Rehan Akbani: Thank you, Neil. So I would like to thank
the organizers for giving me the opportunity to speak here. And that's a picture of me
jumping from joy because I heard that my abstract was accepted for a talk. And I'll be talking
about a Pan-Cancer Proteomic Perspective on the Cancer Genome Atlas. We at MD Anderson
generate RPPA data, protein expression data for TCGA. So we analyzed -- in this particular
study we analyzed 3,467 samples across 11 different tumor types. Those are the tumor
types listed there. We did not receive any AML samples so as opposed to PANCAN-12, we
had PANCAN-11. We had 181 proteins in total, 128 of them were total proteins: one cleaved,
one acetylated and 51 phosphorlated forms. And the data were produced in six reverse-phase
protein array or RPPA batches. This is a platform for developing protein expression data.
So the first step we did was we developed an algorithm called replicates-based normalization
to reduce batch effects between the six batches. After correcting for batch effects, then we
performed protein versus other platform comparisons. So the first one was gene versus -- mRNA versus
protein matched comparisons. So what we found that on average the mRNA versus protein correlations
was on the order of 0.3. That sounds a bit on the low side, but this is common for patient
samples. In cell lines we see much higher correlations: 0.7, 0.8, but for patient samples
this is common. But what's interesting is that when we looked at the individual mean
correlations within the 11 disease types, we found that the correlation was not uniform
across diseases. In colon cancer, for instance, the average correlation between mRNA and protein
was on the order of 0.16. However, in ovarian cancer, the correlation was much higher: it
was 0.33. So we didn't see a uniform distribution of the correlations across disease types.
When we compared copy number variations with protein, we found that on average amplifications
resulted in only a 5 percent increase in protein levels whereas deletions resulted in only
a 5 percent decrease in protein expression levels. Now, there is a caveat here that this
may be due to the large number of passenger amplifications and deletions that we often
see in copy number data. So those might be diluting the results of -- for the copy number
data. For mutation versus protein, we saw that, on average, elevating mutations, those
that result in higher expression levels, increase protein expression levels by about 20 percent,
whereas suppressive mutations decrease protein levels by about 10 percent. So those numbers
are more than the copy number numbers, and again, we think that's because there are fewer
passenger mutations than there are passenger copy number variations, so that might possibly
explain why we see this discrepancy.
Other comparisons we made were between microRNA and protein. So every microRNA versus every
protein, mean Spearman correlation was on the order of plus or minus 0.07. And protein
versus protein mean Spearman correlation was on the order of plus or minus 0.15. I'm going
to focus on ERBB2 as a use case. So what we did was, we used breast cancer HER2 positive
versus everything else as our benchmark. And using HER2 positive versus the remaining breast
cancers, we identified thresholds for copy number, mRNA and RPPA that would separate
HER2 positive from HER2 negatives. Then we applied those same thresholds in other disease
types to see what comes out. And what's interesting is that not surprisingly, in many diseases
we don't see protein expression level of, you know, it's high in let's say less than
5 percent of the samples. But in some of the diseases, what we saw in those diseases where
more than 5 percent of the samples had HER2 positive protein expression levels, we saw
that by comparison with copy number and mRNA, proteins predicted a much higher level of
samples, a much greater number of samples that had high HER2 levels. So for example,
in bladder cancer we saw that only 7 percent of the samples were HER2 positive by copy
number, 8 percent by mRNA, but 22 percent by protein.
Similarly for colorectal cancer, only7 percent by copy number, 3 percent by mRNA but 37 percent
by protein and so on. So we saw discrepancies in lungadrenal and in endometrial as well.
So why is this important? This is important because many of the therapies that are currently
being used, for example, Herceptin and the new drug that's about to come out, T-DM1,
they work at a protein level and they actually target the protein in, on the cell surface.
However, many of the tests that we use today to check for HER2 positive versus HER2 negative
patients, they rely on copy number versus -- or mRNA platforms. So this finding is interesting
in that we should probably, you know try to measure protein directly when trying to assess
HER2 positive versus HER2 negative patients and who could possibly benefit from the therapy.
Moving on, this is the clustered heat map for the 3,467 samples by 181 antibodies and
using unsupervised clustering, we got eight different clusters. We nicely got the women's
cancers: that's luminal, breast, endometrial and ovarian in one major cluster on the left
hand side. Basal breast and HER2 breast were not in that cluster. They were quite distinct
from the remaining samples. And the URL is given there if anybody wants to look at this
in much greater detail, the URL is provided. This is using the tool called next generation
clustered heat maps that was developed at MD Anderson. It allows you to navigate and
zoom and search the entire heat map. So zooming-in on this Heat Map, we see that most of the
clusters, they cluster by tumor type. But there are a few exceptions. So for example,
Cluster F has a mixture of many different tumor types in there. Mainly squamous tumor
types: head and neck, lungadrenal, lung Squamous, some of the squamous bladders are also in
there.
The other cluster that didn't have a one-to-one correspondence with tumor type was Cluster
E in fact. And Cluster E had basal breast, HER2 breast, and ironically a few bladder
samples. Those bladder samples were the HER2 high bladder samples. And if you look at bladder,
that's a very, you know, different animal all together in this particular picture. What
we see is bladder is spread out across four different clusters. This is not in a single
cluster. That's in contrary to mRNA clusters where we do see bladder in a single cluster
for the most part, but here we have bladder in four different clusters. If you look at
outcome differences, so bladder that looks like endometrial -- so the ones that cluster
with endometrial cancers, they have a much worse prognosis than the remaining bladders.
And it's interesting that we found any difference whatsoever because bladder data isn't you
know, many, there are many examples with censored data so it's not the best outcome data that
we have.
Similarly with kidney cancers, we found that the squamous-like kidney caners have worse
prognosis than the remaining kidney cancers. And then there's breast cancer. So in breast
cancer, for those of you who are familiar with the marker paper for breast, we had indentified
a reactive group -- this is based on protein expression levels in breast cancer. And that
reactive group had good prognosis. Some of the markers of the reactive, breast cancer
reactive group were Caveolin, Collagen, MHY11, and Rictor.
Some of the marker proteins that were identified: so what we did was we did a differential analysis
to see which proteins would differentially, you know, were differentially expressed in
different clusters. So in women's cancers we saw ER-alpha and AR. Now AR was a surprise.
Now, prostate cancer was not included in PANCAN-11 but regardless of that, AR was pretty high
in women's cancers. In luminal breast, we found AR, BCL2, FASN, ACC1 and pACC, they
were high. In ovarian cancer, we had CYMC, they were high -- so these are potential targets.
Not all of them have targeted therapies as of right now but some of them are under development
so these are potential targets.
All of them except women's cancers had pospho Src elevated. And in head and neck pospho
Src is a downstream target of EGFR so perhaps we can use EGFR therapies for head and neck.
And HER2 was high in a number of cancers including endometrial, bladder, breast and colorectal.
HER3 was high in kidney cancer, and pEGFR, NOTCH1, HER3, they were all high in GBM so
maybe it could be amenable to combination therapy. The next step we did then, was well,
so we find clusters that are mainly there by tumor type. The clusters almost have a
1:1 correspondence with tumor type. Can we go ahead and reduce tissue-specific effects
so that we can see clusters that span multiple tumors instead of having a 1:1 correspondence
for the most part? We did that using a technique called MC that we developed, and I don't have
time to go into the details of that but here's a zoomed-in view of the clusters that come
out. So we came up with seven clusters and as you can see from the top bar, the tumor
types are all spread out among the different clusters. There's no single dominant tumor
type that's there in each of those clusters.
Cluster one had PEA and HER2 elevations, then we divided Cluster two into two parts: 2A
and 2B. 2A had HER2 elevation. 2B had EGFR elevation. Then we had a cluster with AKTP
pathway high, a cluster with AKTP pathway low. One signaling high, one signaling low.
And then we had the reactive cluster that I just spoke about. So the reactive cluster
had predominately breast cancers but also other disease types were there in this cluster.
So let me talk a little bit about this. This is the reactive cluster right there. This
is the zoomed-in view of the same clustered heat map that I showed earlier. And we can
see CAVEOLIN1, MYH11, Rictor, CollagemVI -- those are the markers for the reactive cluster,
so we can clearly see that this is a reactive cluster. What is interesting about that cluster
is that, like I said, in breast cancer, reactives have good prognosis and the same is true for
colon cancer and kidney cancer. So in colon and kidney, reactive samples have good prognosis.
However, if you look at lung squamous, ovarian, and bladder cancers, then in those the reactives
tend to do worse. Now, we don't know why that's the case. All we see is an observation here
that the reactives do distinguish from the others and that they can either have good
prognosis or bad prognosis. And that's not just true for reactive, but ironically that's
also true for some well-known pathways. So for example, the AKT pathway, one would expect
that when the AKT pathway is suppressed, you would have a better prognosis. AKT pathway
is responsible for proliferation. And that is indeed what we see in ovarian cancer, that
those samples where AKT pathways are suppressed do better. But in kidney cancer that's not
the case, it's the other way around. In kidney cancer, when AKT pathway's suppressed those
patients do worse. And as a sanity check we tried the other away around so when the AKT
pathway is activated, we indeed see a trend where the ovarians are doing worse but kidney
is actually doing better. So this is somewhat of an enigma right now, that some of the pathways
you know they don't behave as expected in some of the tumor types. So it looks like
pathway activity is dependent on tumor type.
This is interesting because there are some studies or some trials that actually try to
silo different samples by pathway activity rather than by tumor type. If we're going
to, you know, do studies like those then we should be mindful of such observations, and
this is by no means conclusive, but it just raises a possibility that pathways may not
be agnostic to tumor type.
So to summarize, the take home findings are, when we did cross platform comparisons we
found that mutations, on average, have greater full changes than copy number variations,
possibly due to the larger number of passenger events and copy number domain; mRNA protein
correlations can vary widely by disease, they are by no means uniforms across disease; HER2
protein levels are not predicted well by copy number or mRNA in certain diseases like colorectal,
lungadrenal and bladder cancer, but they are predicted well in breast cancer. And then
several novel markers were identified. There were outcome differences seen across clusters,
possibly driven by pathway differences. Pathway effects are not equal by disease. Certain
pathway activations may have good or bad prognosis depending on disease.
And not shown here, we also looked at protein-protein correlations and even they vary by disease.
So for example, p10 versus pAKT, it was found to have strong correlation in lung Squamous
cancers but not so much in any for the other cancers. Finally, I would like to acknowledge
my co-authors. This is the paper that is due to come out in Nature Communications in about
two weeks. The title is "A Pan-Cancer Proteomic Perspective on the Cancer Genome Atlas." I
also have poster number one, so anybody who's, you know, wants to talk to me further is welcome
to attend the poster session. And I would like to thank specifically Gordon Mills and
the rest of my team, and that's a picture of us celebrating the acceptance of the PANCAN
paper over dinner. Thank you.
[applause]
Male Speaker: Very nice work.
Rehan Akbani: Thank you.
Male Speaker: Just one comment: the reason you are seeing
the AKT pathway in kidney cancer when it's low, the behavior is better is because the
mPORT pathway's activated and [unintelligible] feedback. Because kidney is very unfortunate
disease, you see 6 percent [unintelligible] mutation, the CTS mutation. I think that's
the reason for why you are seeing it.
Rehan Akbani: Good, very good. Thank you.
Male Speaker: The only question I really have is, between
F and G you are talking about kidney cancer. They are squamous type; what are the markers
that differentiate them from the most of the G-type kidney cancer that you separate them?
Rehan Akbani: So I think you're talking about --
Male Speaker: The very, earlier, because all, mainly our
lineage --
Rehan Akbani: Right, right, let me get back to that slide.
Okay so you're talking about this particular figure right? So in squamous-like -- oh.
[laughter]
Rehan Akbani: There we go. So in this figure we can see
some of the markers in kidney cancer in the squamous type. Those are the markers for the
squamous type so we have phospho Src for example that's elevated in this cluster versus the
others. There's also FASN that is differentially expressed and also AMPK-Alpha, so those are
some of the makers that we see between the two clusters.
Male Speaker: Great, thank you.
Rehan Akbani: Thank you.
Male Speaker: All right, thank you very much. Our next speaker
is John Martignetti from Mount Sinai, who's going to talk to us about data mining, TCGA
breast and ovarian exomes for novel susceptibility markers.
[end of transcript]