Thomas Giordano: Thank you very much.
I'd like to thank the Planning Committee for the opportunity to present, on behalf of the
Thyroid Group, our work today.
So, today we're going to talk about the view of the data from 30,000 feet, but before I
do that I felt obligated to give a little background.
So I was thrilled when the TCGA announced that they were going to do thyroid.
Thyroid cancer can sort of get shortchanged in the big scheme of cancer.
I think one of the reasons that they did is because thyroid cancer is clearly on the rise.
However, the mortality of thyroid cancer is not that high.
It's actually -- over the same period, has actually trended down flat and now is rising
up.
But, clearly, it's one of the few cancer types that is actually increasing in incidence.
So, I'm a professor of pathology so I have to show some histology -- no, just kidding.
This is important because the papillary carcinoma, which is what our project's on, has three
main types, and it'll come through in the data so I really wanted to spend just a minute
to express this.
So, papillary carcinoma gets its name from the tumor on the left here, which is a tumor
that has finger-like projections, or papillae.
But not all papillary carcinomas are actually papillary in terms of their architecture.
There's a group here that we recognize that are called the follicular variant, not to
be confused with follicular carcinoma, but the follicular variant of papillary carcinoma.
And then there's a third main type, the tall-cell variant, in which the cells are more columnar
than cuboidal.
And so we recognize these three variants, and it's kind of important.
We know from lots of papers that these have different genetic profiles and different gene
expression profiles, and that will also come out in our dataset.
These are the common genetic defects in thyroid cancer.
Now, we're just looking at papillary carcinoma.
This whole chart shows the whole spectrum of thyroid cancers, but it's worth looking
at.
This one on here is post-Chernobyl, so radiation-induced cancers.
So, RET rearrangements are common in papillary carcinoma, even more common in radiation-induced
areas.
BRAF mutation, a very high rate of V600E mutations; a small rate of BRAF rearrangements, which
we'll see.
NTRK rearrangements.
RAS mutations are quite interesting.
So, notice it goes from zero to 21 percent, so depending on the cohort of papillary carcinoma
you'll get different numbers, but we definitely know that the follicular variant is enriched
for RAS mutations, just as follicular carcinoma.
PAX8 -PPAR gamma root rearrangements are thought to be present mostly in follicular carcinoma,
but there's been a few recent studies that show that they can also occur in some papillary
carcinomas; again, the follicular variant.
And then β-catenin and TP53 mutations are sort of things that occur down here when tumors
show histologic progression to poorly-differentiated and undifferentiated carcinoma.
So that's the genetic landscape.
There's a big issue for -- a big opportunity, a big issue for the TCGA.
So, if you genotype thyroid cancers, differentiated thyroid cancers, you only find one of these
driver mutations in about three-fourths of the cases.
So we don't know what that other mutation -- what those other mutations are.
So, this project is all teed up to try to find those other mutations.
And this is important, not just for understanding of cancer biology, but there are already cancer
diagnostic tests on the market based on genotype.
So if we can expand the genotypic universe of papillary thyroid cancer, those tests will
get better.
So, again, this is the first look at the data.
We did a data freeze about -- just less than a month ago.
Much of what I'll show you today was generated by the Firehose platform.
We're really just getting started, and Gaddy wanted me to stress that much of this is not
been validated.
Here's the sample counts.
We're pretty far along in the project, and the remaining cases are sort of in the pipeline.
So we're thinking we might just wait until we full it out to the full 500, but we're
still discussing that.
Here's some of the clinical data.
Thyroid cancer occurs in a younger population.
The mean age is 46.
You know, I was sort of envious when I saw those survival plots.
That's really not so easy in thyroid cancer; there's not a lot of deaths.
To do a good outcome study in thyroid cancer you sort of need 15 years of follow-up, and
so we're not going to have too many Kaplan-Meier plots anytime soon.
You can see we've had one death so far.
Like in all endocrine diseases, way more common in females, almost three to one.
And we have a good breakdown of the different histologic types.
So here's some of the data.
So, again, I want to stress that papillary carcinoma is a differentiated tumor, and overall
it has a low mutation rate.
So here's the mutations per sample.
It's generally uniform across here, but you can see there's a few samples that are jumping
up and showing that they have a much higher mutation rate.
The sequencing coverage is outstanding.
And then down here we can see the rate is quite low.
I'm pleased someone showed the plot earlier.
I don't know if anyone looked.
I looked for thyroid.
It was definitely on the left end of that plot, away from the head and neck squamous
cancers.
So it's a low mutation rate; not surprising because it is such a well-differentiated tumor.
So this is a great slide that integrates a lot of the data, and I'll spend a little time
going through this.
This shows the common mutations, with BRAF here representing 57 percent of our cohort.
We have some RAS genes, and notice the BRAF and the RAS genes and the fusions down here
are all mutually exclusive, which we've known in thyroid cancer, the thinking being that,
you know, having more than one of these doesn't add any biological advantage.
The histologic data is up here, and so it's hard to see, so I'll explain it.
So the BRAF tumors are enriched for the classical type and the tall-cell type, which is consistent
with the literature.
The RAS mutant tumors are almost all the follicular variants.
Again, this is consistent with what we've known.
The tumors with the fusions are mixed between the classical type and follicular type.
And then these so-called wild-type tumors, where we don't know the driver mutation, have
really a smattering of histologic types.
So immediately we're seeing a very strong correlation between genotype and histologic
type.
And so that gives me a lot of confidence that this is a quality dataset.
We can see the fusions, as I mentioned briefly, are here.
RET fusions are the most common, but we're also picking up some PAX8-PPAR gamma, NTRK,
and a few BRAF fusions down here.
And, again, mutually exclusive with BRAF RAS.
I want to bring your attention to this point right here, which is this initiation factor,
and it's also mutually exclusive with these other common mutations and the fusions.
So that's telling me -- suggesting to us that this is a biologically significant mutation
even though we have not validated that.
And then the copy number changes are shown here.
Notice there's -- across the BRAF cohort there's not a lot of copy number changes.
Across the follicular variant or the NRAS cohort, there's not a lot, but notice there's
a band right here that represents chromosome 22.
So there's an enrichment for a loss of chromosome 22 in these RAS mutant tumors.
The tumors with fusions are also pretty quiet.
And then the remaining 20 percent, the so-called wild-type tumors, can be divided into two
groups: those with more changes and those that are pretty silent.
So the first thing that I want to do as a pathologist is I want to go back and look
at these tumors over here that don't have a lot of changes and just review their pathology
because, truth is, endocrine pathologists, we fight over diagnoses a lot, so we need
to look at that.
There's also messenger RNA differences that we know, some genes here, and then microRNA,
which I'll show a little bit more.
So this tells a very compelling story, this one slide, that integrates much of the data
and shows that this is a quality dataset.
Yes, we've replicated some things that we've known, but the integration of all the data
in this setting I don't think anybody has come close to doing.
So this is that new potentially novel mutation, this X-linked translation initiation factor.
It's very interesting.
There's no known role in thyroid cancer, and, in fact, we could find just one other synonymous
mutation in the COSMIC data base.
So this will take validation, but it's certainly an interesting finding.
Fusions -- I showed some fusion data.
Truth is, we're still going through this, there's much work to be done on fusions.
This was some data from an earlier analysis that showed this fusion right here, which
is, in the business, called RET PTC1, which is the most common version.
And then we have some more novel things.
This ETV6, NTRK3, which, in speaking with Jim Fagin, he's validated this in an independent
cohort from the Ukraine, both in radiation and non-radiation cases, so we think this
is a real finding.
And then PAX8-PPAR gamma as we'd expect in a few cases.
Onto the methylation work.
The methylation profiling identifies four classes of tumors that generally correlate,
again, with histologic type and mutational status.
So we can see there's two groups over here on the left that are mostly the classical
type and the tall-cell type, and they are enriched with BRAF mutations.
And on this side, which is more similar to the normals, we have an abundance of the follicular
variants and tumors with RAS mutations.
So the methylation work is integrating nicely into the same compelling story.
A few interesting molecules, miR-21, which we heard a little bit about this morning,
and miR-146b, both been worked on in thyroid cancer but not at the methylation level.
And here we show inverse expression between methylation and expression, so these are interesting
leads for us to work on.
And then, as a thyroidologist, we always like to look at thyroid-specific genes.
Not because they tell us that much about the cancer biology, but more they give us thoughts
about progression and possibly a loss of radioactive iodine treatment.
And so that's a big issue in our field when tumors become a little less differentiated
and no longer respond to radioiodine.
So, here we can show one gene, thyroid peroxidase, that actually shows a differential methylation
profile based on the different types, classical follicular and tall-cell.
And where from my group and others, we know that TPL goes down in BRAF mutant tumors compared
to the follicular variant, and, sure enough, methylation is playing a role here, again
inversely expressed between methylation and expression.
So that's a potentially interesting story, and we need to look at other genes in the
thyroid, like the sodium symporters, sodium iodine symporter, and others.
MicroRNAs; we can use various tools to cluster these.
Here's a cluster driven by miR-21, so we can get four types or seven types.
And notice the four types is actually showing some, again, correlation to histologic type,
which we know reflects genotype.
So I think the microRNAs will fit in nicely.
This needs some work though.
If you look at the British Columbia software versus Firehose, you can generate different
clustering algorithms and then actually compare them head-to-head and start to make some sense
of how much you believe them.
This is kind of interesting.
So, this is the British Columbia software versus Firehose, four groups versus three,
and you can see some cohorts line up nicely here and other cohorts get sort of scattered
into others.
So, we have some work to do to try to figure out what's the most meaningful way to look
at the microRNA data in a global sense.
But, clearly, there's some useful data in there.
And then cancer regulome.
Lisa Iype was kind enough to prepare some slides for us in which they look at all the
factors, all the measurements throughout the dataset, and we just started with something
simple, like histologic type, and she was able to show that there's many, many, many
associations shown here.
And, sure enough, some of the more interesting ones are -- is miR-21, which I just showed
you had differential methylation expression, and here's BRAF.
So these are all potential leads and we'll certainly be using some of these software
tools which are at our disposal.
So, in conclusion, I would argue that we're making good progress.
I think that we're progressing as planned.
I think the cohort is outstanding and truly representative of the disease.
I make this point because that's not true for every paper in the literature.
People cherry-pick cases, and what they're publishing is not representative of the broader
disease.
So I really do think this cohort is accomplishing that.
We have a low overall mutation rate, with few copy number changes, but we have a few
tumors that have increased copy number changes, which we'll spend more time working on.
And we've uncovered and reproduced strong associations between the tumor morphology,
its genotype, its gene expression profile, copy number changes, and methylation status,
and we've uncovered many interesting, novel leads, mutations, and gene expression patterns
to keep us busy for quite some time.
So there's much to do.
We still have to decide whether we're going to fill out the whole cohort or publish where
we're at now, but I do think we're on track for our first paper in the middle of next
year.
As with all the projects, there's many, many people to thank and I would hate to go through
these individually, because I'm sure I've left people -- some people out, but, clearly,
I'd like to thank Gaddy Getz, who I've not met yet.
So Gaddy, if you're here, come up and introduce yourself.
But thank you very much.
[applause]
Raju Kucherlapati: Thank you very much.
You think that the fact that so many of them have BRAF mutations would suggest that the
BRAF inhibitors might be a therapeutic approach for them?
Thomas Giordano: Well, you know, I wish -- you know, maybe
Jim Fagin can answer that.
He's more in tune to the clinical trials.
There's certainly many clinical trials ongoing.
Steve Sherman, at MD Anderson, is working on that.
But I've not heard of -- it's not distilled down into, like, this home run where people
are ready to give up radioactive iodine.
Matt?
Matthew Meyerson: Sure.
So just a couple questions and then a comment.
My first question is about the relationship between, you know, thyroid carcinoma and lung
adenocarcinoma, because I think there are a number of similarities.
First, the major -- you know, as you very well know, the major marker and one of the
leading amplified genes in lung adenocarcinoma is the thyroid transcription factor-1 gene,
you know, suggesting potentially some common etiology.
And, you know, there's also some --
Thomas Giordano: And there's also RET rearrangements, too,
that are common.
Matthew Meyerson: There's RET rearrangements, BRAF, and RAS
mutations, and I'm wondering if you've started to look at commonalities between the diseases
or have thoughts about how to do so?
Thomas Giordano: You know, certainly that would be a good comparison,
but there's some jump -- things that jump out, like there's very few EGFR mutations
in thyroid.
So I think the similarity is there, but I don't know how strongly it'll hold up.
Matthew Meyerson: And I think my second thing is more of a comment,
which is I think if the rest of the rest of the cancer community could do as well as the
thyroid cancer community has done, we probably wouldn't need TCGA or a National Cancer Institute,
so we're very struck by the survival data.
Thomas Giordano: Yes, yes.
Well, that's not to belittle thyroid cancer.
Most of the deaths, though, are actually in those poorly differentiated and anaplastics.
If I showed those survival plots, the survival plot for anaplastic is measured in six months.
So, thyroid cancer represents the whole spectrum, and I'm hoping that we're going to get to
a more aggressive thyroid cancer project, which will then nicely integrate right into
this, because we have many cases that have both papillary carcinoma sitting right next
to anaplastic carcinoma.
It'd be nice to profile both of those in parallel.
Thank you, Matt.
I don't know if there's --
Stephen Chanock: Can I ask one last question?
Thomas Giordano: Yep.
Stephen Chanock: Thyroid cancer, you know, is one of the most
interesting ones with respect to heritability and familial syndromes and, you know, sibling
risk.
And, you know, it's been in the past discussed that the outcome may be related to whether
the early-onset, family-driven cases versus the more sporadic later ones.
When you look at the dataset you've put together, are you able to, in any way, parse out, or
do you have information on the heritability, in terms of family-related cases or the like?
Thomas Giordano: So we have to separate, obviously, medullary
thyroid carcinoma --
Stephen Chanock: Right.
Thomas Giordano: -- which has a much stronger familial association
than, you know, sort of follicular-cell thyroid cancers.
I know the group at Ohio State has worked on this.
There's some germline mutations of, I believe, TTF-1 that, you know, make you susceptible
to papillary.
So there are familial cases of familial papillary carcinoma, but it's not nearly as strong as
medullary.
That's something that I think we'll get around to looking at, but it's not something that's
going to jump out at us right away.
Raju Kucherlapati: Thank you.
[applause]