Hi, I'm Dr. Brian Solow.
I'm glad you decided to join us today
and hope everyone around you is safe and healthy.
Well, many of of you know me already,
let me briefly introduce myself
to those of you new to our Life Sciences Business.
Or from another segment of the health care ecosystem perhaps.
I'm the Chief Medical Officer
for the Life Science Division here at Optum.
Our Life Science teams help pharmaceutical,
biotechnology, medical device companies,
successfully address product development
and commercialization challenges.
Before joining the Life Science Business,
I was the Chief Medical Officer for Optum Rx,
which follow the nearly 25 year career
as a real doctor.
I'm going to spend the next several minutes
talking about our Clinical Genomics Program
here at Optum Life Science,
and why we think this is the next big leap
in real world data.
I'll be joined today by Dr. Jill Hagenkord
who currently serves as the Chief Medical Officer
for Optum Genomics.
Thanks for having me, Brian.
And thanks for joining us, Jill.
And we'll be back with Jill in a little bit.
I wanted to go over first with you
about Optum's unique data set.
And you can see here on this slide
about our expertise in our data linkage, management,
curation, analytics, clinical research,
and consulting experience.
We have over 100 million lives
of electronic health record data,
180 million lives for claims data,
and 60 million lives of that integrated data set
representing over 300 health plans.
We can bring those together in something we call Optum IQ
and that gives us our great curated data.
What we plan to do and are currently
on the process of doing and accomplishing
is adding genomic data to this asset,
and are seeking select partners interested in better,
faster, more inexpensive drug discovery and development.
As we can see,
this next slide shows us the value that the genome sequence
being an important part of this discovery research.
And what we want to do is innovate
this discovery through commercialization.
We know that the very best genotypes come
from high quality clinical and real world data.
We believe that Optum can bring better clinical
and health economic data to power the discovery
of associations between this genetic variation
and the clinical needs.
we believe there's three keys
to this genomic driven drug discovery and development.
One, we want to have access to a large population,
which we are obviously well suited to.
Next we want to have uniform high quality genomic data.
And third, this rich longitudinal phenotypic data.
And you can see that if we can bring these in
and link them together,
this will give us our full potential.
Let me now bring back Dr. Hagenkord,
and help us out and go through some other areas
in this clinical genomics journey.
Dr. Hagenkord is a Board Certified Pathologist
with subspecialty boards in molecular genetic pathology
and an additional fellowship in pathology
and oncology informatics.
She trained at Stanford Medical School,
UFC San Francisco and the University of Iowa.
And she's been an Associate Professor
at Creighton University Medical School.
Subsequently, she served as the Chief Medical Officer
of several Silicon Valley startups such as
InVitae, 23andMe and Color Genomics.
She's an active member of many medical associations.
And what's most exciting is that
she's made the great decision to join the Optum team
to help bring our genomic vision to reality.
Jill, I'll turn it over to you.
And thanks again for including me
in your clinicogenomics session
at the Optum Forum this year.
So I want to start with this slide
and I'm going to spend a few minutes talking through this slide
and some interesting points about it.
But these are all examples of large genotype phenotype
data assets that have been created by different entities,
and then have worked with the pharmaceutical industry
and partnerships for the better, faster,
cheaper drug discovery and development.
And one of the things I think this slide does
is it really shows, it kind of proves that
there is a market.
So if all of these different entities
are putting together these data assets
and then these partnerships exist,
there's at least some appetite and credence
to the hypothesis that having access to this
kind of data asset can facilitate drug discovery
And the genomics team at Optum is new
and a lot of the team that has now joined,
we were either directly involved in a lot of
these entities that you see on the screen here,
or we were closely observing it
from their very inception.
So all of these partnerships
are very familiar to us
and collectively we've got a shared experience
about what worked and what didn't work
what are the things that were harder
to do than maybe they thought when they started off,
what are the things that were easier to do
than they thought when they started off?
And so it gives us the ability
to step back now at this point in time in 2020
and say what are the most differentiating features
about large genotype phenotype data assets
and how does Optum play into that.
And what we see is that Optum is pretty uniquely positioned
to hit the major elements of what would be differentiating
from a drug discovery and development point of view.
And so for example, one of the things that's very important
is the size of the database,
the number of participants in participating
in this study or volunteer cohort.
And the reason that these are important,
the number people is important
is because the types of studies that are typically done
require hundreds of thousands to millions of people
with some kind of shared phenotypic
or genotypic characteristic
to do these broad association studies.
Another reason that they need to be so large is that
we want to try to find what are called,
colloquially we call them genetic heroes
or genetic outliers.
Example of this would be like PCSK9 inhibitors,
which recently came on the market like the last five years,
but if you go backwards in time,
but they now target the PCSK9 gene.
Well, there was a woman in Texas
whose doctor noticed that
she's an otherwise healthy middle aged woman,
but she has surprisingly low LDL cholesterol levels
and they're close to a research center,
Where there's researchers doing cholesterol research,
so they take a look at her genetics
and they realize that why she has really low LDL cholesterol
is because she's missing both copies
of the PCSK9 gene,
she doesn't have a functional copy of that gene.
And so that's making her LDL
very low compared to other people.
But she's otherwise unaffected by this.
It's kind of a natural experiment,
It's like a knockout mouse, but it's a knockout human
that occurred naturally.
And so from the perspective of a drug developer
you can say, well, if I make a drug that inhibits that gene,
I know that I'm going to get,
some it's going to be able to take people
up high LDL cholesterol
and lower it without having significant adverse events
in the human.
And that is incredibly valuable information
and it saves years of time in the research lab
and in the develop phase of the drug
to have that kind of a strong signal
that you're not going to have a lot
of adverse side effects if you target that protein product.
And so these genetic heroes as outliers are rare.
And so you have to be systematic
in collecting your phenotypic information
and looking at that phenotypic information.
we're pretty lucky that that person's primary care doctor
got curious about her low LDL cholesterol level,
but you could imagine that get missed
in an ordinary clinical situation.
And then you also that means they're going to be rare.
So you just have to have
over a million people in your database to start identifying
these valuable genetic heroes.
Another important concept
is what is the source of your phenotypic data.
And this slide highlights 23andMe.
So 23andMe, they do have the large data assets
so they have over 8 million participants
and it is all self reported data,
but they've really managed to prove the model.
I think this slide in general proves the market.
It proves that if you build an asset like this,
there are pharma customers
who are interested in partnering with you.
But up until now, it's just been a hypothesis
that this will really enable better, faster,
cheaper drug discovery and development.
And two months ago
23andMe in partnership with GSK
announced their first clinical trial of a drug that
was discovered in partnership between GSK and 23andMe.
And that partnership was signed in 2018.
And so to go from the signing of that deal in 2018,
to a drug and in man in 2020,
It's pretty remarkable.
And I think we're actually going to see increased interest
in the pharmaceutical industry and having access
to really rich genotype phenotype, data assets.
Other things that are really important
you want to make sure that the data is organized
in a very coherent, secure accessible data architecture.
Which is the core of our business already here at Optum.
So we're just adding genomics,
sensitive genomic data into a lot of other health care data
that is sensitive that we have to have
privacy and security policies around.
And it's the kind of data that we're already used
to handling here,
where a lot of these other entities
that you see here are either ventured as startups
or research funded like government funded research projects.
So essentially, it's either started by like
two grad students in a garage
or two grad students in a university.
But either way, they're kind of like
hacking something together to prove a point
so that they can go get another round of funding.
Oftentimes, they don't come back and do that,
the thought process and re architecturing
that you're going to need to do to scale.
And so when they start to scale that's kind of rickety
held together by duct tape and bubble gum
and not as easy to ask and answer questions out of it
as you would like it to be.
And again, that's something that we're
ahead of the game in here at Optum.
Last few points, you want uniformity of genomic data,
which isn't always true, but something we haven't
and we can control here at Optum.
Ethnic diversity is a real challenge
in all of these databases,
but really in all of medicine.
But at Optum, I think we've got,
we're uniquely positioned
to break across the socio economic
and ethnic barriers that we've seen
other people struggle on in this space.
And then lastly,
the way that we would like to roll this out
is really kind of focusing on making sure
that we've got an ongoing re contactable relationship
with our participants and the ability to get that
longitudinal data as well.
And in my next slide,
the next two slides really are just one example
of kind of some of the things that we've learned
from watching other genotype phenotype data assets
One of the things that a lot of these entities
have struggled with is recruitment.
You can get funding for 500,000 patients
or people and you open the study and nothing happens,
But what they saw here,
Healthy Nevada is the name of this project,
and it was by Renown Health Center and health system
and Desert Research Institute in northern Nevada.
And we see that here, we see the 23andMe,
when you give people back information about themselves
for participating in the study,
they tend to be more engaged and it tends
to be easier to recruit.
People pay to participate in 23andMe.
And so I think of it
it's free for anybody to participate in it,
they don't have to pay for it.
But their goal was to recruit 10,000 people in three months.
And they recruited almost 10,000 people
in less than 48 hours.
And so there really is some benefit to recruiting
when you design the participation and the engagement
in a very thoughtful way.
Some other interesting data points that come out
of this paper have to do with evidence of
the engagement in research.
And so you see that it was of these 10,000 people,
97% of them opted to participate
in future research after getting the results back.
And then we also see that of the people that participated,
it was open to people who weren't part
of the Reno health system.
But when they took part in the study
and they got the results back
40% of the people who weren't previously engaged
with the Renown system became patients at Renown.
So there's some kind of marketing sizzle that happens.
It's like people want to be a part
of a health system or health plan that they feel
is taking care of them, engaging them,
giving them preventative
health information about themselves.
And we'll have an example of what
that looks like on the next slide.
So part of the engagement,
some part of the information that they gave back
to individuals were these three conditions
that are listed on this slide.
So it's two hereditary cancer syndromes
and one hereditary high cholesterol syndrome.
these conditions go largely undiagnosed
in the United States.
Even though they're completely treatable and preventable,
and there's no reason that these people
should go on and present with advanced disease,
we just aren't detecting them in the current health system.
But by offering this information back
to the research participants,
they detected 90% of people
who are otherwise being missed with these conditions.
They were able to intervene
and start providing them preventative health
but both them and they're at risk relatives.
And interestingly, about a quarter of them 26%,
when they enrolled in the study
and got the results back and went into follow up
with their mutations,
they had low stage,
early stage disease present in their body at that time.
So they have an early stage cancer,
or early stage cardiovascular disease,
that they were able to identify
in the very early stages when it's very easy to treat
and inexpensive to treat.
And the survival rate is significantly higher.
Than how they had gone on and presented
later on in time with more advanced disease.
And so there's a lot of gratitude
from the participants
who get these kinds of results back.
And it's really aligned with the
highest level vision and purpose
of what Optum does.
The mission is to help people live healthier lives
and health system work better for everybody.
And this Clinical Genomics program really offers that
on a number of different levels.
Yes, it puts together a big
world class genotype phenotype data asset
for better, faster, cheaper drug discovery and development.
But it also provides engagement,
preventative information back to our,
the patients and providers
and health systems that we work with.
And because of that commitment on the very highest level
and how this kind of data asset is just kind of
inherent in what we do all the time anyway,
Optum is kind of uniquely positioned compared to a lot
of those other genotype phenotype data assets
that you saw on the first slide that I showed you.
And with that, I'm going to pass it back over to Brian.
I think now we've all have heard why Optum.
Is uniquely positioned to accomplish this task
of going into this clinical genomics program.
We have access to this large diverse population,
which many of you license this data already.
And we can obtain,
protect a variety of sensitive self reported
and electronic health record based data.
We can obtain this high quality uniform genomic data.
And lastly, we are going to be able to get
into the recruitment and engagement
with easy access and with those return of results.
It's these robust analytics and experienced data scientists
on the Optum Life Science team
on Jill's new genomics team,
look across the board in Optum
with their medical data quality, privacy, security,
that's going to let us make this reality.
So I'd like to take these next few moments
to finish up and have a conversation with Jill
and ask some of those important questions
that people have been wondering about
that have come up in some of our discussions.
So I think about Optum and I think
about all the things that Optum accomplishes.
Whether it's a pharmacy benefit company,
whether it's all the health plans
and patient care that we do,
are other areas within a hospital space,
but why genomics?
Why should Optum get in genomics?
What's going to differentiate us?
Interesting question and,
but a kind of a boring answer.
Optum has always been involved with genomics.
We had almost a half a million genetic tests
in 2018 that were done on our patients.
It's just up till now we've not added this genomic data
to our data driven insights business.
So it's like I said,
as in the slide section of this presentation,
we're really just
layering in another sensitive
data set into
an existing collection of health data
about our participants.
It's really an extension.
So then I'd like to talk about my other favorite subject,
which is my mother in law.
And I do love her, don't get me wrong.
but I'm thinking the other day
I went and asked her question,
I said, you have arthritis, right?
And she says to me,
"oh yes, I have rheumatoid arthritis.
"I've had it for many years."
And of course, I take care of all her medical records
and her medications, et cetera.
And I've talked to her doctors,
she indeed has osteoarthritis,
she doesn't have rheumatoid arthritis.
Talk to me about,
some people say, well, self reported is just as good,
when I did, and I did my 23andMe,
and luckily, I found I was not adopted but
why do we need this
EHR driven phenotypic data,
why not just patient reported outcome data?
Okay, and this is going to be a longer answer.
All right. We'll go for that.
It's, the answer, the goal is, like the ideal is
to get all of the different types of health information
that impact the health of an individual disease cohort
or of the general population.
And, but especially if you're going to go and focus
on disease cohorts that might be of interest
to the pharmaceutical industry.
You really want to make sure that,
a health system based encounters.
Which are difficult to collect
through pure patient reported data only.
And what we've learned is that patients
are really good and reliable at reporting
certain kinds of data.
Some examples of that would be,
Have you ever had cancer?
And what kind of cancer is it?
People are really good at that.
Have you ever had a heart attack?
Have you ever had a doctor tell you
that you have high cholesterol?
Are you taking a statin?
Those are pretty reliable
types of information you can get from people.
Now when you start getting into numeric data
or stepping into
more remote relatives,
things fall apart pretty quickly.
So people aren't very good at saying,
what dose of statin did you take or are you taking?
What is your cholesterol level?
What subtype of cancer did you have?
So that's where having the patient reported data,
as well as the EHR and claim space data
to collaborate it and make it more granular
is critical, especially in disease cohorts.
But there's also times when you want to
maybe over index on the self reported data,
because it's, a lot of times we'll see that patients
are more likely to be honest
when they feel like they're anonymous
and sitting on their couch,
answering questions about,
how many drinks do you have?
Have you ever been depressed?
Have you ever used illicit drugs?
These are kinds of questions that the data suggests
people are more honest.
When they don't feel like they're being judged by
somebody in a white coat in a clinic.
So there's, again,
benefits to having both types of data
and that's one of the strength in Optum,
is that we actually have access to both kinds of data.
Okay, I think that's key to understanding.
I still try to explain, she still argued with me,
but it was a battle I wasn't going to fight then.
Talk about the potential impact of
the insights, we talked about it,
but some of the unique insights
that we can get from this population?
The mechanism disease, the treatment response,
just briefly touch on that for a second.
Yeah, this is one of the most exciting things to me
and actually, to people on my team about joining Optum.
Is the potential that become what we're calling
the evidence machine.
Cause for those of us who have been
in precision medicine for a long time,
we know that the Human Genome Project
was finished 20 years ago
and 20 years later, there's very few things
that are in routine clinical use,
that people would call precision medicine
or precision health.
And the primary reason for this is lack of access
to large research cohorts, and specifically, outcomes data,
like prove that these new precision medicine techniques
are actually making a difference in the health
or economic benefit of managing a patient.
And this Clinical Genomics program,
in addition to all the other benefits that I listed,
provides kind of a machine for generating evidence
and testing new hypotheses
about these precision medicine products.
And so I just think this can be remarkably impactful
and our ability to accelerate the understanding of disease
and the adoption of precision medicine tools.
And give Optum really the opportunity to be a leader
and have our brand be associated with that.
And again, Jill, thanks for joining me on this
little journey today.
And I wanted to thank our audience
for taking the time and joining us.
And you know how much I look forward to meeting live
with you all again.
Hopefully when it's safe and we're all traveling.
I know the only person happy today
is my dry cleaner,
who will get to wash my first dressed shirt in months.
So again, thanks,
have a great rest of the time at Optum Forum
and we hope to speak with you soon.
Data expansion to accelerate solutions
The clinicogenomics program started by Optum® Life Sciences combines world-class genotype and phenotype data assets for better, faster, cheaper drug discovery and development. It also provides engagement and preventive information to patients, health care providers and health systems.
Join Dr. Brian Solow, chief medical officer, Optum Life Sciences, and Dr. Jill Hagenkord, chief medical officer, Optum® Clinical Genomics as they discuss this next big leap in real-world data.