Christopher Benz: ...thank the organizers for asking for an
oral presentation of our abstract and I want to point out that this is the work of senior
scientist at our institute, Dr. Christina Yau, who was unable to attend. I do also want
to point out that this afternoon you'll hear more from Josh Stewart, who co-led the Pan-Can
analysis; it was the freezing of that data set and over 3,000 samples that enabled this
kind of pathway analysis. So, most people are familiar with the phospatidylinositol-3
kinases, it's a very large family, heterodimers between regulatory subunits, p185 -- p85 alpha
is most commonly know, but there're four others and the catalytic unit, the p110 alpha, which
is encoded by the PIK3CA gene, which is very commonly mutated in human cancers, as you
heard. Modular domains of this PIK3CA gene are shown here; there's five of them, it's
frequently mutated in multiple types of cancer, as I pointed out, but there are hotspot mutations
in both the kinase and helical domains that we'll talk about. These are thought to be
early and possibly initiating events in malignancies like breast cancer.
There's a notable absence of mutations in the Ras-binding domain, RBD, and also within
the core catalytic site of the kinase domain and that's seen across cancers. Now, pre-clinical
evidence which led to the rational for doing this study, indicates that PIK3CA mutations
are activating and gain a function, but domain-specific mutations may have different downstream effects,
in particular, phosphorylation and activation of AKT and other downstream pathways including
Emptor -- and also, phenotypic consequences, but these are pre-clinical studies in model
systems. So the TCGA Pan-Cancer data set is big enough to offer an opportunity to ask
questions about possible pathway differences linked to domain-specific PIK3CA mutations
in different tumor types. Are there common domain-specific pathway activities across
the different tumors or are these cancer-type specific?
So, in terms of pathways, we're going to use an algorithm, known as PARADIGM, that's been
used in most of the marker papers of TCGA. Briefly, this integrates RNA gene expression
data with DNA copy number data, superimposes this on a super pathway to infer activities
which refer to as inferred pathway links, or IPLs, and encompassing over 13,000 pathway
features, which can be displayed in a heat map or analyzed for differences between tumor
subsets. Now, the Pan-Can data set characteristics that were used here -- and these were the
frozen data sets employing the platforms including exon sequencing, RNA expression, and DNA copy
number. We had over 3,500 samples that had the PARADIGM analysis, in other words, that
we had IPL values, we had over 3,200 samples with exon sequencing data, so we have an overlap
set shown in the Venn diagram of 2637, with both IPL and a PIK3CA mutation data.
As you can see, in the Pan-Can histograms here, that the cancer-type distribution among
the valuable samples is very different, that's actually reflected a lot in the overall TCGA
accrual of these samples. So, that's going to have to be taken in to account. Now, somatic
PIK3CA mutations have an overall mutation frequency, in this Pan-Can data set, of 22
percent, that's 572 out of the 2,637 cases, but the frequency per tumor type is quite
variable, as you can see from zero percent, as seen in AMLs, leukemia, to greater than
50 percent as seen in uterine endometrial cancer, with the intermediate values as shown
there.
Now, in order to look at domain-specific mutations, we also filtered so that we would use only
nonsense mutations, because we're interested in coding data function abnormalities, and
also mutations that occurred in only one domain. As you can see, in nearly 100 of the cases
there were multiple mutations across PIK3CAs, so we ended up with a set of 447 cases that
we could evaluate and you can see, eliminating the AML, we have a very different -- again
we have an uneven distribution across the tumor types that's going to have to be accounted
for.
The domain-specific PIK3CA mutations are shown here, as you can see, there's a preference
for the helical and kinase domains, and you can see also, the hotspots, in particular,
specific mutations histidine to arginine at 1,047, in the kinase domain, glutamate to
lysine at 545 and at 542. So, helical domain has two nearly adjacent hotspots and the kinase
domain has a very common hotspot at one spot. So, 83 percent of the cases had either helical
or kinase domain mutations and 64 percent of these were at either of the three hotspots
as shown.
So, the domain distribution of the mutations by cancer type was significantly different,
color-coded here: the helical domain is in orange and the kinase domain is in red, and
that's the most common, as shown, and across the different tumor types and as an example,
again, of this variation, we see in breast cancer seems to prefer kinase domain mutations,
with 52 percent of those being in the kinase domain, whereas in squamous cell of the head
and neck and lung, there's a preference for helical domain mutations, 65 percent of those
being in that domain and only about 20 percent in the kinase domain.
So, how did we look at pathway activation and in control just for cancer-type? Well,
we used the IPLs and identified significant IPLs for each domain versus all others by
logistic regression, and we adjusted for the cancer-type with a wall [spelled phonetically]
test p .05. The kinase domain ended up having 711 significantly different IPLs; the helical
domain over 1,000, and amazingly, these did not overlap much, there were actually 1,500
that were non-overlapping IPL features between the two kinase and helical domains. We then
determined, in pathway enrichment among the different IPLs, with an FDR corrected E score,
and we visualized by Cytoscape. So as a, kind of, proof of consequence, we first went through
to the PI3 complex itself and here you see a color-coding that indicates: if it's very
dark red, this is kinase domain positive association and a reciprocal helical domain negative relationship;
and if you have lighter shades of red increasing to pink, this is again kinase domain positive;
blue is more helical domain positive or kinase domain negative for green. But here, what
you see is in the PI3-kinase catalytic subunit itself, in the center here, where it's the
red areas that the kinase domain mutations most strongly associate with the super pathway
hub showing the PI3-k catalytic subunit activation and this hub is negatively associated with
the helical domain mutations, and this is, in fact, consistent with the pre-clinical
evidence, which is why we look to that to begin with.
But then asking, what was distinguishing the kinase domain mutations from the helical domain
mutations in other areas, we first turned to cell cycle and proliferation activities,
and these were featured by the polo-like kinase 1 and FOXM1 hubs, which were most strongly
associated with kinase domain mutations, you can see red and pink virtually everywhere,
and negatively associated with helical domain mutations, using the same color-coding as
before. And then, if we go to helical domain mutations, we see very different; we see the
Rho-GTPase families and also gap junction degradation as hubs and pathway enrichments
that were more strongly associated with the helical domain mutations, and negatively associated
with kinase domain mutations.
So, although these are common across different cancer types in the TCGA Pan-Cancer data set,
missense PIK3CA mutations distribute very differently with respect to total mutation
frequency and domain specificity, examples being: breast cancer's greater than 50 percent
in the kinase mutants, where as squamous cell head and neck and lung: less than 25 percent,
and opposite, sort of, relationships with regard to the helical domain mutations. The
kinase domain mutations appear to be linked more strongly with pathway features, like
FOXM1 and polo-like kinase 1 that enables cell proliferation, while the helical domain
mutations appear to be linked more strongly with features enabling cell migration and
dissemination, such as the Rho-GTPases. Now, functional studies are clearly needed to confirm
these findings, including the suggestion that breast cancers preferentially mutate PIK3CA
to drive cell proliferation, while lung and head and neck squamous cancers prefer the
helical domain mutations potentially to drive their malignant cell motility. But additional
comparisons are also needed here to identify potential tumor type, specific, or context-dependent
differences between the kinase and helical domain pathway preferences, because as we've
begun now to do the functional studies, we then commit ourselves to a particular tumor
type. In fact, working with the Hopkins group that has made isogenic lines in the mammary
cell line, MCF-10A, for these different mutants we need to know what context-specific differences
there are between the helical and kinase domain mutations, as well as general differences
across the entire Pan-Can data set, as I've shown you today. So, I'd like to that our
U.C. Santa Cruz and Buck Institute GDAC, team members, again, Christina Yau, who did all
this work, our partners at Five3, who developed the PARADIGM and performed that for us, and
then also, I'll point again to this afternoon's session where I think you'll hear more about
the Pan-Cancer 12 analysis working group and the subtype data that's in press. Thank you
very much. I'll take questions.
[applause]
Han Liang: So we have time for a few questions, yes.
Male Speaker: [inaudible]
Chris Benz: P85 alpha, is that the -- yeah, regulatory.
I guess I'm trying to understand it myself, but I think the question is: did we see any
mutations in the regulatory subunit, which would be p85 alpha. We did not look specifically
for it, but I do not believe many were reported if any at all. Maybe I stand -- I could be
corrected by that, for someone in the audience that might know better. So, the other interesting
aspect --
Male Speaker: [inaudible]
Christopher Benz: Ah, okay. That's the p85 --
Male Speaker: [inaudible]
Christopher Benz: We did not specifically look at that, but
that's an interesting aspect, because these mutations that are outside the kinase domain
are felt to alter the dependency on that regulatory subunit.
Male Speaker: So, when you look at a helical domain and
kinase domain mutations and infer their functions, for those low frequency mutations there could
be bystanders or passenger mutations, have you then go back to use those low frequency
mutations in a kinase domain or in a helical domain and look at whether they still carry
the same signature?
Christopher Benz: We didn't --
Male Speaker: -- because most of time it's highly [unintelligible]
skew everything that you are seeing. I mean, the hotspot mutation of the kinase -- hotspot
mutation of the helical domain, they're so predominant, and so the signature you're seeing
Christopher Benz: Right.
Male Speaker: -- could [unintelligible] be from them. I'm
wondering, besides doing all the functional studies of the [unintelligible] mutations,
can you take the [unintelligible] mutation and go back to see whether they actually carry
the same signature, that can infer whether they actually have a functional...
Christopher Benz: We could do that. We didn't specifically do
that, no.
Han Liang: So, I have question. So, we usually see mutations
[unintelligible] concurrent pattern or mutually exclusive, so have you tried this concurrent
or mutual [unintelligible] pattern for [unintelligible] different across different cancer?
Christopher Benz: No, we didn't. We only looked for the -- no,
we didn't control for anything that was concurrent or mutually exclusive with regard to PIK3CA
mutations. We only looked at that question most broadly.
Han Liang: Thank you. So, let us move on. So, now we
move to the last speaker, Dr. Siyuan Zheng, from the University of Texas M.D. Anderson
Cancer Center, here to talk about comprehensive molecular profiling of adrenocortical carcinoma.
So, this is the last talk, hopefully, so as [unintelligible] lunch.
[end of transcript]