFR3 fusion. So looking at the distribution of these fusion calls.
So obviously breast and ovarian have large number of samples, therefore you see a higher
number of calls and most likely because they have a lot of copy number alterations too.
So if you look at known cosmic and novel fusions, approximately 9 percent of all the fusion
calls have a gene involving a cosmic gene or as a novel, or is a novel -- or is a known
cancer gene -- known fusion gene. So -- and the distribution of number of fusions per
sample across the different tumor types. So some of the solid tumors have like higher
number of fusion calls per tumor and you see some tumor types like the thyroid, adrenal
tumor types showing like, very fewer fusions. So one thing to look at is like are these
fusions caused by local rearrangements or they still long range rearrangements, like
translocations or rearrangements further than 10 mega bases. So the interesting thing you
see, again, with this thyroid cancer is that it has far fewer rearrangements but most of
them are like, distilled. And some of the leukemia, as expected, show like, higher number
of translocations.
And if you look at the distribution of samples that have at least one fusion or more than
one fusion, you see this sort of pattern where like, tumors that are certainly very highly
rearranged, like ovarian have like, large number of fusions and some of them have greater
than 20. These are candidates for chromothripsis and so on. And another thing is approximately,
more than 75 percent of the thyroid tumors do not have a fusion, yet there's like, a
quite a large fraction of them have like, are a known driver fusion -- potentially driver
fusion. So again this is the same pattern with ovarian, I'm just trying to reiterate
the large number of rearrangements in ovarian tumor, and you see fewer rearrangements but
a large umber of translocations here. And with thyroid, like, fewer rearrangements and
many known cancer genes.
So these are some of the novel fusions we found, there are some interesting ones in
here. For example, this fusion involving the MPR1B has, is seen in breast, ovarian, and
prostate and these are all hormone-driven cancers and some fusions like ESR1, CCDC,
it's found in MCF10A cell line two. Some of them are interesting and very, like, following
up on functional validations on some of these.
So our approach for functional validations is we synthesize a fusion construct, package
them into viral particles, and create stable cell lines expressing the fusion construct
and perform three different types of assays: proliferation, invasiveness, and evasion of
apoptosis. So I'm going to show some preliminary data on 11 fusion constructs we built using
this pipeline in using MCF10A cell line and we did proliferation assays. And we assayed
two different phenotypes: the EdU and CellTiter Glo. One's for DNA synthesis and one for ATP
metabolism. So these are some of the -- so we have two positive controls here: FGRF3,
TACC3 and PML_RARA. You can see that some these fusions show greater proliferation than
FGRF3, TACC3. You can argue that FGRF3, TACC3 is not, it's not as endogenous enrollment
in MCF 10A, yet it's interesting that we are replicating these assays in NIH3T3 cells.
You'll notice that the one oddity here is that we have two isoforms of this fusion,
one doesn't have a DNA binding domain, and one has a DNA binding domain so the one with
a DNA binding domain that has significant -- shows significant increase in proliferation.
So in summary, we are repeating these assays in the NIH3T3 cells and we are over-expressing
full length individuals genes as controls, and we are -- and then follow with further
functional validations, analysis and so on. So another thing that I didn't bring up is
that recurrently fused genes, not the fused genes pairs, but the genes, are also interesting.
And finally, integrating this analysis with copy number alterations, mutations, and fusion
events is of significant interest just looking at the vigorous scale of, like, genome scalability
and what drives these alterations. So finally I want to thank Page Lynn, the post doc [spelled
phonetically] in our lab. She spent an incredible amount of time and effort in trying to set
up these experimental validations and following up on them and so on. And I want to thank
my advisor, Kevin, and some of our like helpful lab mates. And the bio member's team for providing
the TCGA data and the Beagle supercomputer for, in computing resources and so on. Thank
you very much.
[applause]
Male Speaker: I'm interested in what the influence of depth
of RNA sequencing would be on your discovery. So in the amount that we've been usually doing
in TCGA, the number of reads, is that maxed out your discovery rate or can you model whether
you do a lot better with much deeper RNA sequencing?
Chai Bandlamudi: Absolutely. The more you deeper -- you tend
to pick up more clonal events. So a large fraction of our fusions have only one or two
supporting reads and we're able to pick them up because we have some samples that are deeply
sequenced or in those samples that is actually a clonal event compared to the sub-clonal
event so...
Male Speaker: Then using that, can you model how, if we
were going to do a new project--
Chai Bandlamudi: Sure.
Male Speaker: --how deep would we want to sequence RNA?
Chai Bandlamudi: If you have an estimate of what's the clonality
or what sort of clonal events or what's the purity of certain tumor types that you would
expect, you can model it. We have enough data to look at that.
Male Speaker: Have you tried to validate by the TCPR by
followed the [unintelligible] sequencing of the fusions that you ever --
Chai Bandlamudi: That's a very good point. So we don't have
material for most of these, so the way we are looking at it is looking in the whole
genome sequencing.
Male Speaker: So I ask this question because I'm a little
bit concerned for some course, in particular the FGRF3, TACC3 fusion in GBM.
Chai Bandlamudi: Right.
Male Speaker: We have been starting the fusion for quite
a long time and we definitely know the frequency is not 20 percent. In particular the problem
there might be, I would suggest that in some batch of RNA tumors that are real positives
with a very high expression of the fusion gene in the contaminator -- a lower level.
Also, all of the other samples in that batch, so I would suggest that you give a look to
that possibility.
Chai Bandlamudi: To the validations, yeah. That is, that is
pretty -- yeah. Contamination's pretty hard to --
Male Speaker: I wish you were right in that case, because
that would make actually therapeutic trials much easier if we added the frequency, but
unfortunately that's not the case.
Male Speaker: I think we need to move on. We'll take the
remaining questions after the talk or at the poster session. Our next speaker is Audrey
Fu from the University of Chicago presenting on widespread genetic epistasis among cancer
genes.
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