Hi everyone.
I am so glad you're still here.
I have certainly learned a lot.
Even though we've been using Palantir Foundry for about five years,
it's very interesting to see it used in these different spaces and have a chance to talk with you,
some of you, especially last night.
So my talk today is is Cancer -- Taming Complexity.
And I wanted to make sure that I, you know,
my Palantir staff, especially Katherine, said,
Jeff, you know, I don't think you really understand how unique your situation is.
You've lived it for so many years.
Make sure you give some background slides on on really what's what's going on in this space.
And so.
I wanted to do that and so on.
So I'm from the National Cancer Institute, which is a United States federal organization.
It's under the executive branch.
So we work for the President.
It's under the Department of Health and Human Services.
We're in the National Institutes of Health, and we're the National Cancer Institute in there.
So we have this this federated model, which, you know,
to me, it's kind of I'm so used to it and I kind of think it's a little weak.
But after hearing the Morgan Stanley thing, it's like, hey, I'm the CIO of the NCI.
Hey, the sizzle works for me.
We accept the risk for the National Cancer Institute.
I don't have to listen to someone else.
So it's really nice that way.
I put in here that we focus on rare cancers.
But that's really only the intramural program, the part where we do the research.
The National Cancer Institute is quite large.
It focuses on all cancers, prevention, education
and funding 50 cancer centers throughout the United States.
And what is biomedical research?
We are researchers not bringing products to market or anything like that.
And so.
Biomedical research is all the way from the beginning, chemistry, training --
lots of training grants all the way through the body.
Human beings are organisms.
We're based on cells, we have tissues.
And cancer is a genetic phenomenon that happens at the cellular level.
And so we have to really understand what's going on.
I have two things I want to tell you for sure about about this this thing, this phenomenon.
And that is one it impacts us.
All right.
It really impacts us all.
So everybody's in touch with that.
I want to say my son worked at Jacobs,
so, hey, that's kind of cool because I was touched by Jacobs.
And I also had a very good friend of mine
who worked at the Milwaukee Metropolitan Sewer District.
So we're really connected in some ways,
but that's kind of a just a rare occurrence.
But everybody knows somebody who's had cancer:
your parents, your family, everyone, and it affects so many people.
And so the United States has put a lot of effort towards doing this.
And it's a it's a it's a holistic solution.
And there's many other groups doing that as well.
I also want to say it's part of the health care system.
So we've also been in the health care system in the
United States, and we know how not really optimal that is.
You go to your doctor, the doctor says,
hey, I need to get some blood in.
Say, Well, can you do that?
No, no, no, no.
You've got to make an appointment over at the blood draw place.
They're going to draw the blood away from you.
They're going to test that.
They're going to give me the results.
Oh, yeah.
You have to get an MRI.
You have to get a CAT scan that's over here.
You have to go over there, get those results.
A radiologist has to read those things.
So so there's an inherent complexity to how health care is provided.
And it really is amazing because we have so many great technologies,
but it hasn't really been re-imagined.
That's that to me is what I hear throughout all the talks.
We have more data and more technology
than our 1950s design can really handle.
And so we are, of course, using more technology to try to solve that problem.
So a little bit more about the NCI.
So we are funded a lot, about $6.5 billion.
And the part I'm going to talk about today as the Chief Information Officer,
I mostly focus on the money that we're spending at the NCI, which is about this $2 billion.
And this biomedical research is --
I wrote some notes to make sure I can say this properly.
So it's basic research like how our cells work.
It's, it's preclinical.
It's like preparing all the things to make sure that
when we give them to humans doesn't kill them.
Right?
So it's all preparation for that.
It's the actual clinical work: recruiting patients,
training patients,
it's ethics,
epidemiology.
That was my note because I wanted to make sure you all understood that,
because it's basically the incidence, distribution and control of disease.
Well, epidemiology is what all about what the pandemic was about.
What is going on with this disease?
Right. And so we have a lot of that in cancer, because while cancer is a genetic disease,
your genome can be impacted by chemicals, sunshine, radiation, all kinds of things.
And we also have a big focus on prevention.
And so I have here our actual mission statement.
NCI leads, conducts, supports, cancer research across the nation
to advance scientific knowledge and to help all people live longer and healthier lives.
When you have a mission statement like that,
you have a lot of amazing people work for you
and we have a lot.
I don't know why my miscellaneous isn't there,
but we have about 9000 staff that work at the NCI proper
in three locations in Bethesda.
I'm kind of bummed that my statement was there because that was my other one.
I was going to tell you.
Oh, that's right.
I remember now.
It's that biomedical research mostly is a competition.
Scientists are --
They come into graduate school, either M.D., Ph.D. school.
They're trained in this competitive model.
They're funded in a competitive model.
And so that has worked so far very, very well.
There really is no model to replace that.
But.
It's not ideal because when you get to big data that does that little model doesn't work well.
And I mean little researchers, five person, seven person teams.
So how do you leverage big data when I don't even have a data scientist on my team?
Right.
I have two doctors, a couple of Ph.D.s
and then some lab people to actually do the work.
Well, how am I going to make sense of all these things?
And so that's what I'm going to try to talk to you about today a little bit.
So.
So we have really three thrusts at the National Cancer Institute that we're using.
We're using Palantir Foundry for.
So this one is that we're actually trying to do a
bunch of financial and business modeling on our portfolio.
That's a lot of money we give out.
We give it to a lot of different people.
We give it a lot of for a lot of different cancers.
And there's a lot of advocates that want to say, we want you to give more to our part.
And so we try to make sure that what we're doing
makes sense from a life saving, from a health care standpoint.
And so using Palantir is going to help us a lot in that space.
We also have a lot of our scientific analysis pipelines,
and that is just the thing that we do with our data.
You might think like because you said, well,
the human genome is sequenced and I would say, well, it's kind of sequenced.
But if I were to tell you, if I had the time to tell you
how they really sequenced your genome, if you were to get your genome sequenced,
you'd be like, oh, well, that's kind of really that requires
a lot of computational power to put all that together.
And it does.
And so and that's all changing.
The genome is being understood more and more and more.
And so these pipelines are changing.
And since we're a whole bunch of disparate researchers that are put together,
having those pipelines in Foundry allows us to share them
so that there's a set of kind of validated pipelines that you can say,
Well, I'm not a big data, I'm not a big DNA sequencer in my lab, so I can just use all these pipelines.
I don't need all these other people to do it.
So it really, really helps us to, you know,
maintain that set of -- all these different researchers yet be focused on.
Yet be focused on --
My actual work yet I can still use this high order stuff.
And then what I'm going to actually go through really quickly with a couple of slides is...
This.
We've been working on this for several years,
and it is basically this model that we use to bring together all the data.
when people come to us for clinical trials.
They have seen their doctors for maybe sometimes several years.
We have a lot of data that we're bringing together for them.
We're sequencing their tumors, we're sequencing their normal tissue.
We're maybe collecting a lot of blood samples from their family members
to see can we figure out what's going on?
You know, I was kind of practicing the talk and I'm like, exactly how am I going to.
Am I going to say this?
That.
We are not doing something that we know, right?
No one knows how the human body works.
Really.
We guess at how it works.
We -- that's what science is.
We take it, we take a hypothesis, we test it.
We think all this makes sense.
And so the closer you look, the more you look,
the more the smaller and smaller you look well, the more variability you see, you see.
And so all of a sudden now we're at the stage of personalized medicine.
So every treatment is going to be personalized for each person.
And so.
So we call this system the connected lab.
And like many of you who use Foundry, you'd be very familiar with this screen, right?
You take your data on the left.
Those are all the systems that you have.
And you can see they're represented by that little color chart on the right.
And then you do a bunch of Python or R or some scripting.
You move into some kind of a standardized data in the middle
and then you actually kind of end up with a report or something
you can actually consume and make sense of on the right.
So, so we of course, do that in the connected lab, looks a little bit more like this.
So.
This is what the scientists were doing by hand before we had Foundry.
So we had one of the labs with seven monitors sitting there
in front because the imaging is stored in the PAC system.
The tissue system is stored in the electronic medical record
or sorry, the lab tests are stored in electronic medical record.
The tissue samples are stored in another system.
So we're very much of a buy not build.
But we when we bought all those pieces, well, how are we going to put all that together?
And so we worked with Foundry for the last couple of years,
because, of course, this is involving humans.
We have to make sure it's right.
We can't just put it together and say,
well, I think that seems good, sir, and here's your shot.
You know, call it.
Call us.
Right.
It has to be right.
And none of these systems are really designed to kind of be integrated.
They were designed to store those things specifically for those things.
And you can go in there and look and have a lot of detail.
So so what one thing is that very end product to the right that is
the connected lab starting point in terms of data and so.
What I'm going to show you is actually notional you know, dummy data.
Okay.
We do a lot of these demos to try to get the scientists to understand
this will make their lives better because since they're in their somewhat independent,
they're very cautious about doing something different.
And so this is a very simple screen of of what they see.
And so you can see there's the name, there's an ID, and then you can see this.
You see it says, familial diagnosis type the HLRCC.
That stands for their hereditary leiomyomatosis renal cell cancer.
Basically hereditary kidney cancer.
And.
Well, that.
Have any of you heard of that?
Of course not.
It's somewhat rare, but that's how cancer is.
It is very fine grained defined
to the point where we have over a thousand different diseases now are considered cancer.
And so this brings together a lot of the a lot of the in this screen,
you can see at the very top, there are seven tabs.
So this this interface is built for us in Palantir.
We we don't currently for this module use a layer on top
which is very helpful for us because we have many little labs we can't you know, we need to show success.
I took some screenshots of Travis's thing where
he used some great words there to say, we have to show success.
We I can't talk about how great a product is.
I got to say, listen, here it is.
Also, I wrote down that what's in it?
What's in it for me?
Because that's a that's a great one, too.
So I just want to go quickly through this and that.
In this disease.
They found out that if we track the tumors and we remove the tumors.
By the time they get to be one inch, the person can live a long time.
If we if we have do it when they're too small, it's too many surgeries.
If we do one or two large, those tumors get out of control.
We can't control them.
So here they monitor.
You can see the left kidney.
Right kidney.
You can't maybe see that one line is left kidney, one on his right kidney.
Now, remember, these people are not sitting in Bethesda with our doctors.
They're at home.
They're with their families.
They're going into their normal doctor and getting these scans done.
And that's they just brought in.
And now the doctors can track this with without question.
Right.
Because it's right.
It's right in it's right in this person's work.
This slide has a ton of detail because I'm on the genomic variants tab,
which -- this gets the massive complexity of of cancer itself.
When you look at it very small you can see a whole bunch of code here.
What does this mean exactly?
And there's two different marks, the QCI variance, the VEP variance,
those are different modeling systems which tell me that this genomic change
is a problem or this genomic change is no problem.
And so the doctors are able to see all of this consolidated.
And remember, each cell has its own set of DNA.
That means it can have its own genetic variation.
Cells are very small and we have a lot of them.
And when one of those cells changes and gets more mutations, it starts to become a more aggressive cancer.
So there was just some more slides in here to get, bringing in the lab clinical tests.
And we can also bring in tissues.
And so the doctors can see that.
But one of the important things I wanted to point out to you was that you see the blue and green buttons?
That will actually take the take the researcher directly back to the imaging system.
And they can then see the whole image analysis being done.
All the samples that were collected on that patient.
So we don't have to bring that all into the connected lab, actually.
It is part of the connected lab.
And Foundry helps us connect back to that system that collects all that.
So I'm going to end really quickly with this slide, and I want to really give props to Sam Michael.
He's one of the CIOs from NIH, he works at NCATS.
He introduced us to to Palantir Foundry.
And when I saw it, I was like, this is exactly what we need to address our data problem.
And I'm so happy that we've been able to work on it and get this get this put in place.
Thank you all for your time.