23_2014-05-13-TCGA_Sinha
Subarna Sinha: So firstly, I would like to thank the organizers
for me giving me a chance to present our work. Today I'll tell you a story about how we use
the TCGA data to understand the role of a common, but not well understood mutation in
acute myeloid leukemia. Thank you.
First, a little bit of background on the disease. As many of you might be aware, acute myeloid
leukemia is a disease characterized by accumulation of myeloid precursor cells in the bone marrow
that are blocked in their ability to differentiate into mature blood cells, and like many other
cancers, AML is associated with widespread deregulation of DNA methylation. So some work
done early in 2010 comparing leukemic blasts to normal CD34 cells showed both regions of
aberrant hyper-methylation in the red here, as well as hypo-methylation. And so what was
most interesting for us was to see, what was the source of this aberrant methylation?
There are several reasons it could be stochastic, as has been shown -- cancer methylomes have
been shown to be quite stochastic. It could be a cell of origin issue where the methylation
pattern that you are seeing is a result of the cell from which the leukemia originated,
or it could be a genetic mutation; so it could be a mutation that's actually driving aberrant
methylation. So we were particularly interested in this question, because mutations in DNA
methylation machinery have -- had been identified in AML, but at the same time, those mutations
could not explain all of the methylated -- aberrantly ethylated samples, so we would actually have
hyper-methylated samples that didn't have mutations in any one of these genes, as well
as aberrantly hypo-methylated samples.
So we, sort of -- because of the availability of data from the TCGA with both mutation and
methylation data for the same samples, we set out to systematically analyze this question,
and we had two goals in mind: one was to see if we could identify any new genetic drivers
of aberrant methylation; and secondly, if we could actually use the methylation pattern
to find leads for a mutation-specific therapy. So to get to question one, we used a tool
that was developed by my PI's [spelled phonetically] group several years ago. It's called, "Boolean
Implications," and this was inspired by Boolean logic, where in implication actually is a
pairwise operation. So in the context of data analysis, this enables us to look at pairs
at -- pairs of attributes and looks at their relationships. So it was developed for gene
expression data, and when I started analyzing the TCGA data, I actually extended it to consider
mutations, copy number alterations in DNA methylation.
So what does it look like? It's actually an L-shaped relationship if you look at the scatter
plots of the attributes. So visually, it looks like an L-shaped relationship, but of course,
we have a mathematical way of extracting these relationships. So the first step to extract
a Boolean implication would be to discretize the data. So again, we have a systematic algorithm
that looks at the distribution of values for each attribute to come up with an attribute-specific
threshold of when to decide that the sample is high-values or low-values. And for it to
be a Boolean implication, when you look at the four quadrants in a pairwise plot, you
need to have at least one -- you need to have one sparse quadrant -- and by "sparcity,"
I mean that there should be very few samples within that quadrant. And again, there is
a statistical test we have to actually identify sparcity.
So as you can imagine, there are four different quadrants. And so you would see -- you would
have four different types of Boolean implications. The one that I'm showing you here is what
we call a HIHI Implication. So this says that if A is high, then B is high. So another way
of thinking about it is -- what this is saying is all samples that have Attribute A high
would almost always have Attribute B high, but the reverse is not true. So you would
have many samples where Attribute B is high, but Attribute A is not high. So as you can
see, it's a way derive asymmetrical relationships. So the next -- the second implication -- kind
of implication is a HILO implication. In that case you are actually looking for sparcity
in this top quadrant over here. And what -- the HILO implication states that if A is high,
then B is low. And that's typically what is known as mutual exclusion in this community.
I'm not going to talk about the two other types of implications, because that's not
necessary for this analysis.
So what does our computation pipeline look like? So we took all the TCGA AML samples
that had overlapping mutation and methylation data -- and that's about 191 samples -- then
with the mutation data we identified 17 recurrent mutations. So these are mutations that occurred
in five or more samples. With the methylation data we used the 450k arrays, which -- so
they are 450,000 sites. We did some filtering to remove probes that had low dynamic range,
and then we discretized the methylation values. So now I have Boolean data on both sides;
the mutation data is Boolean to begin with, I have Boolean -- I have sort of discretized
the methylation data, and then regenerated these Boolean implications. And just, to kind
of reiterate what I said in the previous slide, so a Boolean implication between mutation
and methylation looks something like this. This is a HIHI implication. So what this is
saying is if IDH2 is mutated, then CpG site A is always methylated. And this is a HILO
implication. So what's this is saying is if DNMT3A is mutated, CpG site B is always un-
-- is always not methylated, or un-methylated.
So once we have the Boolean implications, we just count the number of methylation HIHI
and HILO implications. And when I started this analysis I wanted to see if we would
really see any differences between the different mutations. And to our surprise, it actually
fell into four nice categories. So the category on the right over here -- so these are mutations
that have very few implications with methylation, both HIHI and HILO. So you can see KRAS and
NRAS fall into these, which sort of makes sense, because they are not known to be associated
with methylation. Then, in the hypo-categories -- so these are mutations where you have many
more HILO implications and HIHI implications, suggesting that these have a predominantly
hypo-methylating effect. Again, DNMT3A falling into that makes sense, because it's an inactivating
mutation of a DNA-methyltransferase.
So what was most exciting for us was this category of hyper-methylation. And again,
what stood out was WT-1, which is -- because it's a transcription factor, and we weren't
really expecting to find it there. So if you look at the data -- again, visually, because
there's a whole bunch of numbers here -- so what I've plotted here is the ratio, the number
of HIHI implications to HILO implications. Again, you see WT-1 really kind of stands
out. So that's what we decided to pursue further, to see what was going on.
So the first question was to see how the co-occurrence in mutual exclusion patterns of the three
mutations in the hyper category, and what we found was that both in the TCGA data, as
well as a separate cohort, the e-cohort, we found that the mutations were almost mutually
exclusive. And also when we look at the CpG sites that are being methylated by the three
different mutations, or the genes that are associated with the CpG sites, we find only
partial overlap -- little to partial overlap, actually. And so this is kind of suggesting
that WT-1 mutation has a very unique hyper-methylation signature, but it still does not tell us whether
it's actually actively playing a role in causing the hyper-methylation, or it's just some kind
of epiphenomenon that we are observing, such as a cell of origin thing.
So to get to that question we decided we needed to really do [unintelligible] lab experiments.
So we put a mutant form of the WT-1 protein in AML -- in THP 1 cell lines, which is an
AML cell line, which -- that is wild type for WT-1. And then after 10 passages we measured
the methylome using 450k arrays, which if you remember, is the same array that used
for the TCGA patients. And so, this heat map over here compares the controls to the WT-1
mutants. And you can see an increase in DNA-methylation, going from the controls for the WT-1 mutants.
And furthermore, what was interesting was I looked at the genes that were being methylated
in the cell lines, versus the genes that have been methylated in the TCGA patients with
the WT-1 mutation, and there's a very significant overlap, suggesting that probably a similar
mechanism is happening in the patients as well.
So now we have these genes, and so the next question is, you know, "What are these genes
that WT-1 mutation seems to be prepensely methylating?" So GSC analysis shows in both
the TCGA patients, as well as the THP 1 cells lines that they're extremely enriched for
the PRC2 targets. So that's -- these are genes with the H3K27 tri-methylation marks, and
so, you know -- so I mean, it could -- it's possible that this happens to any -- all genes
that are methylated would be enriched for PRC2 targets. So what we decided to do was
to compare the methylated genes associated with the different mutations and -- to see
if that was unique to WT-1 and that turn. While you do see PRC2 enrichment for the other
mutations, but the enrichment for WT-1 is clearly off the charts.
So just to give you a quick review on what the PRC2 complex is, it's known to be a master
regulator of differentiation events throughout development, and it has three components,
one of which is EZH2, the catalytic component of which has a strong methyltransferase activity,
and it's supposed to induce the HTK27 mark, which is a known mark of transcription repression.
So is it possible that the WT-1 mutation is actually causing dis-regulation of PRC2 targets
in AML? And our preliminary analysis suggests that's indeed the case. So what we did is
we isolated a bunch of genes -- we call them the PRC2 mark genes in adult hematopoiesis,
and we used the ENCODE data to come to that list. And then, we compared gene expression
from -- for the WT-1 mutant AMLs versus normal hematopoietic populations. We found a significant
degree of repression in the WT-1 mutant AMLs compared to mature mono sites [spelled phonetically],
and these were actually more like the normal progenitors -- progenitor populations, suggesting
that WT-1 mutation could be actually inducing a myeloid differentiation block. So to test
this, we used a well-established cell line assay in which TF1 cells in presence of EPO
produced fetal hemoglobin. But when the mutant was introduced in the same cells, they failed
to produce fetal hemoglobin, suggesting a differentiation block.
So, so far I've come -- hopefully I've shown you how the WT-1 mutant seems to be producing
both dis-regulation of PRC2 targets and also seems to be causing a differentiation block.
So the next question from the clinical stand point is, "Can you actually rescue the differentiation
block?" So in order to test that, we used a GSK126, which is an inhibitor of EZH2, and
is already in clinical trials, I believe, for activating mutations of EZH2 in defused
cell lymphoma. And what we find is by treating with GSK126 for primary AML samples containing
WT-1 mutant AML, we see an up-regulation of mature myeloid markers, and this was not observed
in AML samples that did not have the WT-1 mutation, as well as in APML samples that
did not have -- that responded to other treatments, but not to this particular treatment. So we
are hoping that this actually means that the EZH -- the GSK126 could actually be used as
a targeted therapy for AML patients with the WT-1 mutation.
So this bring me to my last slide. I've presented a story about how we think mutation in WT-1
is a normal driver of DNA-methylation in AML, and we have actually tried to use the methylation
pattern to come to a cure -- come to a potential therapy. So what's exciting from the clinical
prospective is EZH2 inhibitors have activity in WT-1 mutant AML. And just a little plug
about the computation pipeline, the initial pipeline that analyzed mutation methylation
data is not AML-specific at all, and it should be applicable to other cancer types, or even,
you know, Pan-Cancer kind of analysis. So if there's interest in that, please talk to
me and I'll be happy to help you with that.
So finally, I'd like to end with acknowledgements. I'd like to thank all my co-authors on this
work, and particularly, Dan Thomas, who did the experimental work; our funding sources,
and finally, the TCGA, actually, for putting out this kind of data that help people like
me with a pure computation background to earn an interest in biology to come out and try
my tool on this kind of data. Thank you.
[applause]
Peter Laird: Thank you very much. I'd like to ask a quick
question, if I may. So, I'm a little puzzled by your rescue experiment, because generally
when polycomb targets acquire abnormal DNA methylation, they lost the polycomb mark.
And so I'm a little confused why you EZH2 inhibitor actually rescues the expression.
Subarna Sinha: So actually -- yeah, so we -- sorry. We started
out with the DNA-methylation, and then we noticed that the signal for this PRC2 dis-regulation
was actually stronger in the WT-1 mutants. And that's why we decided to use EZH2 inhibitors.
Peter Laird: So they're usually mutually exclusive.
Subarna Sinha: Right, that's what -- yeah, that's what we
had read in our own samples, but this is what we are seeing in the data. So we are actually
analyzing it further to see what's going on.
Male Speaker: So how much do we know about CEPPR-IFA [spelled
phonetically], because IDH mutation --
Subarna Sinha: Yes.
Male Speaker: -- this is very clear.
Subarna Sinha: I have seen a pair of -- so the original work,
I believe [unintelligible] they classified AML into 16 epigenetic clusters. One of the
clusters actually had CBP Alpha mutants and they were -- it was a hyper-methylation cluster.
But I do not believe that there is any work to actually validate that. So...
Male Speaker: Thank you.
Subarna Sinha: Thank you.
Peter Laird: Thank you, Subarna, and we're going to take
a coffee break now, and we'll be back for session four at 11:00 a.m. Thank you.
[end of transcript]