Christopher Benz: Okay.
Well, I'd like to thank the organizers for the opportunity to present on behalf of our
UC Santa Cruz and Buck [spelled phonetically] Institute GDAC.
And I'm also presenting on behalf of the first author of this study, a scientist in my group
by the name of Christina Yau, who couldn't attend.
Clinical breast cancer physicians actually have been long subsetting [spelled phonetically]
breast cancer, phenotyping it by virtue of the receptors, the estrogen and progesterone
receptor, denoted as HR here and -- or in tumors that are HR negative either as triple
negative or HER2 positive.
And we do this for predictive reasons because we have targeted therapeutics that treat patients
that are HR positive or HER2 positive, but we only have empiric chemotherapy to treat
those that are HR negative.
Well, since the seminal work of Sorlie, Perou and colleagues in 2001, who took the transcriptome
of the breast cancers and came up with intrinsic subtypes, as you see the five different subtypes
here, we see the importance of this is that it's prognostic that we have two estrogen
receptor positive groups that are in the luminal A and luminal B categories that have very
different outcomes.
Luminal A has a very good outcome as shown by the top Kaplan Meier curve.
Luminal B, the poor outcome, HER2 positive and basal is poor outcome as well.
But clinicians do not use this because it still requires fresh or fresh frozen tissues
to do microarray or RNA-Seq type studies to do this intrinsic classification.
So biological and clinical heterogeneity of breast cancer is most evident by these transcriptome
analyses in the intrinsic subtypes; and with a 50 gene classifier, we can now do this,
known as a PAM50.
And in fact, the TCGA data is now annotated both for the clinical subtypes, the receptors,
as well as the PAM50 calls of these five intrinsic types.
With the exception of the HER2 subtype, however, the pathways and signaling properties of -- drive
the other three major subtypes are really still unknown.
These are the basaloid, the luminal A and B. Of course, we know luminal A and B have
estrogen receptor, but we don't know why the outcome is so very different between those
two.
So we wanted to search for pathway differences that might be able to discriminate between
these three intrinsic subtypes, and we employed the pathway inference tool that you've heard
about this morning and yesterday known as PARADIGM.
This obviously integrates DNA copy number and transcriptome data onto over 500, in fact,
508 TCGA breast cancer samples that I'm going to describe here.
Now, Josh has given you a very good overview of PARADIGM and also the novel applications
of this.
We're using the most recent version that's actually now present in fire Firehose [spelled
phonetically].
It's a SuperPathway, curated 1,300 different pathways with about 16,000 different features.
Features represent either proteins, protein complexes or cell outcomes such as G to M
transition or ribosome biogenesis.
And you get -- then a heat map as Josh pointed out before where the samples are on the bottom,
the pathway features are on the vertical, and you can see the clustering of pathway
activities into potential networks.
Sorry.
So our work flow is shown here.
Using the copy number, changes in the expression data, and we then use the inferred pathway
activities derived from PARADIGM.
We use some minimum activity filters to generate from the 16,000 down to 12,000 of varying
activities.
We then used consensus clustering to identify inherent clusters within the path -- inferred
pathway activities, and we compared these to the intrinsic subtype calls from the PAM50.
We used both parametric and non parametric analyses in the ANOVA or Kruskal-Wallis to
look for differences in the pairwise comparisons.
And then in the differentially expressed pathway features, we used both pathway enrichment
and a subnetwork analysis of the SuperPathway where we looked for 10 or more edges.
What you see here is the cluster analysis of the four different feature pathways.
The inferred pathway activities -- there's actually three major ones.
There's one very small one here which I'm going to ignore because of its size, but it
did come out in every significance test.
So pathway one, however, you see this light blue one, and you see now the clinical annotations
for HER2 estrogen receptor status and the PAM50 calls.
What's obviously in the HER -- in the estrogen receptor where it's negative is dark purple.
You see all the ER negative tumors are over on the left side in this dark blue, and you
see that the HER2 positive tumors are scattered throughout, and you see as well that the PAM50
calls are enriched so that there's cluster three down here is largely basal.
Cluster two here is largely luminal B, and cluster one there is largely luminal A and
-- oops.
I keep pushing the wrong button.
So the enrichment is shown here in these diagrams.
The -- when I show you the entire heat map, again, with those enriched activities present,
we can annotate the heat map and show genes that are C-MYC [spelled phonetically] repressed
as well as those that are C-MYC activated.
Of interest, the C-MYC repressed and activated ones are -- indicate that C-MYC levels are
very low in this cluster one which is luminal A and, in fact, in two examples of the C-MYC
repressed genes that are elevated in luminal A represent the two micro RNAs, 146 and led
seven [spelled phonetically] which actually serve to downregulate invasion and growth
pathways in breast cancer, and these have been well validated.
FOXA1 and estrogen receptor activities are seen to be strong in the two luminals and
absent in the basal or the cluster three, and you see the HIF1 alpha and Integrins are
strong in the basal or cluster three as well as co associating with MYC and FOXM1.
Aurora kinase B and Polo kinase three, genes you've heard about before, but these are actual
pathways associated or hubs associated with these pathways that are higher in the cluster
three or basal like.
So -- and there's the enrichment shown in those three clusters.
When we did pathway enrichment by EASE score, we actually found common differences that
were shared by the luminal A and B that differentiated them from the basal like breast cancer; and
whereas the heat map had indicated FOXA1 and estrogen receptor in HIF1 alpha as being higher
in the basals and different from both luminal subtypes, the pathway enrichment score also
identified Gli-mediated [spelled phonetically] hedgehog signaling and HDAC signaling one
which are differentially represented between these luminal and basal subtypes.
Now, when we specifically look at a luminal versus a basal, I'm going to show you luminal
A versus basal but the -- virtually identical with some minor changes.
When I look at luminal B versus basal, you see a strong enrichment, in other words, blue
activity is higher in luminal A; red activity is lower in luminal A or higher in basal.
You see that the HIF1 alpha or Aryl hydrocarbon nuclear translocator hub is very high in basal,
and you see the estrogen receptor, as you would expect, and its complex and interaction
with FOXA1 is very high in the luminal.
When we look at luminal A versus luminal B, a very interesting division because we know
that the outcomes are very different, at least historically, but we don't really understand
the mechanistic differences.
We see that the luminal Bs are very riched in activities with MYC/Max complex as well
as the MYB complex.
In addition, if we look at the luminal A versus luminal B, we also see FOXM1 and polo like
kinase one activities are enriched in the luminal B.
So what was interesting from the TCGA data that confirmed historical data and probably
is the only evidence of survival differences that we've seen so far in the 508 breast cancers
from the TCGA is the significant difference between luminal A and luminal B in the outcome.
Now, these cases would be uniformly treated with hormonal therapy; so I -- and that's
the most effective therapy in these two subtypes.
So I think these are real significant differences.
What we look to see is whether or not the activity hubs that we found differentiating
luminal A and B could also be used to distinguish overall survival and, in fact, they could
as you see here.
The high MYC/Max complex is associated when you lump luminal As and Bs together with worse
outcome.
High FOXM1 is also associated with worse outcome when you lump them together.
And you look at the composition of the green and red survival curves, they're actually
mixed with regard to luminal A and B status.
We didn't have enough cases in order to do multi variant analysis that showed any significance
for luminal status versus MYC/Max versus FOXM1, but it looks like the FOXM1 and the MYC/Max
actually represent reasonable surrogates to differentiate survival between the luminal
subsets.
So in conclusion, we've used unsupervised consensus clustering, based on PARADIGM features,
inferred pathway activities, showing significant associations and enrichments with the intrinsic
breast cancer subtypes.
Pairwise comparisons between the intrinsic breast cancer subtypes identified FOXA1 estrogen
receptor and lower HIF1 alpha pathway activities as being shared in the luminal versus the
basal; or you could say the other way around, the basals have low FOXA1/ER and high HIF1
alpha ARNT relative to the luminals.
This pathway activity also identified MYC/Max, FOXM1, MYB and polo like kinase one as network
hubs with elevated activities in the luminal B versus the luminal A. Two of these show
comparable prognostic value and survival associations in luminal -- as we see with luminal status.
And the SuperPathway analysis may therefore help to identify some pathways and signaling
features between clinical intrinsic breast cancer subtypes and ultimately point to subtype
specific therapeutic strategies.
I would point out that there are clinical studies in progress with polo like kinase
one inhibitors.
As well, there are newly identified chemical entities that are quite specific for FOXM1;
so these would be of strategies that one might pursue to try to get a handle on the treatment
of this poor outcome luminal B type subset.
As well, we have therapies that can attack the angiogenic, hypoxic and oxidative stress
associated network that seems to be coming up in the basal like breast cancers.
So I want to thank you for your attention and acknowledge the UC Santa Cruz team as
well as my Buck co-investigators, our breast analysis working group led by Chuck Perou,
and the entire organization here for everything that's allowed us to do this very comprehensive
analysis.
Thank you.
[applause]
Male Speaker: Questions for Dr. Benz?
Male Speaker: Very nice presentation.
Question.
The luminal Bs -- are they all hormone receptor positive and HER2 positive, or are there subsets
within there that might have something to do with the progesterone receptor and work
proliferation?
Christopher Benz: Probably not with regard to progesterone receptor,
but probably -- I mean, definitely with regard to proliferation.
In fact, the pathologic poor surrogate for a luminal B intrinsic subtyping is KI 67,
and we -- and clinically, the clinicians will use a KI 67 value of over 11 or 12 percent
to designate a potential luminal B or poor outcome patient.
But what I'd like to point out is that based on this analysis, I don't think proliferation
is the only thing that's really being driven in this pathway, you know, so I think that
we need more clues as to -- to get handles on how to therapeutically attack this subset
of tumors.
Male Speaker: I had one.
So there's been some discussion in the literature about autophagy and its linkage to poor prognosis
in breast cancer.
I was wondering whether in your analyses you saw any evidence of autophagy genes?
Christopher Benz: Well, it didn't come out as a feature, but
I will say that our analysis was filtered so that we looked for features that had a
10 or more interconnections.
So some of the things in the SuperPathway that are out, perhaps not as well annotated
pathways, you know, may still reveal these kinds of things; and obviously, as you heard
this morning, PARADIGM's a sort of a work in progress.
And at this present time with -- you know, has about 1,300 different curated pathways
involved in there, but that's growing; so hopefully we'll get more answers like that.
Male Speaker: Okay.
Good.
Thanks very much.
Please join me in thanking the organizers and the speakers for the morning session.
[applause]
Male Speaker: Coffee break, I think.