Laila Poisson: Good morning, and thank you for the opportunity
to present my work this morning.
My presentation is on the imaging genomic analysis of TCGA and TCIA diffuse lower grade
gliomas by molecular subtype.
And this work is a collaborative effort with the Cancer Imaging Archive TCGA Glioma Phenotype
Research Group.
So since we’re a diverse group of cancer researchers and we each have our own tumor
specialties, I thought I’d start with a few fast facts on brains tumors.
So there are over 120 types of brain tumors, but today we’ll focus on a subset known
as glioma.
This year it is expected that over 66,000 cases of primary brain tumors will be diagnosed,
and about 30% of those will be glioma.
So that’s just over 19,000 cases.
Glioma makes up about 80% of all malignant tumors, and as it indicates here, there’s
only four FDA treatments approved.
And treatments affect varies as we’ve seen with all of our cancers.
And here we can see survival will actually range between less than two years, for some
cases over 15.
So glioma is a cancer of the glial cells, so not your neurons that’s transmitting
the signals in the brain, but those cells that support the brain.
So if you have a cancer of the astrocyte, this is astrocytoma.
If you have a cancer of the oligodendrocyte, this is oligodendroglioma.
And there’s actually a mixed form currently being recognized as oligoastrocytoma.
Now in the lower grade glioma grade groups we focused on WHO grades II and III, which
are infiltrative and moderately diffused -- or proliferative disease.
But these may progress into glioblastoma, which is WHO grade IV disease and those have
a high mitotic rate and are prone to necrosis.
So in the past five or so years, we’ve learned that there are specific subsets of glioma
with genomic alterations, namely about 80 percent of grade II and grade III gliomas
harbor a mutation in either the IDH1 or IDH2 that is isocitrate dehydrogenase genes.
And this leads to alteration in methylation and genome stability -- instability.
In contrast, only about 10 percent of glioblastomas happen to harbor this IDH mutation.
Furthermore, in about 30 percent of gliomas, we have observed -- mostly in oligodendrogliomas
-- a loss of both chromosome 1p and 19q arms.
So we see this co-deletion about 30 percent of the time, and nearly 100 percent of those
that have co-deletion also harbor an IDH1 or 2 mutation.
Both the IDH mutation and the 1p, 19q co-deletion are associated with better prognosis.
So though we have preconceived notions -- as I mentioned we tend to see that co-deletion
going with oligodendrogliomas -- about which types of glioma will harbor which types of
genomic alteration, we found that there’s actually not a strong concordance between
the IDH mutation and co-deletion, and those that histology in grade subtypes -- as shown
here in this cord diagram -- where you can follow the IDH mutation with co-deletion across
to the oligodendrogliomas of type -- grades II and III -- as expected, but also find those
purple cords going into oligoastrocytoma and other groups.
So in particular we noticed that IDH wild-type here -- whoops -- IDH wild-type here goes
into all of the six histology and grade categories.
And actually the IDH wild-type follows right along with a GBM profile on a survival curve.
So in fact, the analysis working group for lower grade glioma went on to show that the
distinct molecular profiles shown here -- and you’ll notice this color-coding kind of
following throughout -- these three groups, IDH wild-type, IDH mutant with co-deletion,
and IDH mutant without co-deletion -- are following this molecular pattern as shown
by the distinct groups here -- DNA methylation, copy number, and then the cluster of clusters
across the group specifically, but also with these molecular profiles.
And of note, again here, these IDH wild-types are really mimicking GBMs showing EGFR, PTEN,
CDNK2A, a lot of those same genomic alterations that we would expect to see.
But notably, it’s not following histology or grade.
So that’s kind of the punch line there.
But in parallel, The Cancer Imaging Archive -- which is the informatics division of the
NCIs cancer imaging program -- was collecting preoperative radiological imaging sets for
the glioma cases as profiled by TCGA.
And this is something that they’ve done for over two dozen -- I believe now -- different
cancer types.
So this includes T1 weighted MRI with and without contrast enhancement, T2 weighted
MRI, fluid-attenuated inversion recovery -- or FLAIR MRI -- and the DWI/ADC maps for our
particular cancer.
And so, you’ll see a lot of these screenshot images here as we go forward.
And I use the xnatview.org -- I’ll give a little plug for you David -- that this is
a great way for those of us who are not wanting to download all the images to be able to go
through and view them.
So, unlike the study we saw yesterday by Maryellen Giger -- which was doing this by computer
automated searches -- we are actually using a set of neuroradiologists.
There were 9 neuroradiologists that read each sample and triplicates, so we had three people
per sample -- they were blinded to the -- to the diagnosis or molecular group of the tumor.
And they looked for 26 -- I keep hitting the wrong button -- 26 features so -- of the tumor.
And these were features that were defined to be related to what you would expect to
see if you were looking as a radiologist attempting to diagnose this tumor in the clinic.
Okay.
So our question for this study today is are there neuroimaging features that are associated
with that IDH co-deletion defined molecular subtype that we found from the LGG working
group that we can see here in the radiology images?
And one thing in particular that I was really fascinated to see is, do we see that IDH wild-type
really showing up as a glioblastoma?
So just to get a sense -- we have 70 cases here in our first round of reviewer-assessed
images.
You can see that this pattern here of IDH wild-type mutant non-codel and mutant codel
is following just what we saw.
It’s really mixed with the different subtypes.
We see that this subset also has that characteristic drop in survival for the IDH wild-type.
So these 70 is a good representation of the 293 that we used in the full study for LGG.
So some of the things we found.
First thing we found was that IDH codel tumors were likely to be centered in the frontal
lobe.
In fact 75 percent of those tumors were found in the frontal lobe, compared to other locations.
Similarly, the IDH mutant non-codel tumors were split between the frontal lobe -- 41
percent -- and the temporal lobe -- 41 percent.
So 80 percent were found in this region.
In contrast, we saw that the IDH wild-type would be found in any region of the brain,
and it did not have any specific localization.
And this is something that we did note in the LGG marker paper, but it was nice to have
that confirmation that we were finding on the same page.
So other IDH-mutated associated features.
IDH mutant codel tumors were more likely to have a T1/FLAIR signal cross at midline.
We’ll see a little bit more about what the T1/FLAIR signal is looking like.
They have presence of hemorrhage and they have presence of cysts -- more prevalent than
other tumor types.
The IDH mutant non co-del tumors were the least likely to have the presence of satellites
-- so less likely to have little bits in other regions of the brain.
So these IDH wild-type tumors -- we found that they tended to be more infiltrative.
So here’s that T1 versus FLAIR.
And in fact, what we were looking at in this case is T1 region and the FLAIR region and
were they of similar dimension, or did the T1-FLAIR -- or the T2-FLAIR region have a
more expansive molecular -- or footprint than the T1 weighted region?
And so, for the mutant codel cases and the mutant no- codel cases, we tended to have
more of them where the T1 and the FLAIR regions were similar, showing a similar expanse here
-- so not showing that infiltration -- whereas in the IDH wild-type -- and you can kind of
see this guy here -- but you can see it’s much more expansive in the flare region and
so we’re showing that infiltrative pattern.
IDH wild-type were also less likely to have a well-defined non-enhancing regions.
So the enhancing region is that part that you scoop out when you’re actually doing
the resection, it’s that part defined by the blood-brain barrier being broken down.
But the non-enhancing region is that expansive region beyond it -- is showing that edema,
that growth, that infiltration.
And so if you were in IDH wild-type, you are more likely to have this type of a fuzzy,
just broad expansive infiltration, whereas you would have a better, more circumscribed
tumor if you were in an IDH mutant case.
The IDH wild-type tumors also tended to be smaller.
So here we see an example of an IDH mutant co-deleted tumor that has 57.6 centimeters
squared area in its largest region.
And you can see this big guy here in the frontal lobe as expected for an IDH mutant codel.
And then the IDH wild-type here is only -- this one is 21.5 centimeters squared area and it’s
maximal range.
So they tended to be smaller.
And the hypothesis there is kind of two-fold.
So one might be that it’s more aggressive, it’s expanding quickly, and so we’re catching
it in that rapid growth phase.
Whereas these guys are kind of growing a little more slowly and we’re not catching them.
Also it could be that being in the frontal lobe, you’re less likely to get into some
of those eloquent regions as quickly, and so we’re not catching them by symptomatic
diagnosis as early until they’ve grown to a larger state.
So even though we’ve seen some things that are related to IDH wild-type tumors and aggressivity,
there are two key features that we see in GBM that we did not actually see more common
in IDH wild-types.
So this is a GBM, so you can see that real bright enhancing region showing that breakdown,
and that hemorrhage of the blood brain barrier and that hemorrhage.
We’ve got a necrotic region here, which is key to the WHO grade IV disease, that necrosis-prone.
Now in the lower grade gliomas -- the grades II and III -- we actually saw about 66 percent
of our tumors had an enhancing component, and it was not more prevalent in the wild-type
than the other groups.
It was more relevant in grade III but they -- we did see some groups with enhancing tumor
in grade II disease as well.
Likewise, the necrotic component was found in about 26 percent of our cases, so these
are lower-grade gliomas -- grades II and III -- did show necrosis in 26 percent of our
cases -- but it was not isolated to wild-type.
And in fact, five of those cases -- or about 30 percent -- were found in the grade II disease.
So we tried to go one step further to see if maybe we could come up with some classifications,
some clustering algorithms, and this is one of our attempts at it.
We used a bi-clustering metric because this allows for the ordinal and the nominal types
of data that we have, because really the only thing that we have that is continuous is the
dimension.
And even though we found some great clusters here, none of them are really aligning except
for this one cluster, which has five IDH wild-type grade III tumors, and shows this series of
features in common.
So in conclusion, there are several MR imaging features that are associated with LGG molecular
classes.
IDH wild-type does seem to show an aggressive phenotype, but it does not seem to be that
it’s simply an underdiagnosed GBM.
Imaging patterns are present but we’re still working on exactly what those patterns mean
and how we can use them for clinical impact or whether they have other genomic associations.
So this work could not be done without the support of our large group here in the TCGA
Glioma Phenotype Research Group at the Cancer Imaging Archive, without the support of the
Hermelin Brain Tumor Center for my time, and our colleagues in the lower grade glioma analysis
working group.
I’m happy to take questions now.
You’re welcome to email me at any time, or I’ll be wandering around my poster number
98 later today.
Thank you.
[applause]
Male Speaker: Great presentation, and I applaud you for
using nine different observers.
But the question that comes in, is what was the concordance between the nine observers,
and what was the kappa value?
Laila Poisson: So it actually depended on the -- each of
the 26.
So concordance was higher for some than others.
We ended up -- in some cases we would have five categories, like really strong, strong,
neutral, and so forth, where we had to collapse some of the highest -- or the two high, the
two low and have a high, none, and low -- to gain concordance.
So, we did go through a training phase.
Unfortunately, I can’t give you the kappas for all of them.
We went through a training phase with each of our readers ahead of time to make sure
that everybody understood these 26 features and how to score them.
So in -- all in all, it was fairly strong.
In the end, we did a voting, and they had somebody who went through and did a check
to make sure that it was best representative.
So there was a fairly rigorous amount of data checking and quality behind that, but I don’t
have the exact kappas for each one.
Male Speaker: Okay thanks.
Laila Poisson: [affirmative]
Female Speaker: Thank you.
We are going to take a break and then please come back by 10:40.
[end of transcript]