Kjong-Van Lehmann: Hi. So, I'm a postdoc in the Ratsch Lab with
the Sloan-Kettering Institute, and I will be showing you some really cool results of
our analysis on the TCGA data where we are trying to take a PANCAN view on alternative
splicing. So, let me give you a little bit of our motivation here. So this is a figure
from the paper, a Nature Review paper from 2012, and it shows you how if you have alternative
splicing you can have a few genes where you have alternative three prime ends exon skips
which may change certain domains in the gene and can, in fact, change the function of the
gene. It gets switched from having pro-apoptotic to anti-apoptotic functions and you can also
change function from being angiogenic to anti-angiogenic, so there's a certain motivation to look into
these things as potential targets for cancer treatment.
So, we set out, therefore, with the following goals: we wanted to identify cancer-specific
splicing patterns based on the TCGA RNA-seq data. We were also interested in identifying
variants regulating splicing in the same gene so trying to find cis-associations essentially
and then also trying to find variants regulating splicing in other cancer genes. So, the TCGA
data is particularly suited because it has RNA-seq data and the matching exon data. For
the RNA-seq we will find and quantify the splicing events and the exon data does, in
fact, cover enough of the flanking regions into the intronic into the introns to actually
have varying quads there and try to find the cis-associations. However, there is one problem
with this. Unfortunately, the TCGA data has not been uniformly processed and so we set
out to do the following, and this was quite a major effort on our side. We've basically
gone back, reanalyzed all of the raw sequencing data. We have gone, taken all of the exon
data and the RNA-seq data and designed our own pipeline to remap everything and do a
joint variant calling using the unified genotype, but also trying to find somatic variants using
mutate, and then we have done splicing quantification using SplAdder. It's a tool developed in the
Ratsch Lab to actually find alternative splicing events and to add to what the annotations
of the already known existing splice events.
So, let me walk you -- sorry, this is jumping -- let me walk you through our approach of
finding new splicing events. So, we basically start off with creating a splice graph from
the known splice annotations and then, using the RNA-seq data, we are trying to find new
splice junctions if you have support for new events. We will then count the amount of reads
supporting and this is an example of an exon skip so in this case we will try to find the
read supporting this alternative exon and then basically normalize it across the reads
covering the whole region, and this gives us something we call a splicing index or the
percent spliced in.
And so here you can see an overview of our efforts. On the very left you can see that
these are already annotated splice events broken down to the different types. Here is
what we have filtered down to, this is actually a very stringent filter towards a set of events
we have high confidence in based on the data we have seen, and this is what we have generated
using SplAdder. Again, filtered with the same thresholds adding a lot of new splicing events
to what's already existing annotation and so, in order to find cancer-specific splicing
events, you can see here a little bit of a map where we have broken down the splicing
events into specific tumor types and sorted them, so each color here represents a little
bit of the fraction of samples which support a specific exon skip and on the left you can
see a comparison to whether we see that particular event in the end code data or in the amount
of the normal RNA-seq data available from the TCGA. Unfortunately, there's not much
of the match normals available, so you have to take this with a salt of grain, but you
can definitely see a bunch of cancer-specific -- in some cases possibly tissue-specific
events, of alternative splicing.
So, basically this gets us to our first point. So, we basically have gone and analyzed the
RNA-Seq splicing events. We detected new splicing events that occur frequently in specific cancer
types. Of course, we still need some more independent validation, but there are very
interesting cases which may be suited for potential targets -- as potential targets
for treatment.
So, let me go to the next goal, which is trying to take a statistical genetics view on finding
variants associated with alternative splicing. Here you can see on the left what people have
done so far in the statistical genetics field and during QTL analyses on next generation
sequencing data with gEUVADIS being one of the most recent ones, but if you are looking
on the right different colors represent heterogeneity in the cancer and the different cancer types,
but we have quite a large data set available already to do this type of analysis so it
is perfectly suited to understand tissue and cancer-specific specificity of splicing as
well as finding trans-associations which has been very limitedly possible due to the lack
of power in the previous projects.
However, there's a problem if you're doing this on this type of data. Of course, it's
noisy, you have heterogeneity, you have purity of the samples, and the many rare events due
to the somatic mutations definitely cause a problem. I won't be talking much about rare
variant analysis. We have done this as well; come talk to me if you are interested in this,
but I want to focus here more on the common variant analysis where we're basically taking
the typical linear regression approach but we're going a step further and doing something
which is called the linear mixed model. So, you're basically just looking for correlation
between an exon skip here and a variant you observe in your data, but what often is a
problem with something we call population structure where your data set is composed
out of patients from different populations and you have a lot of population-specific
variants and if you're doing a PCA on the germline line data you can see how your population
actually separates.
Doing this on the somatic variants, actually, you don't get that type of separation but
nevertheless, destruction cancer requires you to actually account for it and if you're
actually now coloring this PCA based on cancer types, you can see a little bit better how
your cancer types cluster based on their somatic variants, which is quite interesting and we
believe that it is necessary to account for this in doing an association analysis.
So, let me show you an example. Here, this is a cis-association in SNRP-C that generates
a protein which is essential for the formation of the spliceosome so this is why it's particularly
interesting. On the bottom here you can see the gene structure with the three exons. This
is the exon which is being skipped. You can see on the top right the different splice
indices. You can see how the inclusion of the exon goes down from the outer or respectively
up from the reference to the ultimate allele. This are the sample sizes we have in the TCGA
data, and here you can see on the left you can see the p-values, right, so you see the
snips and the associated p-values. Each color represents a cancer type here, and you can
see how you have a nice cis-association right around the splice junction which is associated
with the change in using the -- in the exon usage for the data we have seen.
And so, if you're actually doing this across multiple cancer types you get, actually, to
this nice plot here. So this is basically just a plot on a set of cancer census genes
and we have tried to indicate here on the left the different cancer types and each blue
dot marks a cis-association within the cancer type of a particular gene. So, this is particularly
remarkable MMAB where we can replicate its cis-association in each of the cancer types
just using a subset of samples. On the right side you can see this doing the same thing
on a splicing of related genes. You can see here some of the interesting ones, like RBM
25, and we can replicate a lot of these cis-associations across different cancer types.
This is all across 5 percent FDR -- just need to check this out. We also look at trans-association,
and this is a bit of an example. This is an interesting gene that's directly related to
P53 mediated apoptosis, and we find if we are looking, actually, at trans-associations
at the same FDR threshold of 5 percent, we find various other factors connected to this
for alternative splicing in this gene. And here you can see obviously the [unintelligible]
itself indicate a cis-association, and you can see we found some other links in the data
sets supporting several trans-associations, and this is the subset of what we have found
so far.
We find several trans-associations in various genes. Of course, most of them will be cis-associations;
it's simply where you have the most powerful and most of the sickness coming from that,
and we certainly don't believe that all of them are right and it definitely still needs
some confirmation, there's a certain error rate associated with it, but the truth will
probably be lying somewhere in the middle.
So, let me go to conclusions. So, basically we have developed a new resource for novel
and known alternative splice events so we will try to make this available to the community
as soon as we can. We identified cancer-specific isoforms that appear rarely expressed, we
have seen that in the heat map, we have a slightly updated version now where we're actually
accounting for the existence of tissue-specificity. So, we have performed common variant associations
to map splicing phenotypes and the sample size in TCGA enables us to detect a bunch
of trans-associations certainly, again, right not all of them may be functional -- may be
correct but some of them certainly will and so we're definitely looking further into that
some of them still need some validation, and again right in particular trans since they
are usually hard to find and there certainly may be some spurious associations.
So, let me acknowledge particularly this group which has been working with me on this project.
Particularly I want to emphasize André here. He has been particularly helpful in handling
the next generation sequencing data. We have been working with 3,000 samples; it has been
a huge amount of work. It's a relatively small group for this scale of a project. Thank you,
and we'll take questions.
[applause]
Male speaker: Two questions: one is do you see what's the
frequency or prevalence of those kinds of specific splicing? In other words, do you
see a small group of genes that has high enough frequency in tumor that could be a novel [unintelligible]
of targets? Second question is [laughs] regarding -- do
you do any associations with somatic mutations, especially those splicings on machinery?
Kjong-Van Lehmann: So, these are two questions. So, first, we
are definitely looking into trying to filter down these events. Right now we are just looking
at the subset of genes which seems to be cancer/tissue-specific. We are trying to particularly sort out what's
tissue-specific and what's cancer-specific using the normal data available. And towards
doing somatic variant association, we are working on doing a rare variant association
using the somatic snips, but we have also associated all the common somatic variants
available in the data set so there's a certain frequency threshold where you just have to
cut off or otherwise you're just increasing the amount of spurious associations you get.
But for the common somatic it's already available, and the rare variant is currently in progress.
Male speaker: So, I think on your first slide you talk about
recalling all the somatic mutations, all the splicing events, using the same pipeline pan-cancer.
So how important, in your opinion, is that versus a lot of people that are using individual
calls from different marking groups?
Kjong-Van Lehmann: It depends on what you want to do. We are
particularly interested in also looking across cancer types and if you are doing that and
using QCs which are done separately for each cancer type and you are looking across various
cancer types there is definitely a difference, particularly if you are looking at mutation
cause you can sometimes see that there is an increase of cause in certain cancer types.
For example, all your lung cancers are hyper-mutators, right, and so sometimes thresholds have been
more stringent but you want to have sort of uniform thresholds to actually analyze this,
so I think for that type of analysis it's important, it's not necessary for any type
of analysis, it really depends on what you do.
Male Speaker: Are you guys going to make those mutation
calls available?
Kjong-Van Lehmann: We can make the somatic variant calls available.
The germline calls, however, is [unintelligibile].
[applause]
Male Speaker: Okay, our next speaker is Dmitry Gordenin,
from NIEHS and he will talk to us about pan-cancer analysis of APOBEC mutagenesis.
[end of transcript]