Andrew Gross: Good. So, I think this is sort of an appropriate
time for me to give this talk because I’m going to be talking about how, you know, a
lot of the things that worked in single-cohort analyses don’t necessarily scale as we add
more data, as we try and integrate across different data sets. And so, I’m going to
give you a simple example looking at tumor normal phenotypes. But I think this -- the
general lesson applies to many things.
So, TCGA is sort of both a blessing and a curse. We’ve been talking all, you know,
the last couple days about all the really nice things about it. And I really show this
unprecedented data set to do lots of different things. But as you can see from this figure,
you know, from a methods standpoint, one of the really nice things that we thought we
wanted to do with this is to integrate across different data types and different tissue
types to look at networks and pathways in a pan-cancer context. But, you know, it turns
out that this is actually a really hard thing to do. In our lab, we’ve spent a long time
trying to do this. And you know, we get really complicated really quickly. The same methods
don’t work in one cohort as they do across many cohorts and in one datatype integrating
across many datatypes. And so, I think part of it -- part of the problem is that we need
to think about this from a different perspective.
So, how do we get past this? Taking from a lot of the work that Google has sort of trumpeted
in what they’re doing, when we scale out, when we get to truly big data, often simple
models will get you most of the way. So, complicated models may give you better results, but it’ll
be much harder to integrate with different data sets and much harder to put into production.
Additionally, we need to really work on incremental and transparent methods. So, I’m a graduate
student; I don’t have a full team of software engineers to check all my pipelines and make
sure everything’s all right. You know, doing these complicated analyses, it’s really
hard to find the bugs. It’s really hard to do all the tweaks that’s required to
bring a project to completion.
So, the goal of this study is going to be relatively straightforward. We’re going
to look at the tumor phenotype, and that’s going to be the mRNA, microRNA, methylation
data. And we’re going to try and get at, what is the scope of the events observed in
specific tissue cohorts as well as a pan-cancer context? And so, we’re going to do this
by sort of going the opposite way that we normally do these studies. We’re going to
first look at what all cancers have in common, and then, and only then, we’re going to
look at what are specific to individual cancers. And so, traditionally, when we’re talking
about something like differential expression, in 2002 someone might do a gene expression
profiling and highlight a handful of genes in one specific cancer. Then, a few years
later, we’re going to do a couple different cancer cohorts. And maybe a meta-analysis
or a review will integrate these things and highlight those genes that are sort of ubiquitously
turned on or off in cancers and those that are tissue-specific. But this data set really
allows for a unified analysis to do this sort of all at once with a shared data set.
So, sorry, this isn’t coming out too clear, but on the right, we’re going to start by
defining a very simple model of differential expression. So, this is an example of one
gene profile across TCGA, so it’s going to be about 8,000 patients. Over here, you
can see that about 600 patients are going to have matched adjacent normal tissue samples.
So, the first thing that we’re going to do is, we’re going to take this data, and
we’re going to throw away about 90 percent of it. We’re only going to look at the matched
tumor normal samples. And then, we’re going to do something that’s going to throw away
even more data. We’re only going to observe whether the -- a gene has higher or lower
expression in the tumor compared to the normal. And then, finally, we’re going to count
how often that happens across a given cohort.
So, this is -- we’re going to call this statistic fraction over-expressed. And this
is going to get at how often the gene’s over-expressed in a -- in a given context.
And we’d expect if the gene was under sort of random regulation, this would happen half
the time.
So, there’s a lot of advantages to this method that may not be obvious immediately.
Anyone that’s done pan-cancer analysis with expression data knows that tissue baseline
-- tissue-specific baseline expression is really hard to overcome. So, this is going
to be -- this is not going to be sensitive to that because we’re not -- we’re only
looking within paired samples, and we’re not incorporating the magnitudes at all. This
test statistic is going to be much easier to interpret. It’s a fraction from zero
to one. And we’re going to be able to carry this forward to integrative analyses and have
a common -- a common baseline to compare across different data layers and different datatypes.
So, obviously we’re going to have some disadvantages throwing away this much data. And those are
going to be that we’re, you know, less sensitive to the magnitude of differential expression,
and obviously less powered. But sort of in preliminary analysis, we’ve found that this
very simple metric, given enough samples -- and, you know, as we know, in the future we’re
only going to get more samples -- it actually compares very well to a -- to a paired t-test,
which uses the full -- the full data. And so, we get about 99 percent correlation. And
this is within one specific cohort. You can imagine, if we were trying to carry this over
to a pan-cancer analysis, we would have to look at things like tumor purity differences
in the tissues. We would have to look at margin effects, all these different -- all these
different factors that aren’t going to be that big of an issue in our analysis because
our sole assumption is that our tumor sample has a higher concentration of tumor cells
than our normal sample.
So, we’re going to start out by trying to get at what’s common across all different
cancers. So, we can apply this across the methylation data, the mRNA data, and the microRNA
data. You can see that we get a full profile. Fifty percent is our sort of random model.
Immediately you can see a skew of microRNA. So, this data tells us that microRNAs tend
to be over-expressed in this pan-cancer context. When we look into the tails, we see a lot
of the players that we’ve seen over and over in these studies. We can see alcohol
dehydrogenase, and a number of the alcohol dehydrogenases are turned on -- turned off
sort of ubiquitously across all different tumor types. Additionally, we see a number
of things that are turned on across many different tumor types. So, here’s microRNA-21. And
you can see it’s turned on in every different tissue type except for maybe kidney chromophobe
cancer.
We can carry this forward. So, what we get here is, we get a simple metric. We get a
fraction of how often the gene is turned on or off across this pan-cancer cohort for every
gene. So, we can use this to do things like pathway analysis, network analysis. We see
an enrichment of mitotic genes, and a lot of the fatty acid -- a lot of the metabolic
pathways are turned down in tumors. Visually, we can do functional enrichment. So, this
is on the methylation data. And we can see PRC2 probes, how to -- enrichment for hypermethylation,
whereas we see a decrease in promoter hypermethylation that is enriched and is across every different
-- across the full set. So, promoters may be methylated to silence a specific, you know,
patient tumor. But these things aren’t generally conserved across many different cancers.
So, finally, we wanted to see how well this type of metric would replicate. We were able
to get about eight microarray data sets for about 900 subject. The data’s not as good
because these are older microarrays, but we do get pretty good replication. So, we get
about 76 percent correlation.
So, now we’ve sort of established what all cancers have in common. If we were to look
without this knowledge at individual cancers, these are the things that we would get. But
with this knowledge, we can go further in to see what differentiates cancers. But first,
we’re going to look at -- we’re going to sort of explore another hypothesis. So,
we know that these genes that we see over and over again -- you know, these are probably
genes that are associated with growth and proliferation, because this is sort of common
processes that we see in all different tumors, but we haven’t looked at the levels. So,
now we want to bring back the levels and see, are there -- is there differential usage of
these genes, and does it matter?
So, we can do this by using our fraction over-expressed profile. So, this is our fraction over-expressed
for 20,000 genes. And we can correlate this with our -- across -- with all of our different
patient profiles that we’re going to normalize to the normal tissue expression. So, you can
see, this is one patient with a matched sample. You can see in their normal sample, there’s
not much correlation with this statistic, which is sort of [unintelligible] construction,
whereas in a tumor, we see a pretty solid correlation. And what we can interpret from
this is that in this patient, those genes that are always turned on in the cancers tend
to have higher expression levels, and those genes that are always turned off tend to have
lower expression levels.
So, just as another sanity check, for all of our matched patients, we see 99 percent
of them have lower values for this correlation in the normal compared to the tumor, but you
can also see that there is a pretty big spread of this correlation level amongst the tumors.
So, this analysis doesn’t depend on the matched normal data at all, so we can apply
this across all 8,000 tumors that have expression levels. We see a pretty big spread with some
tumors, such as colon and rectal cancer having pretty uniformly high correlation to this
tumor growth signal. And others have a bigger spread and pretty low correlations.
And finally, we can look at whether this is associated with outcomes, and, like many of
these growth and proliferation signatures, we see a high hazard ratio of many of these
tumors, which have lower baseline correlation levels. And so, the way to interpret this
is a hazard ratio of about two. This is in kidney cancer. And this means that one -- you
know, patients with one standard deviation higher correlation to this tumor growth signature
are about twofold increased risk of death. And, you know, this is a -- there’s a pretty
big effect size for many of these tissue cohorts, as you can see down here.
So, now we want to really get at, what are the differential pathways activated in different
cancers? And we can do this by doing another very similar analysis. So, this is a -- our
breast -- we’re looking at our breast cancer cohort. And here we have our fraction over-expressed
metric for all the genes in the breast cancer cohort compared to the same metric for all
of the other tissues pulled. So, you can see that there is a pretty strong correlation
between these two things, which is to be expected. So, most things are shared between breast
cancer and the rest of the tissues. But there are some genes off the diagonal, and those
might be interested to see what is turned on or off in breast cancer but not in different
tissues.
And so, one of the first things that comes up -- this is this gene right here -- is MET,
which we’ve been talking a lot about this -- these last couple days. We can see MET
has very high over-expression in kidney papillary cell renal -- kidney papillary cell carcinoma,
which is to be expected. That’s why we know it has clear oncogenic activity -- a number
of other tissues as well. And these tissues that have the highest differential expression
are generally the ones where we see familial germline mutations. So, we know that has oncogenic
activity in those tissues, whereas in breast cancer and in prostate cancer, we actually
see a decrease in expression in the tumors compared to the normal -- to the normal tissue.
Additionally, a lot of what we’ve shown in TCGA is that not everything is tissue-specific.
There are -- you know, the genomic context matters. And so, we can group by the genomic
context and look for things that are context -- are differentially activated in different
contexts. So, here we’re dividing based on the status of P53 mutation. And we can
see, again, while most things agree, there are some genes off the diagonal. We can see
that MCM8 has no differential expression in P53 wild-type patients, whereas it does have
higher expression levels in the tumor in P53 mutants. Sort of in contrast, Mdm2, which
we know has, you know, a strong interaction with P53, is over-expressed in the tumor in
P53 wild-type patients, whereas there is no differential expression in P53 mutants.
So, in summary, we described a very simple metric for studying the tumor phenotype, and
we’ve used this to define a list of differentially expressed genes, microRNA and methylation
sites in this pan-cancer context. We’ve used these features to stratify patient outcomes
and define both tissue and driver-specific changes in cancer. And I encourage you -- so,
we’re working on putting this manuscript together. But I encourage you think about
the specificity of results in these -- in these targeted analyses to really understand
what is specific to a given tissue context or a given model versus the general features
of cancer as a whole.
And with that, I’ll thank -- this is the Ideker Lab -- and open it up to questions.
[applause]
Male Speaker: Very nice talk, thank you. I’m curious:
Why not assign a statistical significance score to your -- to your statistic, maybe
by permutations?
Andrew Gross: So, we’re using a -- you know, so, our statistic
is basically a coin flip. And the values reported here -- we’re getting, you know, 90 percent
of the time a gene’s turned on. These are screamingly significant. We get significant
at about 55 percent. And so, in this realm, we’re really more worried about the effect
size than the -- than the P-values that we get on these things because, you know, a traditional
differential expression analysis has power with maybe 10, 20 samples. We’re using 600.
So, we’re sort of past the realm where significance matters. Anything that we’re looking at
is going to be very significant.
Female Speaker: [inaudible]
Andrew Gross: Oh.
[laughter]
Andrew Gross: In the back. [laughs]
Female Speaker: [inaudible]
[laughter]
Female Speaker: Thank you very much, Andrew.
Andrew Gross: Thank you.
[applause]
Female Speaker: Okay. So, we are going to have a break. After
break, we will have workshop. But before that, I want to -- on behalf of the TCGA project
team, I want to thank Julia for taking the time to organize this productive scientific
symposium. Julia, thank you.
[applause]
We also would like to thank Josh Shapiro, Jennifer, and the colleagues from the Capital
Consulting for getting us organized so we can have a hotel, and we can have our slides
[laughs].
[applause]
And I also would like to thank TCGA project team, especially J. C. Carling [spelled phonetically]
for the work they do behind the scene for getting this going. And finally, I’d like
to thank my co-chair, Katie Hoadley -- where is she? [laughs] --
Female Speaker: [inaudible]
[applause]
Female Speaker: -- for her excellent work. Thank you, everyone,
for coming to this meeting, and see you in the future.
[applause]
[end of transcript]