Jianjiong Gao: Thanks for this great opportunity for us to
present our work here.
And I'm going to talk about a new feature that we developed in the cBio Cancer Genomics
Portal for visualizing and analyzing clinical and genomic data for individual patients.
cBio Cancer Genomics Portal is a web resource that is open access and for interactive exploration
of high dimension cancer genomics data.
Our goal is to lower the barriers between cancer genomics data and the cancer researchers,
including wet lab biologists, and clinical researchers, and computational biologists.
With this portal, one can very quickly visualize and analyze genomic alterations, in an iterative
and interactive way.
And we hope that with this portal, we can really help that the cancer researchers to
[unintelligible] the complex genomics data to biological insights and the clinical applications
I would like to mention one key abstraction, namely, altered gene.
We can see a gene is altered if it is, mutated or amplified or deleted.
So this simplifying concept, actually, it's very powerful and enables user to derive hypotheses
regarding frequently to alter the gene set of pathways.
And we just released this patient view.
We aggregate -- we integrate diverse datasets and filter them and present them to the user,
and we want to do something more interesting and more intelligent because tumor samples,
they -- often time they have hundreds or even thousands of alterations.
Which of them are relevant to the disease and can we use these relevant events to define
the choice of treatment.
And actually, experts, they are pretty good at intuitively identify them.
Can we automate part of this process and can we even improve it?
So our goal is to derive interactive system to facilitate personalized medicine research
for cancer and ultimately guide a patient treatment.
So in this patient eccentric view, we integrate diverse data including clinical data and genomics
data.
So the clinical data include patient information, disease status and pathology reports, and
we're going to support tumor images and treatment data as well.
And the main part is -- the main part in a patient review is the medical profiles.
Currently, we support mutations and copy number alternations, and gene fusion and expression
will be supported later.
After we get this alterations, the next step we are adding functional and [unintelligible]
annotation to this alterations, including predicted mutation impact by mutation assessor
and cancer gene annotations from Sanger Cancer Gene and COSMIC.
And COSMIC -- and the cohort context information, such as significantly mutated the genes, copy
number altered peaks.
We also bring drug data information and clinical trials from resources such as NCI drugs [unintelligible]
base, and after we are putting this together we present this to cancer researchers and
clinicians in an interactive way.
And we are also building a Wiki system that can be used for the user for annotate tumors.
Enough said.
So this is an example of our release to product of the patient view in the current portal.
In this particular case, this woman is a 69-year-old serous ovarian patient with stage three and
grade three, and we can get more clinical data from here.
And the summary view is basically summarize the mutation event and copy number events,
and I'm going to take a few minutes to explain the detail and hopefully make this not only
colorful, but also meaningful to you.
This graph plots copy numbers events and mutation events across the chromosomes.
The copy number check from the copy number segments.
And from here we can see that actually in this patient the copy number is pretty much
altered and this same characteristic was observed in serous ovarian cancer.
And the mutation check is we plot the histogram of number of mutations across chromosomes.
This is a scatter plot of number of mutations and the fraction of copy number altered genome.
Each dot represents a tumor example in the cohort study and the red one is the patient
we are currently viewing.
In this panel we list the, I mean, the mutation events in this patient.
In this patient there are a total of 35 mutations, and four of them are reported as interesting
by the portal, based on recurrence, COSMIC, or annotated cancer genes.
The first mutation is a TP-53 nonsense mutation; TP53 mutation is almost universal in serous
endometroids.
The second mutation is a phosphatase 2 missense mutation.
It is a recurring mutation.
There are 26 other samples in this study have first mutated first to phase two.
Three of them have the mutation on the same amino acid.
The MutSig was also showing that it is significantly mutated the gene or recurrent mutated gene.
And it has also overlapped with the COSMIC data.
Mutation Assessor predict this mutation to have a high functional impact, and from the
3D structure through the Mutation Assessor, clearly, it is mutation in the highly-conserved
region.
In this panel we list the copy number events, and there are a total of 385 copy number,
copy number all to the genes.
And 17 of them are reported as interesting.
For example, the second [unintelligible] is reported because it's a recurrent gene and
based on gestate, and it is actually a four-code [spelled phonetically] amplification.
For example, this EGFR actually is reported because of -- it's a annotated cancer gene.
And it's rare event, it's not recurred in gestate peaks, but it could be interesting
and EGFR is a frequent -- is a [unintelligible] target.
For example, for the drug [unintelligible] we can get the drug information with clinical
trial information linked.
The second and third tab, we list all of the mutation and copy number alterations.
And the fourth tab, we also linked to the pathology report for this patient, if available.
And from this patient view, we can link back to the cancer study view.
The cancer study view present off the clinical end cancer genomics data in an iterative way.
For example, for the clinical data, we're plot of the histogram and the pie charts,
and they are all interactive and can be cross-futured [spelled phonetically].
For example, for this histology plot, if we click on the serous pie, then of the serous
cases in this plot will be selected, and immediately we can observe that this patient is an outlier
because it has a very low copy number alteration and has a lot of mutations unlike other serous
cases.
And if we click into this patient and have a look, clearly, this patient almost has no
copy number alteration and has a lot of mutation events, and it has -- such as a KRAS [spelled
phonetically], et cetera which aren't characteristic to serous cases; therefore, makes this case
very suspicious.
And back to the cancer study page, we also list the significantly mutated genes and copy
number peaks.
Next, I will do a little bit demo in the next two minutes.
I hope the Internet is work.
Okay.
So this is the front page of the query page, and for the time sake, let's select the endometrial
case for demo and we leave this mutation.
Copy number change as default, and we can select some genes from MutSig list.
For example, this ?-catenin and PRAS [spelled phonetically] and somatin [spelled phonetically].
This will bring us to a summary page including a so-called ONCO print.
Yeah, from this ONCO print, it is clear that there are a lot of events occurring in these
two genes, represented by this green dot, and let's look at the mutations.
So here in the mutation diagram, all of this, most of this ?-catenin mutations occurs in
the [unintelligible] area, and they have well known functional impact, actually, if we saw
this.
And as a chance, we can have -- it's a very long list and terminal.
And similarly, for the KRAS, most of them -- actually, 35 of them actually occurred
on the G12 position, and if we saw, it's a long list of them, and they have well-known
functional impact.
Getting back to the summary page, there are six amplified specific cases with amplified
KRAS, and if we are interested in whether this amplification is functional or not, we
can have a look at the plot.
Select KRAS.
And this is a plot between gene expression versus GISTIC, and this right-most part, from
this right most part we can clearly see that there's at least one sample with a very high
expression level of KRAS.
And from this ONCO print we can kind of spot a mutual exclusivity pattern.
And let's have a look at the p-value here as a .02, which is significant event.
So the next question is why do we still have this overlap, still have cases that have both
mutated in both genes.
And we can take a look at the maybe the first one.
This patient has a lot of mutations, more than 6,000 mutations.
It could take a while to load it.
And -- but I think that this is exactly the reason why this patient has both mutations
-- has mutations in both gene because of the higher mutation account background.
Okay.
I will give up on this one.
And I have a preloaded version here.
So this patient has a functional mutation, KRAS mutation and ?-catenin mutation, not
well known function here.
Okay for the time sake, okay, I will maybe show a little bit on the patient -- on the
cancer study view.
Oh, it just loaded.
And the cancer study view actually plots off the attributes and the scatter plot, and the
patient is remembered.
And if we're clear, then we're clear -- or -- and we can like search [spelled phonetically]
of the living patient with recurrence and et cetera.
With that, I would like to stop here because of time.
And we are going to have a workshop in the Alexandria Room.
And with that I would like to thank my colleagues, especially Nicky Schultz.
He's been managing this project and coming up with a lot of ideas to the patient view.
And it is a joint effort of the whole team, including Ben, Arman, Onur, Gideon, Boris,
Ethan, and Chris.
Doug Levine from MSKCC provided a lot of feedback, and his group brought a lot of feedback to
the portal and to the patient view.
Thank you all.
[applause]
Richard Gibbs: Congratulations on the live demo.
It's impressive.
Jianjiong Gao: Thank you.
Richard Gibbs: So what's the status of your clinical interface,
you know, physicians using this.
You're gliding us all into some position where the less academic clinician can use the tool?
Jianjiong Gao: Yeah, we -- I mean, for now, we -- basically,
it's for researchers, clinical research or, like, cancer research, basic cancer research,
but we're hoping that it really can be used in clinical setting.
Richard Gibbs: Thank you.
Female Speaker: So I have a question.
When you're deriving the individual copy number alterations, do you use the cohort context
total or are you just looking at tumor versus normal for the same patient?
Jianjiong Gao: The -- you mean --
Female Speaker: The -- so when you're deriving the copy number
alterations for a particular patient, do you just look at that particular patient's tumor
data and normal data, or do you also include the cohort context like GISTIC information?
Jianjiong Gao: Yes.
We use GISTIC information, actually.
Female Speaker: Okay.
Jianjiong Gao: Yeah.
Female Speaker: Oh.
Okay.
Richard Gibbs: Two more questions.
Female Speaker: Quick question.
[unintelligible], Georgetown University.
Fantastic presentation.
Thank you.
I was wondering how much pre-processing occurs in the back end prior to loading the data
into the cBio portal?
I mean, for all the 7,000 or so samples that you have on your portal, I can imagine, you
know, it takes quite a bit of time for the various data types that you're presenting
here.
So can you shed a little bit of light on what kind of pre-processing that you do before
loading the data into the cBio portal?
Jianjiong Gao: Yes, most of the data for the [unintelligible]
we get it from, like, Fios [spelled phonetically], and we have some pre-processing, for example,
for the expression data, and mutation data we add a lot of annotation to the math file.
And for the copy number data, we also have some pre-processing.
Yes, it's a lot, and we have people in charge of managing those data and the pre-processing,
and we have a whole pre-processing module.
Female Speaker: Quick follow-up.
Do you have a timeline for how long it takes for a particular study, let's say 100 samples
with three different data types?
Jianjiong Gao: Timeline?
You mean...
Female Speaker: Yes, for pre-processing and getting it into
cBio.
How long does it take?
Jianjiong Gao: You mean that --
Female Speaker: About a 100-sample study into the cBio?
Jianjiong Gao: Okay.
It may be like a few -- well, a hundred -- maybe less than one hour.
Female Speaker: Okay.
Thank you.
Male Speaker: Last question.
Female Speaker: Yes.
I just have quick comments maybe also for the community so that when many people think
[unintelligible], many people think about it like a bioinformatic software and tools
as a black box.
And it's appreciated that you talk about the workflow how to use the black box, but I think
that I'd like more insight, more explanation to the [unintelligible], to like what's inside
the black box, what's the computational approach to the limitations.
Why.
What kind of work it can do.
What kind of work it cannot do.
So that the inside of the black box is more insight into the computational approach.
So there will be more [unintelligible] for I hope to say more information like during
workshop, like that.
Jianjiong Gao: Thank you.