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Proteomics in Proximity discusses the intersection of proteomics with genomics for drug target discovery, the application of proteomics to reveal disease biomarkers, and current trends in using proteomics to unlock biological mechanisms. Co-hosted by Olink's Dale Yuzuki, Cindy Lawley and Sarantis Chlamydas.
Welcome to the Proteomics in Proximity podcast,
where your co-host Cindy Lawley
and Sarantis Chlamydas
from Olink Proteomics,
talk about the intersection of proteomics
with genomics for drug target discovery,
the application of proteomics
to reveal disease biomarkers,
and current trends in using proteomics
to unlock biological mechanisms.
Here we have your host,
Cindy and Sarantis.
Hey everyone,
welcome to Proteomics in Proximity.
Today we have some exciting guests.
We've got Rory Collins from the UK
Biobank and Chris Whelan from J&J.
We also have my colleague from Olink,
Evan Mills.
I would really like each of you
to introduce yourself.
Talk a little bit about your
why and maybe a little bit
about why you're here today
and what we plan to discuss.
Rory, let's start with you.
Well, thanks very much, Cindy,
for inviting me to talk about, UK Biobank
and this fantastic step forward,
in analyzing proteomics in UK Biobank.
So, fundamentally, I'm a cardiovascular
epidemiologist in clinical trials.
So I've been at the University
of Oxford for the last 40 odd years.
And back in
2005, I was asked by the Wellcome Trust
and the medical Research Council
if I would take on the role
of establishing UK Biobank.
So this cohort of half a million, British
men and women,
who provided
lots of information from questionnaires,
allowed us to measure them
in all sorts of ways and to provide,
biological samples,
in particular blood samples
that we stored, 20 years ago.
We then been following them up
with their consent,
through linkage to all of their medical
and other health related records,
and importantly,
making all of these data available
to scientists around the world,
whether academic or commercial scientists,
to try to understand
the determinants of different diseases
and better ways to prevent
and treat those diseases.
The samples have had biochemistry
done, hematology
done, genetics done on them,
including sequencing.
But what's happening now,
I think, is a massive step forward,
the ability
to measure thousands of proteins
on these very large numbers of individuals
is going to be, a huge, improvement
in our ability to understand
how to better prevent and treat disease.
I love that.
So, Chris, how about you background.
And then I'd love it if you could talk
about, you know, J&J's perspective.
Great to be back, Cindy.
Thanks for having me again.
It's terrific to see Evan on
the podcastthis time as well.
So yes, I'm Chris Whelan
and I lead the Pharma Proteomics project.
I co-founded that consortium about,
five years ago now.
Formally, my PhD was in neuroscience
and in genetics.
But over the last few years,
I've really transitioned and deep dived
into proteome mix.
I think that deep dive was driven
by a desire
to understand, human health and disease
at a much finer grained level.
I ultimately, I want to live in a world
where we can directly monitor and detect
and treat illnesses in a more powerful way
than currently possible.
And, UK Biobank is enabling that.
And why do you see proteomics?
You know, this proteomics project
that that, Rory referenced,
where do you see this
as beneficial to pharma?
Just at a very high level, because I think
we're going to dig into this
more as we along our discussion today.
But I'd love just
your why
because you it's taken a lot of work
to put this together.
There are, you know, 13
pharma partners in the first project.
The pilot of over 54,000 samples.
And now there's 14
pharma partners in this latest iteration.
Sure thing. Yeah, absolutely.
I mean, I'll sound like a broken record
soon, but I'm all about precision
medicine, finding the right drug
for the right patient at the right time.
And I think proteomics will help us
get there quicker than on the other tools
that are currently available.
I think it's the key that unlocks
precision medicine.
So you need a lot of, statistical power
to do proteomics in a,
sort of solid manner.
And I can't think of a better cohort
in which to do a really well power
study than UK Biobank.
So, you know,
as we all know, we've just announced
the latest iteration of the UK
b PGP project.
So it was 13 partners last time
around, its 14 partners this time around.
And the last time
it was about 55,000 samples.
This time we're starting with 300,000,
and we hope to expand that to 600,000
pending additional, sources of funding.
Amazing. Super exciting.
All right, Evan, you're on your why, your
background, whatever you'd like to share.
Sure.
Thanks again, Cindy
it's nice to be back on the podcast.
I had a previous experience,
so, I've been with proteomics companies,
and I would consider next generation
proteomics companies for about 11 years.
I started my career as a research
scientist in neuroscience and oncology.
But my goal was
always to do something
that could actually impact patients.
So I moved into a pharmaceutical,
sales role, which was not very satisfying,
to be frank.
But I've been in the life
science tools business for about 16 years.
And, my goal is to put the best tools
in the hands of the best scientists
on the planet to make meaningful
change towards improving human health.
And so, fortunately,
Chris and I had lunch,
one fateful day in Boston, I think it was
probably, gosh, five years ago now.
And Chris just asked the question.
He said, hey, I'm on the UKB
steering committee.
And, you know,
we were thinking of a phenotypic
data arm, like,
what could we do after sequencing?
Right? Where we were doing whole exome,
we're going to do whole genome.
What do we do next?
And he said, do you think all and could
possibly measure 50,000 samples.
And at the time it was a bit of a pipe
dream, but I kind of knew what was coming.
And I said, I think we can.
And so that began this beautiful process
that brought us to where we are
now, where, I've been in the fortunate
position of, representing a technology
that's really, enabling quite a bit
for the research community and,
working with Rory and team,
you know, the combination of really,
game changing tools
with unique to the world resources
with, people like Chris that have
the passion and motivation to make things
happen, has brought us to where we are.
So, I'm very fortunate
and excited to see what comes after this.
Amazing.
As an aside, I will say the episodes
that Evan and Chris were on, respectively,
are two of the episodes
that I get the most inquiries
on that we get the most hits on.
There are very popular episodes.
In fact, somebody sent me an email
saying that they wanted to work
for Evan after his podcast episode,
so you should go back.
Plus,
we'll put a link to that episode
and Chris's
episode in the show notes,
because those were very good episodes.
The work that you all have
really spearheaded
and, consolidated resources to do
required money.
And that money in part has come
from pharmaceutical companies.
In part, it's come from an investment
in. Olink.
I think Evan, you and I both know
that's been, you know, a lot of
of a subject of internal conversations
where we're really about
advancing precision medicine here at Olink
as well, and understanding diversity.
And I think this next step
will have a lot of diverse samples in it.
You know, ten times the ones that they had
in the first project.
But but when thinking about funding,
certainly
the UK government has been a big supporter
of the UK Biobank.
And so Rory in particular
you if you're in an elevator
and you need to talk to someone from the
NHS or from from Wellcome Trust
or from one of the funding agencies that
where you're
you're familiar with their goals.
What is your pitch about why proteomics
should be done
on a large cohort with outcome data
like the health records in the UK Biobank?
What do you say in that elevator?
Well, the first thing I'd
say is, why should they engage with the UK
Biobank?
So. So what's so important about UK
Biobank?
It's not a research
project or, it's not a national resource.
It's an international resource.
So it's something that's used by thousands
of scientists around the world.
It is the Hubble telescope
or the CERN accelerator
or biological Science.
Now, industry in academia
go to the UK Biobank data,
because it allows them to do things
that they couldn't otherwise do.
And I think the UK government
are proud to have been part
of creating that,
through the Medical Research Council
and obviously
the Wellcome Trust charity.
I think there are two points
that one can make to them
by putting resource into UK
Biobank, creating UK Biobank.
They've leveraged enormous investment,
from external sources.
For the sequencing for
the proteomics
now for imaging of participants,
so from a financial perspective,
their investment is leveraging additional
investment in a resource that is of value
to UK scientists and global scientists.
More importantly, I think
from all of our perspective
is they're leveraging better health.
They're providing data that is allow
that is allowing scientists
around the world to work out better
how to prevent
and treat disease.
And I think what's really important
about the proteomics,
as Chris has alluded to,
is that in a way, it's the common pathway.
There's been lots of excitement
over the last ten,
20 years around genetics.
But the genetics
lead to disease through a pathway,
and that's a common pathway for lifestyle,
environment, genetics and other factors.
And the proteins will be that common
pathway.
And I think that's why the analysis
of proteomics, thousands
of proteins, thousands of pathways
to tell us how lifestyle environment
genetics leads a particular individual
to determine a particular disease.
And that's where I think
we're going to see massive
knowledge generated,
which will help us to work out
how to better prevent and treat disease.
And that would be my rather long elevator
pitch.
But, I was in a tall building.
That's a long elevator ride.
I will say you've created an environment
where the sharing is controlled
and managed and safe
that welcomes international participants
to feel comfortable
interacting with those data.
And that's, I think, a fundamental piece
that I've seen
that people really appreciate there.
Chris, I'd love your thoughts on
how you convince leadership within
not just your company.
How do you support those scientists
that are representing other companies
in speaking to their leadership
about being in a part of
this team effort, this consortium?
Somewhat ironically,
for a proteomics consortium,
we're actually predominantly populated
by human geneticists.
That's actually been a huge,
driver in,
convincing our leadership teams
that and the value of proteomics.
We've been
advocating for the last 5
to 6 years on the value of human genetics
for drug discovery.
I think we've all seen the papers and the
presentations that I've suggested that,
if your drug target
has supporting evidence
from human genetics, it's
at least twice as likely to ultimately
make it to the market
or be approved by regulatory bodies.
But there's still a lot of missing pieces
between, you know, the genetic variant
and the actual disease phenotype.
And I think the proteomics is increasingly
being recognized as a tool that we can
use to bridge that gap and help understand
that and much more finer grained,
molecular level,
what's happening between that pathway
between gene and disease, phenotypes.
So there's growing traction is growing
appreciation from our heads of R&D,
that this is a very important, potentially
transformative new research tool, even.
Any thoughts
you want to share on any of that?
I mean, you had to convince our internal
leadership
of the importance of this project.
I don't think it you know, I think they
they came on board pretty quickly.
But no, but it's a fair questions in the
I mean, to enable really
transformative projects sometimes requires
a commercial entity to take some risk.
And I think that's
exactly what happened here.
The promise of proteomics is, is fairly
clear,
just based on the central dogma
and what you've heard from Rory and Chris.
I mean, there's clear utility
in looking at the proteins, but,
technological limitations and frankly,
cost have been significant barriers.
So I have to give all credit
to John Heimer.
You know, CEO of Olink,
who really had the foresight
to go to the board and push for something
that was simply unheard of
to enable the project.
And I think we can't underscore
the importance of,
you know, companies
that have a really powerful technology.
Sometimes you have to put profits aside
and just think about impact.
And I think this is a
really good example of that.
I love that.
I think it's a technology
that I've described as a rising tide
lifts all boats
and there are many of those technologies
around, for human health
in the context of genetics,
I've seen few that are dull,
I'd say punching above their weight
and, you know, over
delivering what I expected anyway.
So I am very excited about things to,
I'd like to touch back on diversity
in running the entire UK Biobank,
for example,
with the whole exome data, whole
sequencing data, whole genome sequencing
data.
Yeah, there's a large representation
of African diaspora
and South Asian, ancestry
that I just like people to realize.
I just want to point it out,
because that's one of the things
that I'm particularly excited about.
And the plans for running this larger
next step in proteomics.
So I wonder if, if any of you
would like to make comments on that?
Chris, I'll ask you first
if that's all right or we can
we can go to you, Rory.
I'm certainly happy to comment on this.
I mean, as an epidemiologist.
So I think people being much
more similar than dissimilar.
So I find the focus on diversity
a little bit odd in a way.
You know, we are all human.
Blood pressure is strongly
predictive of the risk of stroke
in all ethnic groups.
But cholesterol is strongly predictive
of the risk of cardiovascular disease
in all ethnic groups.
The reason why, as an epidemiologist,
one would want to measure
cholesterol,
for example, in different populations
is that the levels are different
in different populations.
So it was really our work in China
showing that very much lower levels
of cholesterol than we see in the West
were associated with very much lower rates
of coronary artery disease.
That drove our studies in the UK.
To look at lowering cholesterol in people
with so-called normal
cholesterol levels, and demonstrated
that we could lower their risk.
So the reason for thinking about studies
in different settings,
is to be able
to study a wider range of risk
factor levels or to study populations
that have different levels of disease,
higher rates, or lower rates of disease.
If you want to study cerebral hemorrhage,
do your studies in China, not in the UK
or in Western populations,
because it's much more common there.
And so what we need is not, diversity
so much in terms of ethnicity,
but diversity in terms of risk exposure
is the reason why I think that that's
valuable from a genetic perspective,
is that there have been particular
genetic, variants,
if you like,
that, have been in particular populations.
And that makes it very valuable
to be able to study,
genetics in, in different populations.
But but equally, as I say,
for studying environmental or lifestyle,
in different populations as.
You're right that there will be quite
a lot of diversity within UK Biobank,
but not enough to really
look at the full range of exposure levels
and to look at the full range
of disease levels.
But in the same way
that the first 50,000 participants in UK
Biobank having proteomics is a pilot
for doing it in the whole of UK Biobank,
I see UK Biobank
as being a pilot for doing proteomics.
In the other large scale studies
that have been established
in other parts of the world,
in Mexico, in China, in North America,
particularly in Hispanic populations,
and say the all of us study.
So no one study answers all questions.
I think what we're doing
is building on the knowledge we have
and then building on that knowledge.
And that's why I think this is a very,
a very important next step
in understanding
the diversity of human disease.
I like how you flip that
from thinking about diversity, which is a,
a, you
know,
foundationally sort of genetic construct
to looking at representation
of disease state,
because that's where
we're going to be able to understand
more about proteins
showing up in those disease states.
And so representing,
you know, as much of an understanding
in epidemiology as we can.
Yeah, the kind of minority of people from,
African backgrounds
or Asian backgrounds in UK
Biobank will not be the ones that tell us
predominantly about the relevance
of proteins to disease in Africa, Eurasia.
It will be the totality of UK
Biobank that will do that.
That's really helpful. Perspective.
And so with this expansion project,
I mean, as Rory said,
we'll not only capture more samples
from underrepresented populations,
which is absolutely crucial,
but will also capture, in my opinion,
samples from underrepresented illnesses.
So we're going to start by asking
300,000 samples, 250,000.
Approximately of those samples
will be from the baseline visit.
And then an additional approximate 50,000
will be from various repeat assessments.
As somebody who, primarily works
in neuroscience and in rare diseases,
we were maybe somewhat underpowered
to study
certain diseases of interest
in that pilot proteomics data set.
Let's just take an almost like moisty
gravis.
Right?
That's an illness
that I'm quite interested in.
But we only had a couple of dozen cases
in that pilot project.
We'll go from a couple dozen
to hundreds of cases.
Schizophrenia is another good example.
I have a lot of interest in that.
We have maybe 150 cases in the pilot.
We'll go to maybe more than 2000 cases
in the full scale project.
So that will be a game changer
for biomarker discovery.
But it's also incredibly exciting
because of those folks
with repeat samples,
there will be approximately up to 80,000,
maybe up to 40,000
in the first 300 K cohort,
but ultimately up to 80,000 that will have
plasma proteomics on samples
that are collected contemporaneously
with whole body MRI scans.
So that will give us next level
biological granularity.
We can go from microscopic to microscopic.
And that didn't really exist
in the pilot study.
So you can imagine
not just saying if this blood protein
is changed in people
with bipolar disorder, you could say this
blood protein associates with white matter
microstructure
alterations in the corpus
callosum of people with bipolar disorder.
It just gives a level of granularity
that could really be game changing.
Yeah, it
feels like functional genomics,
you know, like
it just feels like we're getting
it doesn't answer all questions.
It's corroborative, perhaps with true
methods of functional genomics.
I just think there's so much potential.
Chris, would you be willing to talk to us
a little bit
about how bringing you
mentioned genetics helps us?
I think there's a Matt
Nelson paper on this.
There's a couple of other publications
around
how genetics helps build confidence
in clinical trial success.
How does, bringing proteins,
genetics and clinical outcome data
like we have in the UK Biobank?
How does that help your company
or pharma company in general,
have more confidence
in the therapeutic targets
that they're building molecules for?
So there's a multitude of ways
that we're using these kinds of data.
I think the lowest hanging fruit,
as you pointed out, Cindy, is specifically
for target discovery.
We mentioned earlier the,
increased
confidence in drug targets that have
supporting evidence from human genetics,
what the protein data allow us to do
when they're combined with
genomics is actually pinpoint
the proteins that we should be targeting.
Obviously, most of the drugs
that we develop are targeting proteins.
They're not targeting genes.
So just finding the gene that's linked
to your disease and having high confidence
in the gene linked to your disease
doesn't get you all the way.
Ultimately, you need to figure out which
protein has a causal link to disease.
So we employ techniques
like Mendelian randomization
that help identify or establish
that causal association with disease.
And we've done this across the board
for, countless disease areas.
The example that I often point to,
because it's my team
at JNJ who did a lot of the work,
is, Parkinson's disease.
We did some proteome genomic modeling.
We identified dozens of new targets
for Parkinson's
disease that weren't previously identified
using traditional Gwas.
So galectin three is a good example there.
We published in that recently
in nature columns.
But we've also identified
inflammatory targets for schizophrenia
and Alzheimer's disease
and a variety of other conditions.
I would say that one of the things
I'm most excited about
in terms of the applications of proteomics
in the context of pharma,
is how we're applying eye on the protein
data themselves in a sort of an unbiased
manner to find insights, new insights
into different kinds of complex illnesses.
So, the example I often point towards
is major depressive disorder, depression.
We are currently writing of a paper
where we've identified
three different, kinds of, depression
based on the proteomics,
one that has a strong
inflammatory component
and one that has a metabolic component,
and one that seems to involve disruptions
to synapses and neurons,
that could potentially lead to new
and tailored treatments for depression.
Pending some further analysis.
You can imagine a world where, you recruit
into your clinical trial based on,
an underlying proteomics signature,
not just a clinical, signature.
So in principle,
I can absolutely see the trajectory
of improving clinical trial success.
And I'm excited to see, once
we've had these data around a while,
what the actual impact is.
Yeah. Thank you.
Yeah.
I mean, that brings to mind a question,
I think, for both, you, Chris and Rory.
You know, Rory as a cardiologist, right.
So some
cardiovascular epidemiologists
and someone who has spent time,
you know, in the world
of caring for patients and individuals.
And Chris does
a very entrepreneurial thinker in this
space who's had firsthand experience
with these data.
You know,
what do you think of the most exciting
near future
possibilities for clinical impact?
Well, I think it comes to the right person
point that Chris has made.
And he gave a beautiful example
there of the depression.
So you,
there's the right treatment
for the right person.
And if there are
more than one type of depression
with more than one kind of pathway,
then the idea that you would use
a specific treatment
for a specific subtype,
I think is exciting,
that probably will take some time,
before you get treatments
that are specific for particular subtypes
where I can see very rapid,
emergence of value from the proteomic
data is is coming back to this right
person.
Can we identify the people
who are at risk of developing a disease
much more precisely
than we do at the present time?
Can we use the proteomic data,
combined with other data to identify
the people who will develop a disease,
and therefore be able to intervene
with treatments?
We already have, in a focused way,
but early in the condition,
and I think that may well be something
that comes out of these data
very rapidly
and could be implemented very rapidly.
Who should we be giving cholesterol
lowering drugs to at the moment?
We wait until they get to a certain age,
pretty much.
Or we wait
until they have a cardiovascular event.
But could we use the genetic data
and the proteomic data to identify
the people who we should intervene
in before their arteries
flare up in order to avoid them?
Ever getting to that point
where they have an event.
It makes sense that the, the genetics
and the polygenic
risk scores are going to going
to tell some of the story.
I think proteins, as we've talked about,
are catching
additional information that are telling us
about the person today.
Well, they are they combine the genetics,
the lifestyle, the environment
that pretty much, you know, to a large
extent the common pathways.
Yeah.
And we've seen lots of publications coming
out recently with the first pilot data
with polygenic risk
scores, protein risk scores and show,
that they,
that they complement each other,
that they're really,
supportive of each other.
Yeah.
No, Chris, I mean, I'm curious
to get your perspectives and thoughts.
I mean, I know that we've certainly had
some conversations on the topic
and it's super exciting,
you know, seeing all the publications.
But, you know, what?
What are your thoughts on near-term
possibilities and what could be tractable?
Yeah, I was going to say I mean,
you and I have talked
for hours and hours over the phone
and over, over a few beers.
On the topic of disease
prediction is something we both are
incredibly passionate about.
And I do think, as Rory says,
that we'll see the implications
of those prediction tools.
I would say by the end of the decade,
I think even shorter term,
we'll probably see
the most clinical update
uptake in the very short term
in pharmaceutical trials.
And I'll say that I'll put my money
where my mouth is.
I think we're already doing this.
We're already
employing proteomics on trials to help
better understand
the impact of the drugs that we are.
You know, that we're putting through phase
one, phase two, phase three.
I'm applying it in our neuroscience
trials, a change showing how
different drugs impact the blood proteome,
with potential implications
for repurposing and for drug filings.
I think I just saw a paper published
in Nature Medicine yesterday, which did
this for, semaglutide showed
the proteomic impact of semaglutide.
So you'll see more and more of that over
the coming years.
I'm sure.
Yeah.
And if I could just share from, you know,
my viewpoint, which is one of supporting
a lot of scientists,
both in the pharmaceutical space and then
in the, you know, more traditionally
academic research grant driven space,
there's a real, and a coming together,
merging is probably the better
word of these worlds, right?
Where there's folks that have these
beautifully characterized cohorts
where if they have the access
to population data from UK Biobank,
they can then kind of hone in on a disease
area of interest that they've spent
perhaps a good chunk of their careers
understanding, leverage, proteomic.
Yes. Look at it
in the context of a large population.
And then there's often
a, you know, triad of, of,
collaboration
with drug development companies.
And I think that's
a really powerful combination because,
you know, you're lending someone's
disease expertise
that's bolstered
with the weight of a population cohort.
And then that can really inform far
more efficient drug development decisions.
For folks, you know, that
that see the value in this. So,
I can just share that.
I think that's incredibly
exciting is happening today.
And the next steps, I believe,
are some version of, of risk scores
and how they can be.
Implemented in some way
that's cost effective, convenient and,
accessible to, to a
as much of the population as possible.
I mean, that's certainly some time away,
but I think it may come more quickly
than people think.
We now have an amazing team
that represents, you know,
many aspects of Thermo Fisher Scientific,
but what comes to mind
is the complementarity of Olink,
you know what Evan calls
the next generation proteomics.
Where does mass spec fit in?
If you can share within that drug
discovery pipeline
for corroborating anything
you're seeing in the UK Biobank data,
is there anything that you can share
about that?
Yeah, certainly.
I think Mass Spec is still viewed in
many ways as the, the, the gold standard.
Within pharma.
We have a, growing
mass spec team at our change
a site in Cambridge,
Massachusetts, led by Harris Bell team and
in many ways, the mass spec
sits alongside the affinity
based proteomics for discovery.
We have an ongoing project for,
movement disorders, where we are
employing both affinity based proteomics,
Olink as well as mass
spectrometry, to identify
potential subtypes of movement disorders.
And the data do very much complement
each other.
We see similar subtypes
using both methods, but with the mass
spec, you know, you can often take it
just that little bit step further.
Especially when you're using tissue
like brain tissue,
you can take a little bit step
further and maybe go a little bit further
looking at proteome forms, etc..
Well, I mean, I think along the lines
of, you know, where this is all going.
I think another important piece
of that puzzle is,
you know, to get the attention
and capture the imagination
of the general public outside of this,
you know, population research community,
drug development community.
I think there may have to be
some sort of killer application
or some sort of moment that raises
people's awareness of the power
and the potential impact of proteomics
and how it could perhaps
impact their own lives.
I mean, Rory,
as someone who's, you know, spent
as much time as anybody
thinking of population epidemiology
and the impact of the resource
you and others have built in the UK,
what do you think a killer app could be?
I always laugh about,
people's perception of health
and the way in which medicine has gone.
So here's, someone who is training
cardiology and has been doing
working in that area for a long time.
I think the general public thinks, well,
you know,
nothing much has happened.
Except if you actually think back
40 years, we had nothing, really
that was useful for controlling blood
pressure, for controlling cholesterol.
You had a heart attack,
you got into a coronary care unit,
you were monitored
and given some pain relief.
The progress in the last
40 years has been phenomenal.
And I think the general public doesn't
really know that.
And maybe that's the right way.
Maybe the thing will be
that what we need to do, as with genetics
and with proteomics, is they just
get incorporated into, the system.
We shouldn't be trying to train the public
or indeed most doctors in genetics
or proteomics or whatever
we need to be, or build systems
where it's like turning on the light.
It just part of the standard things
that happen.
So I think the more invisible it is,
the more likely it is
to really change the way in which,
people are cared for,
in which the NHS works.
We will we will provide better care.
More precisely.
Yeah, it will be precise
population health.
We will be ensuring that we've identified
the people who are at risk
well before they develop the disease.
We will have the kinds of treatments
that Chris is talking about
that are specific for the condition
they are going to develop.
And we will be able to implement
those treatments
in a more precise way for the individuals
who will benefit from them.
And the more
that is kind of like turning on the tap
by turning on the electricity,
by going to the television,
the better the more it is success.
So it sounds like integrated,
woven throughout.
What health care will be in
the future is the killer app.
So woven through, you know, the ability
to understand what proteins are doing well
through our, predictive capabilities
and woven through improved
clinical trials is the way to really make
the biggest impact.
Is that fair to say, Rory?
Yeah.
I have no idea how the internet works.
It just works.
People use it, and that's what you want.
You want this stuff.
You want genetics and proteomics
to not be cutting edge, but just
the things that happen.
And if we can make it like that,
then I think health services
will function so much better
and our governments will get better.
Bang for their bucks or patients.
The public will get better health.
I think Rory's answer was excellent.
I will say, you know, for folks like us,
sort of nerdy folks, super passionate
about proteomics, maybe the proteomics
equivalent of the folks that work
in chat rooms on the internet in 1993.
Right.
We'll probably be looking for,
more subtle signs, or,
a more subtle moment.
I think there might be two
or all of those two different scenarios.
I think the first scenario will be one
in which we can unequivocally show
the proteomics saves millions of dollars
in health care and drug development costs.
Longer term, it's still I shouldn't
really post this to Olink, but it is still
a relatively expensive technology
to implement a higher throughput.
So we need to show that that expense
pays off.
And whether it's through reducing the time
it gets to phase three,
reducing the number of patients
we need for a trial,
or increasing the likelihood
that a drug candidate actually
will turn into a successful treatment.
We just need to show that proteomics
saves money.
Or the second, maybe more powerful
example is if we can show
that proteomics saves lives.
So maybe somebody discovers
stage one cancer using
a proteomic test and gets treated early
enough to go into complete remission.
And that detection of stage one cancer
wasn't possible through any other means
but a proteomic test.
Or maybe, probably a genomic modeling
that identifies a drug target
that turns into a cure for a disease
like multiple sclerosis.
You know,
perhaps maybe some of these, like,
misdiagnosed with the disease,
like Parkinson's disease.
Maybe they have Lewy body dementia.
And the proteomic tests can show that
actually they have a wrong diagnosis.
It's Lewy body dementia,
and they should be on this treatment
instead of this treatment.
So I think we will get there.
I think proteomics can
and will save lives.
And when that happens,
it'll finally be mainstream.
I love it.
So Chris
I love how you just have really been
thinking about these things so clearly.
You're so succinct in how you summarize
the impact you expect in the future.
So I'd like to kind of wind up
can start with you, Chris's.
If there were no resource limitations
and the UK Biobank farmer proteomics
project has been run on the full UK
Biobank with a longitudinal representation
in there.
Imagine a time in the future and it's,
you know, exceeded all your expectations.
No resource limitation.
What do you imagine where you're sitting
today that you would want to enable next?
I promise I will answer the question, but
I'm going to take 30s to just give Rory
an American and I and the whole UK
but Biobank team some credit.
I think it's already a world class cohort,
and I don't think proteomics
at this unprecedented scale
could happen in any other population.
Biobank.
And they've enabled that
kind of an innovation by encouraging open
access, by embracing firm collaborations,
and by really just incorporating
this multi modal framework
that I still believe is unparalleled.
I don't know of any other studies
that have 80,000 MRI scans.
It's phenomenal.
As somebody who I did my postdoc with,
with MRI scans or the Enigma consortium,
and at that time
we were stitching together
scans from different labs
around the world.
And now there's this one study from,
you know, three different sites
across the UK, all with the same scanner.
It's mind boggling.
So it's a really hard act to follow.
I think that the Beatles have already
left the stage right.
So you're going to need the Stones
and Queen and Led Zeppelin and
some Frankenstein.
Put them
on the stage and you might stand a chance
of following up successfully.
So I guess in Biobank terms. Right.
I think that that Frankenstein,
that that would probably be a cohort
that already
has the open access model of UK Biobank.
It already has the longitudinal design,
the large collection
of multimodal data that I mentioned,
including those MRI scans.
But maybe,
maybe in addition, you could add maybe
recruitment of more nonwhite participants.
I think at the time of recruitment
for UCP,
it was very representative of the, UK
population.
But maybe increasing the nonwhite
participants could be useful.
The ability to recall participants
for clinical trials could be useful,
and maybe the integration.
This is more of a sort of,
a pipe dream because it's very specific.
But the integration
of more specialized clinical skills
for someone who works in neuroscience,
I'd love to see the unified
Parkinson's Disease Rating Scale updates,
or maybe the hospital scale
for depression, things like that.
So fantastic.
And Rory, no resource
limitation exceeded all your expectations.
What's next for the UK
Biobank or health care?
As a, epidemiologist?
Well, the great thing
about being involved in UK Biobank
is that my expectations
have always been exceeded by the way
in which the scientists around
the world have used the data.
And, I mean, that was what
the Wellcome Trust and the MRC wanted.
They wanted the data to be used by as many
different imaginations as possible.
And I think that has been really exciting
to watch.
Just how different people have approached
the same data in different ways
and discovered
really interestingly different things. But
we focused a lot on the baseline
samples,
the samples stored from 20 years ago.
I think that, as Chris said,
the repeat samples being combined
with imaging is very interesting.
But I think also what will be interesting
is the change from baseline
to that repeat sample and changes
in proteomic data
and how that predicts disease
subsequently, in the longer term,
we will have that repeat data on 100,000
or so people
who've come to our imaging assessments.
But I think what we should be trying to do
is getting repeat samples
on the whole of the cohort.
Because my view is that
where a proteomic measures
from 20 years ago are likely
to be very strongly predictive of disease,
changes in proteomic measures
are likely
to be even more strongly predictive.
And more specifically predictive
of a particular disease.
Is and the cohort is now maturing.
So what I would like to see
is getting all of the cohort back,
getting biological samples
from all of them, assessing all of them
in terms of their frailty and their aging,
so that one could look to see
how do the baseline
samples relate to aging processes
in all of the participants,
but then look to see how the changes
in the proteomic data
between baseline and, say,
now are associated
with development of disease
in the next five, ten, 15, 20 years.
And I think change in proteomics,
unlike genomics,
is going to be a massively powerful
source of information.
I'll also add to, you know, what,
I think the UK
Biobank done has done exceptionally well
is, created an environment of trust
with the participants.
The altruism of a half million UK
Biobank participants is unbelievable.
I mean, that trust is really critical.
And something that we take, very,
very seriously.
But their altruism is extraordinary.
The fact to Chris's point
that 100,000 of them have been willing
to travel up to 100 miles, spent
five hours going through an imaging visit,
and then 60 to 70% of them
are willing to come and do it again
is unbelievable.
Yeah. It's amazing.
I think, the UK Biobank participants
are the ones who really deserve
all of our respect
and all of our gratitude for what
they're doing
for the health, around the world.
Rory put it very well that, you know,
we should all be grateful
for the resource and the altruism
that's enabled it with UK B.
And I think in the course of this
discussion, I'm struck by perhaps two core
points of, of impact on the worlds
that, that this work can have.
One of them is,
you know, as Chris mentioned, precision
medicine, right drug, right patient.
And that's along the lines of a disease,
endo type exercise where
if you can get enough data
on enough people,
there's probably more than just 1
or 2 kinds of Alzheimer's, right?
There's probably lots of subtypes
for all of these common diseases
that are creating real societal challenge
and dissecting those differences,
and eventually coming up with treatments
to address those differences.
It will have incredible impact.
So, so, so that's one vector
that I'm very excited about.
And I think, you know, to really advance
that, we have to keep doing more.
We need more cohorts. You need volume.
You need n right.
We need a lot of patients to be analyzed.
But the real power of proteomics
is in its dynamic nature.
And the longitudinal data
that we're going to get a really nice
taste of from the full project here,
I think will point
very clearly that perturbation
cohorts, cohorts
that do have multiple time points over
as long a period as is feasible,
will really start to help us understand
the dynamic.
The proteins whose dynamics are
really important for diagnostic purposes.
So I think we need to do
a lot more of everything.
And I'm not just saying that because
I work for a company that supports that.
I just think we really, as a community
will benefit across multiple vectors.
But by continuing this work and,
you know, on a personal level,
I'm committed to, supporting,
you know, the kind of innovation
that John Heimer did
to try to make things happen,
irrespective of commercial gain.
And I hope that we can continue,
partnerships
like the one we've built
and the friendships and relationships,
they've have built with Chris and
and Rory.
I mean, that's the kind of stuff that
matters far more than everything else.
So, yeah, an opportunity of a lifetime.
It really is such a privilege.
We're very fortunate.
John definitely,
you know, deserves his juice.
John Reimer, he really made this happen.
But so did you.
Evan, you've been instrumental.
You know, you mentioned when we had lunch
and I'd had that vision
to conduct proteomics since maybe 2016,
in a cohort like UK Biobank.
But it wasn't until I met Evan the
like the following year
that it became a reality.
I think I came to him with that idea,
and others had maybe
dismissed it slightly or derided it,
but he listened and he believed in it.
He shared it and the, you know, moved
mountains at Olink to make it happen. So,
thanks, Evan.
Thank you. Chris.
Very kind. Awesome.
So, you know, speaking of sort of
what's next in terms of cohorts,
I'll just make a call out to those
who are listening to this podcast
that Olink has an absolute passion,
commitment, excitement
around the matchmaking function
of being able to bring, cohorts
to our pharma partners,
bringing those to our non-farm farm of,
nonprofit partners, to biotech partners,
those folks
who are in search of the right samples
to demonstrate,
an understanding of various diseases.
And so I think we need more cohorts.
We need an understanding of the value
and the
uniqueness of all of the cohorts
that we can,
connect you.
We can build those connections
because that's, I think, really,
an opportunity to bring people together.
With that,
I will say thank you
all for being here to talk about this
phenomenal international resource
that many folks are querying
over and over again
to build an understanding
of the insights over time.
Sarantis will be back with us next time.
And with that, I will bring this episode
of Proteomics in Proximity to a close.
Thank you.
Well, that wraps
up this episode of Proteomics
in Proximity.
Huge thanks to our guests and authors
of such impactful publications.
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