Louis Staudt: So it's been a pleasure to work with Steven,
and we divided this up where he would be the rah-rah pep talk for "Go team," and I would
give an example from some of our own work that tells us, by example, what we could expect
genomics can offer in the future to making cancer go away, which is, of course, our ultimate
mission.
And we've seen this -- I borrowed this from Kenna, the pipeline of TCGA, and the part
that I'm going to emphasize is some new boxes that we'd like to put in.
That is, using clinical data in a very direct way, both the phenotype, the response is survival,
and putting that as part of the integrated computational analysis with the goal of changing
clinical care.
Developing predictive markers of response to a drug, prognostic markers we already heard,
is coming out of the TCGA.
And then identifying new therapeutic targets.
And, of course, TCGA has had the requirement to get some clinical data, but this is more
to actually putting the type of machine that is TCGA integral into a clinical trial.
And I think that's the lesson that I'm going to drive home.
Very quickly, I worked for many years on diffuse large b-cell lymphoma; it's the most common
type of non-Hodgkin's lymphoma, and we can cure about half these folks.
But we were unfortunately left with this current situation of finding new therapies for the
other half, and I was interested in figuring out who was being cured by our current therapy,
and that led to a subdivision based on gene expression profiling into three large groups.
The ABC group, activated B-cell like, which we'll talk about a lot today -- about 40 percent
of cases.
The GCB, or germinal center, about 43 percent.
And a minor subgroup, primary mediastinal lymphoma.
And it certainly is the ABC tumors that have the worst survival -- or predict the worst
survival, so we're maybe curing 35 to 40 percent of these folks.
But we're doing much better, not good enough, in GCB lymphoma at 75 percent.
Now early on, just looking at profiles, we saw a signature of NF-?B activity, and this
signaling pathway turned out to be essential for the survival of these lymphoma cells in
culture, but we didn't really know why.
And what we did was turn to functional genomics, RNAi screens, and developed over the several
years the idea that it's entire pathway leading from the B-cell receptor on the cell surface
to NF-?B with the component shown here.
And the first hit in our screen was a complex -- a signaling complex -- involving CARD11
MALT1 Bcl10.
Maybe not so familiar to this audience, but really well-known to immunologists.
And then by resequencing in a candidate way, we found that 10 percent of the ABC tumors
had mutations that explained this requirement for CARD11.
That is, these mutants were constitutively active when you put them inter a heterologous
cell; they spontaneously turn on NF-?B. So that seemed to explain 10 percent of the problem.
The other 90 percent of cases had wild-type CARD11, but we had examples in the lab where
they still relied on upstream signals from the B-cell receptor.
In particular, if we knock down a kinase BTK, they really didn't like it.
But we sequenced all the kinases in the pathway, and there were no mutations, and, ultimately,
we found that it was the B-cell receptor itself, that when you disturb any component of that
receptor, it -- the cells died.
And you could actually see this.
Seeing is believing, so here is an image of the B-cell receptor in the membrane of the
ABC lymphomas, and these bright red dots are what you see when the B-cell receptor is actively
signaling in micro clusters.
And you don't see that in other lymphoma types.
Now, given this clue, we directly sequenced the various subunits of the B-cell receptor
and found, in 21 percent of cases, mutations that helped us understand this dependence
on the B-cell receptor.
They occurred in two of the signaling subunits, CD79B and A, and the affected tyrosine residues
that were very important for that signaling.
Now these were not as -- the same as the CARD11, they -- I would call them backseat drivers.
If you put them in a heterogonous cell, nothing happens, but within the context of an ABC
tumor cell, they turn up the volume on B-cell receptive signaling.
Nonetheless, genetically, this told us that this pathway was important.
Now more recently, it got complicated in an interesting way where we came -- yet another
pathway that separately leads NF-?B activity, and that is driven by a signaling adaptor
called MYD88.
And I won't go into great detail about that, but we did find in 39 percent of this tumor
type, activating mutations within one domain of MYD88 that spontaneously, again, in a heterologous
cell, will turn on NF-?B. So if you look at this wiring diagram, this makes some conclusions
about where you might want to intervene therapeutically.
Obviously, something in the B-cell receptor pathway might be helpful, but would the MYD88
pathway somehow be redundant in a parallel fashion so it would compensate if I inhibited
just the B-cell receptor pathway.
So here's a clue from cancer genetics, and it's a point that I want to drive home also
at the end, that we can get information about pathways, biological pathways, simply by looking
at the raw genetics and the co-occurrence or co-exclusion of mutation.
So here we found that, as I mentioned about -- oh, I didn't mention this.
There's one point mutation in MYD88 that is the most potent one.
It's called L265P, and it's found in 29 percent of cases.
So you'd ask, do mutant tumors with that L265P MYD88 mutation, do they overlap just by random
with tumors that have CD79B or A?
And, in fact, we find not, that there's a definite, statistically important enrichment
of those two suggesting maybe they aren't sort of redundant, leading to the same goal,
but could be cooperating.
So that led to a clinical trial, these kinds of findings, with a drug targeting the Bruton's
tyrosine kinase.
And it's a nice drug because it covalently modifies the protein, completely inactivates
it for the duration of that protein's lifetime.
And we had found that it was very potent in killing our ABC cell lines in vitro, but did
not kill other lymphoma cell lines.
So it had pretty good specificity.
So at the NCI, with my colleague Windham Wilson, we did a short trial where we enrolled just
patients with this ABC type of lymphoma, and looked at 10 patients and treated them with
this drug you take once a day by mouth.
And it has apparently no side effects that are significant for most patients.
And I'll give you two vignettes to show the power of this drug and something about the
genetics.
So this woman had ABC tumor.
She had in her tumor the CD79B mutations, MYD88 was wild-type, and she had failed -- this
is all in relapsed refractory settings.
So she had failed multiple therapies and went into a complete remission by week eight.
The arrows point to some PET positive tumors that went away at week eight, and she is our
star.
She's come back to see us now 2.2 years later, has no sign of disease, is taking this drug
once a day by mouth, no side effects.
And she's a happy camper.
So we show -- I'll show the second slide here.
A 59-year-old woman, wild-type for CD79B, no discernible genetic mutation turning on
the B-cell receptor pathway.
She had had a very primary refectory disease, very difficult to treat.
Her LDH in the blood was rapidly rising when we saw her.
She had massive abdominal disease and very -- went into a very good, but partial, remission
that she stayed in for only a month and a half.
But during that month and half, she felt great.
So this is the story of targeted therapy as we know.
So I'll now give you a preliminary analysis, but of a completed clinical trial, where we
had 70 patients treated.
Here we took all comers.
All DLB CL types, and used profiling to figure out their ABC or GCB.
Seventy patients.
And our hypothesis was ABC patients should respond better, and that's what we saw.
So 41 percent of the tumors of the ABC type responded; only 5 percent of the GCB.
So this tells you that molecular profiling works in the context of a clinical trial.
But -- and, oh, this just shows you more profound loss of tumor volume and, actually, extensive
sense of survival.
Our patients are going out.
This is still, as you can see, needing more follow-up, maybe heading past a year here
for some of the patients.
So, for this audience in particular, beyond profiling, can genetic lesions identify the
responders or not?
Well, sort of.
So here we have the CD79B mutation.
Small numbers, five out of seven of these folks responded, somewhat of an enrichment
over the overall response.
That's fine.
But look at this very healthy response rate in tumors with wild-type CD79B.
So this tells you right away that you -- it's not going to be as easy as the stories with
the B-ref and Vemurafenib.
It is not always mutation equals drug response, and then no mutation, no drug response.
This is a gray area, and I believe a lot of what we're going to see.
So then what about all those MYD88 mutant cases?
Well, if you had both a MYD88 L265P and a CD79B, you had a four out of five chance of
responding in this analysis.
A finding was that if you only had the MYD88 L265P, only zero -- you know, four patients,
but none of them responded.
And then, as predicted, if you had a CARD11 mutation, that is downstream in the pathway
and you have a drug that's upstream, no response out of four patients.
Now we did -- we started to do an unbiased analysis of exome sequencing within this,
and we had already noted that there was one very common lesion, which is the lesion of
the INK4a/ARF locus, specifically in this lymphoma type.
And in red, you can see that those -- even within this already bad group, are the bad
tumors.
It's a finding.
Five out of eight patients with that had responses.
Zero out of seven without that had a response.
Small numbers, and we need to -- so where am I going with all of this?
So a couple conclusions of how we can extend TCGA into this arena.
First of all, I think I've learned that there's value in finding co-occurrence and co-exclusion
of mutations.
And I actually -- if you sit down and ask your statistician to do the math, which I
did yesterday, it turns out that if you want to drive this kind of analysis into the 5
percent and 2 percent range, you need 10,000 tumors.
Actually, sometimes you can't even get there with 10,000 tumors, but if you want to see
that 5 percent overlaps with 5 percent in a significant way, that's the number.
And I think that would be a way cool project, but we've got to come up with 10,000 -- Kenna's
trying to get 500, so we've got to get 10,000.
But I think there's real value in this genetic analysis.
Second point: Take a pathway-centric view of the genetic lesions.
Look at both gene expression signatures, so don't forget about the phenotype, but also
then use the genotype as well.
And we may have to go do pathway modifications.
And finally, we have to drive this to actual clinical utility.
We have to make the predictive tests that are based on our genomic methods, make them
available.
And something that I think Barbara was the first to put forward, the idea of a cancer-genome
commons where people would freely donate their cancer genome into a large database along
with their response to whatever treatment they had, and then any new patient might be
able to sort of dial in their genome and see where they fit.
So this is where we'd like to go.
So -- and it takes a team.
It's a different team than TCGA, but it takes a team of researchers, both clinical and investigation,
to pull this off.
So I believe that the kind of integrated analysis that we're going to hear about in the next
two days is exactly what has to be brought into -- right into NCI clinical trials.
And that's one of the things we're going to be discussing at NCI Central.
So, thanks a lot.
[applause]