Ji-Yeon Yang: First, I'd like to thank the organizers for
inviting me to speak here today.
I'll talk about the project that we've been working on for predicting time to ovarian
carcinoma recurrence using protein markers.
In this study, we mainly focused on high-grade serous ovarian cancer types.
Let me start with a little background.
The standard treatment of ovarian cancer is surgery followed by platinum-based chemotherapy.
But the initial response rate is pretty high, but 25 to 30 percent of patients relapse within
12 months, and tumor doesn't respond to platinum therapy.
For the TCGA dataset, roughly 30 percent of patients who are classified as platinum chemotherapy
-- oh, [laughs], platinum resistant.
Sorry.
So there is a need of noble predictors of a platinum resistance that can lead to changing
therapeutic approach.
Lately we developed a patient classification system named Classification of Ovarian Cancer,
CLOVAR.
Using TCGA data we identified the cell types and survival gene signatures, and then we
built a prognostic model.
We found that CLOVAR significantly predicted overall survival.
These Kaplan-Meier curves were generated after classifying patients into three risk groups
using CLOVAR, and this was obtained from the validation set.
The [unintelligible] test was performed to compare overall survival in the risk groups,
and the p-value is very small for overall survival.
But the prediction of progression free survival is not as accurate as the prediction of overall
survival.
And we found a very similar pattern in several other gene signatures from other studies.
Those gene signatures are strongly or significantly associated with overall survival, but not
with a progression free survival or treatment response.
That motivated our work.
You may wonder why the sample sizes are different for these two plots.
Well, not all the samples had progression free survival information.
That's why we have a smaller sample size for this.
In this study we aim to develop a predictor of platinum resistance using protein markers.
For this, we used the reverse phase protein arrays, RPPA technology.
I think most of you are already familiar with RPPA.
It is a high-throughput technology for measuring protein expression levels in a large number
of samples.
Using RPPA, we measured 172 cancer-related proteins and phosphoproteins in 412 TCGA samples
with a serous ovarian cancer.
Of 412, 220 samples were included in the model construction.
Those samples had non-missing values for progression free survival and they all had advanced stage
disease.
In this study, we developed a protein-driven index of ovarian cancer, and we named it PROVAR,
in line with CLOVAR.
And let me show you a brief flowchart of how we constructed PROVAR.
First, we used 222 TCGA samples as a training set, and then, in the next step, we applied
LASSO, a status of regulation methods to the TCGA set.
I'll talk about this in the next slide in just a minute.
Using the LASSO, we identified the nine protein markers that are most associated with progression
free survival.
And we estimate the coefficient from the Cox regression.
After that, we defined PROVAR as a linear combination of the nine protein markers weighted
by the Cox regression coefficient.
A statistical challenge of high dimensionality arises whenever you predict survival using
genomics or proteomics later.
For the feature selection and coefficient estimation, we used the LASSO.
The LASSO estimator is obtained by maximizing the likelihood of function with an L1-constraint.
The main advantage of the LASSO is it provides a parsimonious, very simple model by shrinking
unnecessary coefficient to exactly zero inside of small values.
By applying the LASSO to the TCGA set, we identified these nine protein markers and
estimate the coefficient.
You may notice that the coefficients are very small.
Those are shrunk by the LASSO, and it is known that shrinkage may help avoid overfeeding
problem.
These are nine protein markers we identified, and these five proteins were -- these five
proteins had a negative Cox regression coefficients, which means these were associated with the
better performance, better outcome.
And indeed, these were overexpressed in the low risk group.
The other way around, these were associated pro outcome and overexpressed in the high
risk groups.
And these little bars indicate which samples are platinum resistant, and we see more platinum-resistant
patients in the high risk group.
After we identified the nine protein markers, we defined that we computed PROVAR for each
patient in this way, it waited [spelled phonetically] some, and then we classified the patients
into one of two risk groups according to PROVAR, and we used the median as a cut off.
Here are results with PROVAR.
This is based on progression free survival and this is your overall survival, and PROVAR
was constructed using progression free survival but it is also predictive of overall survival.
And again, not all the samples had a progression free survival data available so that's why
we have a smaller sample size for this.
Well, these were obtained from the training set, TCGA training set, and we need to validate
PROVAR in an independent dataset.
We were able to obtain 229 high-grade serous validation samples from Japan and Philadelphia.
They were treated uniformly.
In using RPPA, we measured expression levels of 144 proteins and phosphoproteins for those
samples.
And as you see, PROVAR is significantly predictive of both types of tumor recurrence and overall
survival in our independent dataset, and we tried out three group stratification, but
we saw singular result.
For the purpose of comparison with a gene-driven model, we implemented CLOVAR in our validation
samples with the gene expression data available.
Well, the sample size is small because it's only subset of validation samples that had
a gene expression data available, so the sample size, we are just using 130.
Let me first show you the results with CLOVAR.
No significant difference in progression free survival between groups.
But PROVAR, obviously, it improved the prediction of a progression free survival.
Finally, we tried to test the robustness of the nine protein markers that we identified
using the TCGA samples.
To do that, we sort of, in a reverse way, we used validation samples to identify protein
markers, and then we compared those samples -- those proteins versus the original nine
protein markers and see how much similar they are.
Using the validation samples, we identified these five proteins, and let's compare this
with the original ones.
Unfortunately, AR is the only overlap.
But when you clustered proteins using hierarchal clustering, we found that some proteins shared
very similar expression profiles.
Let me explain this.
These little bars on the first row indicate the location of the nine protein markers that
we identified using the TCGA samples.
And this one, the second row, this one is overlap, and these are the protein markers
we identified using validation samples.
And the overall, our protein markers were spread out over the clusters, and interestingly,
nine and five protein signatures included representative proteins from each cluster.
So, in conclusion, we developed a protein-driven index of ovarian cancer, PROVAR, using progression
free survival, and we show that it is predictive of both progression free survival and overall
survival in high-grade serous ovarian cancers.
In many genetic signatures in other studies often contain a large number of genes, but
unlike this, PROVAR is simple, but is still predictive, making it useful in clinical practice.
I like to thank my collaborators, especially Kosuke Yoshihara, and Roel Verhaak, who is
my mentor, and also like to thank Gordon Mills, Yiling Lu, and all the other collaborators.
Thank you.
[applause]
Matthew Meyerson: Sure.
I'm curious that one of the common prognostic protein factors was AR, the androgen receptor,
and, you know, I think that kind of goes back to the question that one of the -- our colleagues
was asking about.
ER and lung adenocarcinoma, what's known about the role of androgens in growth of ovarian
cancers or other women's cancers?
Ji-Yeon Yang: You mean YA [spelled phonetically] or --
Matthew Meyerson: No, I thought -- no, AR.
Ji-Yeon Yang: AR?
Oh.
Well, I'm a statistician you don't expect -- [laughs] -- that kind of answer.
Well --
[laughs]
Lou Staudt: Thank you for that great answer, okay.
Ji-Yeon Yang: Well, at least it is a significant statistically
and --
Male Speaker: Go ahead --
Ji-Yeon Yang: Yeah, so we found in many studies AR is a
significant predictor of ovarian cancer, yes.
Matan Hofree: I have question that's hopefully more up your
alley.
So I was wondering, you used an L1 regularization.
There is a lot of evidence that mixing L1 and L2 regularizations, such as when using
elastic net, you could basically also capture redundant predictors.
Did you try anything like that, a)?
And b), if you did not, another trick is to sort of remove those first nine predictors
that you got with your L1 regularization and PROVAR, and then see what's the next best
group of predictors for basically predicting survival.
Did you try any of those things?
Ji-Yeon Yang: Are you talking about the LASSO stuff?
Matan Hofree: Yes.
Ji-Yeon Yang: We haven't, but probably we can do that later
--
Matan Hofree: I mean I would suggest either doing elastic
net or --
Ji-Yeon Yang: Yes
Matan Hofree: -- seeing what's the next group of predictors
because --
Ji-Yeon Yang: Yes.
Matan Hofree: -- that would be interesting from a biological
standpoint as well.
Ji-Yeon Yang: Yeah, we'll look into it, thanks.
Male Speaker: Anderson Baylor.
I was wondering if you'd looked at the patterns of androgen receptor co-activators or co-oppressors
in ovarian cancer that may correlate with your patterns of expression.
Androgen receptor antagonists like Flutamide have a notoriously low response rate in ovarian
cancer, in the range of 5 to 8 percent, despite the fact that there are studies out there
that would suggest that the androgen receptor itself is expressed in about 70 percent of
ovarian cancers.
So I was wondering if you had looked at other factors that may be playing into your observations?
Ji-Yeon Yang: We considered clinical factors such as age,
stage, grade, surgery status, and also BRCA1 and 2 mutation, and those are significant,
actually, but we considered multivariate, multiple Cox regression, and PROVAR was the
most significant one across the dataset.
Lou Staudt: Last question.
Male Speaker: Four of your nine antibodies are phospho antibodies,
so could you please comment on the potential technique as related to the different ischemic
time?
Ji-Yeon Yang: Well, we haven't done that functional analysis
with the pathway of functions interested four clusters.
Probably we will investigate it further.
Thanks.
Lou Staudt: Okay, thanks.