Matthew A. Wyczalkowski: Thank you, Ilya [spelled phonetically], and
I'd like to first thank the organizers for the opportunity to present the latest -- or
recent and ongoing work from our lab on the integration on the multiple data types for
virus discovery.
About 10 to 15 percent of cancers worldwide are caused by viruses, and there are several
virus types, which we know -- which are associated with cancer. The role of others are controversial.
One of the things that viruses do is integrate into the host genome, and whereas others remain
episomal and do not integrate. One of the questions that we wish to address in this
study is, what is the role of integration in gene expression, and also, how is integration
associated with gene initiation and progression? It is a broader question. And then, more broadly
at the questions we wish to address are, even though viruses -- many types of viruses are
ubiquitous in the human population, cancer is not. So a large question is, why do some
people get cancer and others do not?
The TCGA data provides a rich source by which we can start to address some of these questions.
This is a graph of samples, which are available for various diseases, and with various data
types. For this particular work, I will be focusing on four different types of cancer.
All of these have viruses -- known to be associated with viruses to one degree or another, and
we will be looking at bladder, cervical, head and neck, as well as stomach cancer. So a
brief just overview of the particular pipeline that we've developed for this work. We start
off with RNASeq data, which is aligned to a human reference, and we extract the unmapped
reads, which are then aligned to a -- using BLAST to a large virus database. This then
allows us to -- this is the virus discovery part of the analysis -- and it allows us to
form a small select virus reference, which includes just the viruses of interest. We
then have -- we started with whole genome data, which is initially aligned to the human
reference, and we realign it to this select virus reference, which includes the virus
and the human reference, and also for the cervical work in particular, we use cervical
sequences, which are aligned with -- created with HPV probes and are aligned to the HPV
reference to start with. And we use these data to query viral integration.
So just to give a broad overview of the viral patterns that we observe in different cancer
type, in bladder cancer only about 5 percent of the cancers are associated with virus,
and these are the human papillomaviruses, the various herpes viruses, and also BK polyoma;
whereas in cervical cancer, nearly all cancers are associated with some form of HPV. For
head and neck, about 85 percent are -- have no virus with HPV 60 and 33 being dominant;
and then stomach cancer has about 10 percent EBV. So to analyze the integration, we use
a reference that has a human genome, as well as the select virus, and typically read pairs
will align to adjacent locations in the genome near one another, whether it's human, or virus.
On occasion though, we'll find a discordant read pair, where one of the reads maps to
the human, and one of the reads map to the virus.
And it's these discordant read pairs that we then analyze in order to look at for evidence
of integration. And in particular, we take the coordinates of the genomic position of
the human read, as well as the virus read, and we construct a coordinate of one point,
which corresponds to one discordant read pair. We then look at a number of such read pairs,
and clusters of such discordant read pairs then correspond to evidence of viral integration,
and these can be identified both visually, as well as using tools such as Pindel. So,
looking at viral integration we find that for EBV viruses we -- there are -- we haven't
found any integration events. For viruses for HPV-18, in all the cases we looked at
we did find integration. For the other HPV viruses, about half to two-thirds are integrated.
So, for the next few slides, I will now drill down into a specific sample and analyze that
in further detail. In this particular sample, it's a head and neck sample, which has 118
discordant pairs, which map to three different clusters. And these clusters map to human
Chromosome 14. You -- we can now map the -- this onto the gene and these -- this cluster providing
evidence of integration is on the RAD51B gene, which is a DNA repair gene, which has been
associated with virus integration in the past. The -- just to give you a sense of scale,
this is about -- the viral chromosome -- the viral genome is about roughly 8,000 base pairs.
This -- I'm showing is a section of about 150 kilo base pairs, not to mention this particular
gene extends further out in both directions, and there is one exon, which is visible here,
with that location and the integration takes place in the intronic section.
We can now -- you might have to turn your head to see this, right? But we can now look
at the copy number along the Chromosome 14. And what we find is that, outside of the integration
region, the copy number is roughly two. Within this particular section of the integration,
the copy number is about 15, and then there is a smaller section here where the copy number
is about four. We can draw lines to guide the eye from the discordant read pairs to
the chromosome, and what we find is that the discordant read pairs map onto -- very precisely
-- onto boundaries in the -- in regions of the copy number, and we find this to be a
pretty -- basically, very commonly seen. And we find the copy number increases in integration
sites in about two-thirds of the cases. For the remaining one-third of the cases, we see
with about equal probability copy number decreases or no copy number changes.
We can also look at the copy number of the virus, and this scale here is about copy number
of 30. One of the things that we observe is the deletion of a section of the virus. The
circular HPV virus, as it integrates into the host cell DNA, very frequently loses a
section as it integrates, and this is the last section that you see here. The copy number
is a little bit more noisy than what we observe, but -- for the human chromosome -- but once
again, the discordant read pairs tend to map onto regions of differing copy number sections.
We can also look at -- construct a histogram of the copy number, and in particular, I'll
be looking at the blue line -- blue histogram here corresponding to the virus, and what
we find is that there's two distinct peaks in the copy number histogram. And this suggests
that there are two distinct viral populations, which are found in the sample. We can also
look at the viral genes located here on the horizontal axis, as well as the RNA expression
of these genes. And what we find, which is commonly seen, is that the E6 and E7 viral
oncogenes are expressed at a high level with the remaining genes being at a relatively
low level. And we can also look at the expression of the RAD51B gene over here. On the left
is a distribution of the RSCM expression level for -- here for tumors -- a population of
head and neck tumors; and here on the right a population of head and neck normals. And
for this particular case that we're looking here, the RSCM expression is -- the gene expression
is very high.
And we can actually look in more detail at the expression patterns. Here is now a plot
of per-exon RNA expression for the 15 exons, which compose the RAD51B gene. And in red
is the per-exon expression level of the particular sample we're looking at. And the teal color
corresponds to the other head and neck tumor samples under consideration. And what we find
is that downstream of the integration site the expression level per-exon is very high
for this particular sample; whereas upstream it tends to be normal. So, this provides evidence
that the viral integration up-regulates the downstream expression -- or the expression
of the downstream exons. To look at this in a little bit more detail, here is now essentially
the same data, but I'm plotting the -- a normalized per-exon expression level -- it's normalized
so that a value of zero corresponds to an average expression level -- and now I'm plotting
these versus position with the integration event here, drawn here in red, and then this
is for a million base pairs on either side of the integration site. And here are the
various genes that are found there. And what we find once again is that downstream the
viral -- downstream of the viral integration event the exon expression is quite high.
The next question that we wanted to see is, is this a general pattern observed, or is
this something that we see just in this particular case? And so, here is a distribution of per-exon
expression for samples in which -- which have a copy number increase in the integration
site, and we consider the expression of upstream for 100,000 base pairs within the integration
site, and also downstream of the integration. What we find is that upstream of the integration
event we tend to find the distribution -- it is relatively broad, but it has significant
population both below and above zero; whereas within the integration event where there is
a copy number increase we find that the per-exon expression is -- tends to be up-regulated,
and downstream it tends to be up-regulated as well, generally speaking. So this is for
copy number increase, and we can also consider expression for copy number decreases, and
what we find is within the integration site, the per-exon expression is down-regulated.
Upstream it tends to be around zero, and there are two different populations -- upstream
is around zero -- there are two different populations with a population around zero,
and then a population with a significantly increased expression.
So, to summarize we've developed a pipeline, which allows the integration of multiple data
types for the analysis of viral integration. We use RNASeq for virus discovery, as well
as for expression analysis, and whole genome data as well as exon data for integration
analysis. And we've constructed a unified visual representation, which allows us to
combine visually for one sample these integration events. And finally, we've illustrated a close
association for virus integration, copy number domains, and expression levels. And with that,
I would like to thank my PI, Li Ding, as well as the other lab members, and others who have
contributed to this project. And I'd like to thank you, and if there's any questions,
I'd be happy to answer.
[applause]
Male Speaker:
Thank you. Have you looked at what distinguishes tumors, which have integration, versus those
which don't have integrated virus in terms of whether there's a different mutational
spectrum, or other alterations in the non-integrated tumors that might predispose them to survive
them without integration? And what -- the reason I ask is there've been a number in
reports over the years in tumors that are HPV-positive have a portion of their tumor
that becomes HPV-negative and progresses, or cell lines that have HPV in vitro initially
lose HPV and still maintain an immortal state. So, you have any insight into that?
Matthew A. Wyczalkowski: Ah, the question being, are we observing differences
between tumors where there is a viral integration and where there is not? We started looking
at doing various clinical correlations between these. I'd say that right now it's still in
the early stages of the analysis and we haven't observed any particular differences yet, but
that's something that actually is an ongoing work.
Male Speaker: Sure. Very nice talk and really nice analysis
on the details of viral integration and expression. Just want to point out a couple papers in
the literature for you. One is focused on the analysis of TCGA data, and it's from Erik
Larson's lab, I think, a former post-doc of Chris Sanders, that was in Nature Communications
last year. Another is from our group, from [unintelligible] on cervical cancer in Nature
at the end of the last year. And both of those, you know, I just noticed that one of the genes
that you mentioned, RAD51B, for example, both those papers highlight that.
Matthew A. Wyczalkowski: That's right, of course.
Female Speaker: Is this pipeline available online?
[inaudible commentary]
Female Speaker: Is this pipeline available online to the public?
Matthew A. Wyczalkowski: I'm sorry?
Female Speaker: Is this pipeline available online to the public?
Matthew A. Wyczalkowski: This is something that's under development
right now. I expect it will be made available.
Female Speaker: Thank you.
Matthew A. Wyczalkowski: Yup.
Male Speaker: One last question for you.
Female Speaker: Thank you. In the case, this RAD51 where you
see the later exons being highly expressed, are they part of a fusion with some HPV gene?
Matthew A. Wyczalkowski We haven't -- the short answer is I'm not
sure. We haven't analyzed that. Thank you.
Male Speaker: Okay, let's thank all the speakers one more
time, please.
[applause]
So, this concludes our session, and now Matt Meyerson and Marco Marro will make some closing
remarks.
[end of transcript]