Rachel Liao: Thank you.
Good morning.
My name is Rachel Liao, and I'm a graduate student in the laboratory of Matthew Meyerson
at the Dana-Farber Cancer Institute and at the Broad Institute.
And I'm very excited today to tell you a story, which I believe is a success story, of use
of the TCGA data analysis that we all do, and actually translating it to clinical insights
that are affecting patient care and improving patient care, even today as we speak.
Last year, I believe, Peter Hammerman was here and told you about the -- where the TCGA
squamous cell carcinoma of the lung project was at that time, and I believe he told you
that prior to the TCGA study, the squamous cell carcinoma of the lung was a disease really
without treatment options.
It's the second most common subtype of lung non-small-cell carcinoma, the most common
of which is adenocarcinoma, which has much larger percentage of the -- of the population
of people who have non-small-cell lung cancer.
But because in the last 10 years, adenocarcinoma has undergone a lot of study, there are many
targeted therapies and advances that have been made for patient care, such that many
patients with this disease undergo genotyping and are able to be put on to targeted therapies
that will actually target the genomic events that are underlying their disease proliferation,
while, at the same time, squamous cell carcinoma has had few targets and no targeted therapies
that have been elucidated, and so every patient that was put on -- that was diagnosed with
squamous cell carcinoma of the lung was put onto the same chemotherapy radiation treatment,
regardless of the underlying genetic components.
Now, this TCGA preliminary analysis, or the first -- the first pass of the analysis was
published, I believe most of you are aware, very recently, a couple of months ago.
And I'm going to tell you about some of the observations we've made in that dataset that
have actually influenced patient care for the better.
Now, the FGFR family is a receptor tyrosine kinase family which has been implicated in
other cancers and in other diseases.
The events that exist in the TCGA lung squamous cell carcinoma dataset are about 10 percent
focal amplification of FGFR1, which has been observed before, and about 8 percent mutation
frequency across the four family members, the four receptor family members.
That includes about 3 percent in FGFR2 and 3 percent in FGFR3, and those are the two
genes on which I'll be focusing this morning.
Now I think it's important to make the point that just observing a mutation does not necessarily
imply that it's functionally relevant, and so a lot of the functional work that I did
was, in fact, demonstrating the potential for some of these events to transform and
to potentially contribute to uncontrolled proliferation, and hopefully also therefore
be targets that we can target in the clinic.
Now I think it's also important, in this audience in particular, to make the note that this
is not -- neither of these genes are significant.
They're not significantly mutated in this dataset by a large margin, and we believe
that a lot of the reason for that is because the mutation frequency in lung squamous cell
carcinoma, as you've heard before, is extremely high, and so the actual objective number of
mutations that we observe is relatively low compared to that frequency.
But because of our prior knowledge of these genes being implicated in cancer in the past
-- you heard from John this morning that FGFR3s are important in bladder cancer; I believe
you'll hear later this morning that FGFR2s are relatively commonly observed in endometrial
cancer -- we believe that this was a family of receptors that may very well be an important
target in a subset of patients with lung squamous cell carcinoma.
Now these are the events that I studied in FGFR2 and FGFR3.
You can see them across the length of the protein, from the N-terminal on the left to
the C-terminal on the right, and you can see that the Ig-like domains in orange have mutations
that cluster basically between the second and third Ig domain here in FGFR3 and a little
bit more into the Ig3 domain in FGFR2.
These are extracellular, the structured domains in these proteins.
They facilitate ligand binding and dimerization of the proteins to activate the proteins.
And then you can see these two mutations at the residue K660.
Both fall in the kinase domain, have been described previously as being activating mutations
in the kinase domain.
You can also see that there are other mutations that are outside of structured domains, and
then this one mutation, which, when mapped to the protein structure, does not fall close
to a part of the kinase domain that would -- that would suggest that it was involved
in the kinase function.
And I believe that that is, again, not entirely surprising.
I'll show you data that suggests that these might be passenger mutations, and I think
because mutation frequency is so high, we would expect to see some passenger mutations
even in these genes, which are implicated in the proliferation of the disease in other
patients.
So this is a plot that demonstrates that, essentially, there are not repeatedly co-occurring
events with any of these FGFR mutations except for TP53 mutations.
You can see that each row is a -- is a lung cancer sample in the TGCA dataset, and then
each column is the event.
So on the left, you can see here there are amplifications and deletions that are common
in this -- excuse me -- this squamous cell carcinoma dataset.
And then on the right are mutations, which, black are indicating missense and yellow or
orange are sort of the nonsense, frame shift, splice-site mutations that we presume to be
loss of function.
And what you can see here is that the FGFR2 and FGFR3 mutations are indicated P53, which
occurs at about 80 percent frequency in this dataset, occurs at about 80 percent frequency
in the number of samples that are mutated with FGFR mutations.
But then the rest of them are just a smattering of other events that happened to co-occur
in those samples, and again, because the mutation frequency is so high, that's not surprising
that we would see many other events in these samples, but nothing that specifically is
implicated as another potential driver in a large majority of cases.
So the first experiment that I wanted to do was determine whether, in my hands, I could
demonstrate that any or many of these mutations were, in fact, able to drive proliferation,
were what we would call "transforming" in a -- in a transgenic model system.
And so what I did was introduce each individual mutation into a cell line that's immortalized,
but is not transformed and cannot proliferate uncontrollably on its own, to see if the simple
addition of this mutation, of this genetic mutation, would be able to overcome the cells'
inhibitions toward uncontrolled growth and actually just drive its proliferation uncontrollably.
So what you can see here is graphs that indicate the -- essentially the number of colonies
that were able to grow in suspended media, basically in an agar media, that do not anymore
require the -- sort of the contact that cells that are adherent require in plastic in order
to grow.
So you can see that several of these mutations form colonies at far greater rates above the
wild-type, which is indicated about the third from the right or the second from the right
in each of these -- in each of these plots.
You'll see that I have two different isoforms of FGFR2 mutations represented in these experiments.
That's because there are many fewer studies that are done on the isoform differences in
these mutations in particular, in FGFR2; whereas in FGFR3, I used only one isoform because
the two mutations that you see here that are transforming have been well-studied in bladder
cancer in both ise forms.
You can see the two mutations that were in unstructured domains did not form colonies
above wild-type, indicating that they are not, in fact, potential drivers, at least
from this experiment, are not potential drivers of this disease.
So you can see that there are actually four mutations out of the six in FGFR2, which were
-- which were transforming this assay.
There were also four events, three of which were at this S249C event in FGFR3, so four
out of six events that we observed in each gene were transforming in this assay.
And the next thing I asked was whether I could actually reverse this transformation, block
this transformation by the addition of FGFR inhibitors, and I'm showing you those data
here.
So this is the same experiment as the one on the previous slide, in which cells expressing
these individual mutations are seeded into an agar solution, which precludes their growing
on plastic.
Essentially, it requires them to have some transformed potential in order to proliferate.
And so I only used the mutations in this experiment which were, in fact, transforming in the previous
experiment, and I seeded them into increasing concentrations of drug, such that we would
be able to determine whether the addition of drug could block the transformation phenotype
that we saw.
And that's exactly what happened.
You can see over here the different concentrations of these two different drugs that were -- that
were used in this experiment, and I did this experiment with a few more drugs, but these
are two of the drugs that I had used.
And you can see in the case of AP24534, also known as ponatinib, which is from ARIAD, you
can see that there's loss of colony formation across many of the mutations by between 10
and 100 nanomolars of drug, which is quite low.
And in the case of BGJ398, which is actually a specific FGR inhibitor from Novartis, you
can see the loss of colony formation in all of the cells expressing mutations is at 10
nanomolars or lower, while in the case of an EGR transforming mutation, you're still
-- I'm still seeing colony formation at 10 micromolars of drug.
So this was very encouraging to see, and also is confirmed by western blotting that I did
to demonstrate that -- the loss of transformation that I saw in the colony formation assay that
I just showed you also correlates with loss of phosphorylation biochemically.
So you can see here that FGFR2 is expressed in the top two images, and then FGFR3-expressing
cells are in this bottom image.
You can see that phospho-FGFR decreases very significantly at 10, or 30, or 100 nanomolars
of drug, and this is just in the presence of ponatinib.
And phospho-FRS2, which is also a downstream target of FGFR phosphorylation and regulates
a lot of the downstream signaling effects of the FGFR family members, is also inhibited,
demonstrating that this drug is, in fact, targeting the pathway correctly, and is also
doing what we expect that it's doing in the previous experiments.
So I have one slide on the next set of studies that I did, which was a dependency study.
I introduced these mutations into a cell line in which I could actually generate dependency
on this pathway through the introduction of these mutations, so that I could actually
study the dependency on the pathway in the context of a panel of different inhibitors.
And many of you who saw my poster yesterday saw that I actually have eight inhibitors
in this panel, and I'm happy to send you a download of that poster if you're interested
in those data.
And essentially what you see here is that cells that are dependent on FGFR events are
extremely sensitive.
IC50 values are plotted down here for an easier time of looking at them.
Cells that are expressing these FGFR events are extremely sensitive to FGFR inhibitors,
while cells expressing an EGFR event -- dependent on an EGFR event or parental cells are not
sensitive by several orders of magnitude.
Now we -- I've demonstrated to you a couple of cell-based models that are certainly not
-- they're not, by themselves, clinically relevant.
They're not disease models, per se.
They're cell models.
But we also have a clinical case that's an interesting case that demonstrates a lot of
these -- that these data are hopefully on the right track to modeling patient disease.
And this is the case of a head and neck squamous cell carcinoma patient, which, of course,
is not a lung squamous cell carcinoma, but as you heard yesterday from Neil Hayes, the
HPV-negative head and neck cancers genomically are extremely similar to lung squamous cell
carcinoma.
And this patient was found to have an FGFR2 mutation, which has been observed previously
in bladder cancer, which is known to be activating.
Now, this patient was put onto ponatinib -- or I'm sorry, was put onto pazopanib, which is
a multi-kinase inhibitor which is approved for soft tissue sarcoma treatment, but which
is known to have FGFR event inhibitory properties.
And this patient, after two weeks on pazopanib, went from having this lung metastasis, which
is very large, on his right -- or I'm sorry -- this neck metastasis on his right neck
was very much reduced and responded very effectively to this treatment, and we believe that this
is because of the FGFR-targeted therapy that he underwent.
So just in conclusion, as I told you, these FGFR events that we've observed, they're not
significant in the actual computational analysis, were in fact quite significant biologically,
and that we've been able to model transformation potential and also dependency and sensitivity
to targeted therapy.
And a clinical success has confirmed that this may, in fact, be a very good target to
pursue for squamous cell lung carcinoma.
And, in fact, a clinical trial at the Dana-Farber has just been approved with ponatinib for
lung squamous cell carcinoma patients that present with FGFR events, and we're hoping
we'll be able to extend that to head and neck carcinoma patients also.
So it's been a very real clinical translation of the TCGA data that we've all been working
on analyzing to the clinic and to benefiting patients in a real way.
And so with that, I'd like to end.
Thank you -- thank you very much, Matthew Meyerson, and also Peter Hammerman, who have
been very supportive in this project.
And I'm happy to take any questions.
Thank you very much.
[applause]
John Feinstein: That's a really interesting study.
Questions?
Lou.
Lou Staudt: So that's -- it's exciting, but there's a
challenge, then.
You have a diversity of mutations, and some of them are passengers.
So you get a new patient in the clinic, shows up with a mutation.
Do you treat him or not?
Rachel Liao: I think that's a very good question.
And not being a clinician, I won't speak to how to make the decisions to treat patients,
but I believe that with more study, we'll be able to identify more events that are known
to be drivers with algorithms that enable to perhaps infer the function of different
mutations.
So I think you're right, but it's not going to be an exact science of which mutations
will respond and which won't.
Lou Staudt: But you are a biologist, so you can rapidly
make that mutant, put it into a model --
Rachel Liao: Right.
Lou Staudt: -- and show it's transforming.
Rachel Liao: It's true.
Yes, we can.
John Feinstein: Last quick question, quick answer.
Male Speaker: Rachel, what about the amplification events,
which are much more common than mutations?
Any sense as to whether they're drivers from any of your data?
Rachel Liao: So I think that -- not from these data but
from other studies that I and others have done, we do believe that a subset of tumors
that present with focal FGFR1 amplifications are, in fact, being driven by FGFR1 signaling
events.
It's unclear exactly which tumors those are from just patients presenting in the clinic,
and it's certainly clear that not all patients will respond to FGFR therapy.
So I believe that that is, in fact, another avenue down which we can really make some
strides in understanding patient response and potential therapeutic options for these
patients, yeah.