January 17, 2025 • 48 mins
Welcome to the Olink® Proteomics in Proximity podcast!
Below are some useful resources mentioned in this episode:
Olink tools and software
· Olink® Explore 3072, the platform utilized by the UK Biobank to measure ~3000 proteins in plasma: https://olink.com/products-services/explore/
· Olink® Explore HT, Olink’s most advanced solution for high-throughput biomarker discovery, measuring 5400+ proteins simultaneously with a streamlined workflow and industry-leading specificity: https://olink.com/products-services/exploreht/
UK Biobank Pharma Proteomics Project (UKB-PPP), one of the world’s largest scientific studies of blood protein biomarkers conducted to date, https://www.ukbiobank.ac.uk/learn-more-about-uk-biobank/news/uk-biobank-launches-one-of-the-largest-scientific-studies
Press release and news story from UK Biobank:
https://www.ukbiobank.ac.uk/learn-more-about-uk-biobank/news/launch-of-world-s-most-significant-protein-study-set-to-usher-in-new-understanding-for-medicine
News stories:
- Today Programme – Professor Rory Collins live on programme, timestamp 02:36:37
- Times – ‘Game-changing’ study of blood proteins will help fight disease
- Independent – World’s largest proteins study ‘invaluable’ for understanding disease – experts
- Daily Mail – How Alzheimer's could be diagnosed a DECADE earlier following huge study of blood proteins
- Mirror – New blood test reveals risk of developing different diseases in 'new frontier of science'
- Science – Seeking disease insights, UK Biobank launches largest study of human proteins
- Technology Networks – UK Biobank Launches Largest Proteomics Study for Disease Research
- Chemical and Engineering News – Pharma firms team up to study UK proteomics data
Links to referred episodes:
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:05):
Welcome to the Proteomics in Proximity podcast,
where your co-host Cindy Lawleyand Sarantis Chlamydas
from Olink Proteomics,
talk about the intersection of proteomics with genomics for drug target discovery,
the application of proteomicsto reveal disease biomarkers,
and current trends in using proteomicsto unlock biological mechanisms.
(00:26):
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 UKBiobank and Chris Whelan from J&J.
We also have my colleague from Olink,Evan Mills.
(00:46):
I would really like each of youto introduce yourself.
Talk a little bit about yourwhy and maybe a little bit
about why you're here todayand 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,
(01:08):
in analyzing proteomics in UK Biobank.
So, fundamentally, I'm a cardiovascularepidemiologist in clinical trials.
So I've been at the Universityof Oxford for the last 40 odd years.
And back in
2005, I was asked by the Wellcome Trustand the medical Research Council
if I would take on the roleof establishing UK Biobank.
(01:32):
So this cohort of half a million, Britishmen and women,
who providedlots of information from questionnaires,
allowed us to measure themin all sorts of ways and to provide,
biological samples,in particular blood samples
that we stored, 20 years ago.
(01:54):
We then been following them upwith their consent,
through linkage to all of their medicaland 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 understandthe determinants of different diseases
(02:15):
and better ways to preventand treat those diseases.
The samples have had biochemistrydone, hematology
done, genetics done on them,including sequencing.
But what's happening now,I think, is a massive step forward,
the abilityto measure thousands of proteins
on these very large numbers of individuals
(02:39):
is going to be, a huge, improvement
in our ability to understandhow to better prevent and treat disease.
I love that.
So, Chris, how about you background.
And then I'd love it if you could talkabout, 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.
(03:00):
So yes, I'm Chris Whelanand I lead the Pharma Proteomics project.
I co-founded that consortium about,five years ago now.
Formally, my PhD was in neuroscienceand in genetics.
But over the last few years,I've really transitioned and deep dived
into proteome mix.
I think that deep dive was drivenby a desire
to understand, human health and diseaseat a much finer grained level.
(03:23):
I ultimately, I want to live in a worldwhere we can directly monitor and detect
and treat illnesses in a more powerful waythan currently possible.
And, UK Biobank is enabling that.
And why do you see proteomics?
You know, this proteomics projectthat that, Rory referenced,
where do you see thisas beneficial to pharma?
Just at a very high level, because I think
(03:45):
we're going to dig into thismore as we along our discussion today.
But I'd love just
your why
because you it's taken a lot of workto put this together.
There are, you know, 13pharma partners in the first project.
The pilot of over 54,000 samples.
And now there's 14pharma partners in this latest iteration.
(04:06):
Sure thing. Yeah, absolutely.
I mean, I'll sound like a broken recordsoon, but I'm all about precision
medicine, finding the right drugfor the right patient at the right time.
And I think proteomics will help usget there quicker than on the other tools
that are currently available.
I think it's the key that unlocksprecision medicine.
So you need a lot of, statistical powerto do proteomics in a,
(04:27):
sort of solid manner.
And I can't think of a better cohort
in which to do a really well powerstudy than UK Biobank.
So, you know,as we all know, we've just announced
the latest iteration of the UKb PGP project.
So it was 13 partners last timearound, its 14 partners this time around.
And the last timeit was about 55,000 samples.
(04:48):
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, yourbackground, whatever you'd like to share.
Sure.
Thanks again, Cindy it's nice to be back on the podcast.
I had a previous experience,
(05:10):
so, I've been with proteomics companies,
and I would consider next generationproteomics companies for about 11 years.
I started my career as a researchscientist in neuroscience and oncology.
But my goal was
always to do somethingthat could actually impact patients.
So I moved into a pharmaceutical,sales role, which was not very satisfying,
(05:31):
to be frank.
But I've been in the lifescience tools business for about 16 years.
And, my goal is to put the best toolsin the hands of the best scientists
on the planet to make meaningfulchange towards improving human health.
And so, fortunately,Chris and I had lunch,
one fateful day in Boston, I think it wasprobably, gosh, five years ago now.
(05:52):
And Chris just asked the question.
He said, hey, I'm on the UKBsteering 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 couldpossibly measure 50,000 samples.
And at the time it was a bit of a pipedream, but I kind of knew what was coming.
(06:15):
And I said, I think we can.
And so that began this beautiful processthat brought us to where we are
now, where, I've been in the fortunateposition of, representing a technology
that's really, enabling quite a bitfor the research community and,
working with Rory and team,you know, the combination of really,
game changing tools
(06:37):
with unique to the world resourceswith, people like Chris that have
the passion and motivation to make thingshappen, has brought us to where we are.
So, I'm very fortunateand excited to see what comes after this.
Amazing.
As an aside, I will say the episodes
that Evan and Chris were on, respectively,
(06:58):
are two of the episodesthat I get the most inquiries
on that we get the most hits on.
There are very popular episodes.
In fact, somebody sent me an emailsaying that they wanted to work
for Evan after his podcast episode,so you should go back.
Plus,
we'll put a link to that episodeand Chris's
episode in the show notes,because those were very good episodes.
(07:19):
The work that you all have
really spearheaded
and, consolidated resources to do
required money.
And that money in part has comefrom pharmaceutical companies.
(07:39):
In part, it's come from an investmentin. Olink.
I think Evan, you and I both knowthat's been, you know, a lot of
of a subject of internal conversationswhere we're really about
advancing precision medicine here at Olinkas well, and understanding diversity.
And I think this next stepwill have a lot of diverse samples in it.
You know, ten times the ones that they hadin the first project.
(08:03):
But but when thinking about funding,certainly
the UK government has been a big supporterof the UK Biobank.
And so Rory in particularyou if you're in an elevator
and you need to talk to someone from theNHS or from from Wellcome Trust
or from one of the funding agencies that
(08:25):
where you'reyou're familiar with their goals.
What is your pitch about why proteomicsshould 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
(08:46):
say is, why should they engage with the UKBiobank?
So. So what's so important about UKBiobank?
It's not a researchproject or, it's not a national resource.
It's an international resource.
So it's something that's used by thousandsof scientists around the world.
It is the Hubble telescope
(09:06):
or the CERN acceleratoror biological Science.
Now, industry in academiago 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 partof creating that,
(09:26):
through the Medical Research Counciland obviously
the Wellcome Trust charity.
I think there are two pointsthat one can make to them
by putting resource into UKBiobank, creating UK Biobank.
They've leveraged enormous investment,from external sources.
(09:47):
For the sequencing for
the proteomicsnow for imaging of participants,
so from a financial perspective,their investment is leveraging additional
investment in a resource that is of valueto UK scientists and global scientists.
More importantly, I think
(10:08):
from all of our perspective
is they're leveraging better health.
They're providing data that is allowthat is allowing scientists
around the world to work out betterhow to prevent
and treat disease.
And I think what's really importantabout the proteomics,
(10:29):
as Chris has alluded to,
is that in a way, it's the common pathway.
There's been lots of excitementover 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.
(10:53):
And the proteins will be that commonpathway.
And I think that's why the analysisof proteomics, thousands
of proteins, thousands of pathwaysto tell us how lifestyle environment
genetics leads a particular individualto determine a particular disease.
And that's where I thinkwe're going to see massive
(11:17):
knowledge generated,
which will help us to work outhow to better prevent and treat disease.
And that would be my rather long elevatorpitch.
But, I was in a tall building.
That's a long elevator ride.
I will say you've created an environmentwhere the sharing is controlled
(11:39):
and managed and safethat welcomes international participants
to feel comfortableinteracting with those data.
And that's, I think, a fundamental piecethat I've seen
that people really appreciate there.
Chris, I'd love your thoughts on
how you convince leadership withinnot just your company.
How do you support those scientiststhat are representing other companies
(12:05):
in speaking to their leadershipabout being in a part of
this team effort, this consortium?
Somewhat ironically,for a proteomics consortium,
we're actually predominantly populatedby human geneticists.
That's actually been a huge,
driver in,
convincing our leadership teamsthat and the value of proteomics.
(12:29):
We've been
advocating for the last 5
to 6 years on the value of human geneticsfor drug discovery.
I think we've all seen the papers and thepresentations that I've suggested that,
if your drug targethas supporting evidence
from human genetics, it'sat least twice as likely to ultimately
make it to the marketor be approved by regulatory bodies.
(12:51):
But there's still a lot of missing piecesbetween, you know, the genetic variant
and the actual disease phenotype.
And I think the proteomics is increasinglybeing recognized as a tool that we can
use to bridge that gap and help understandthat and much more finer grained,
molecular level,
what's happening between that pathwaybetween gene and disease, phenotypes.
So there's growing traction is growingappreciation from our heads of R&D,
(13:15):
that this is a very important, potentiallytransformative new research tool, even.
Any thoughtsyou want to share on any of that?
I mean, you had to convince our internal
leadershipof the importance of this project.
I don't think it you know, I think theythey came on board pretty quickly.
But no, but it's a fair questions in theI mean, to enable really
(13:38):
transformative projects sometimes requiresa commercial entity to take some risk.
And I think that'sexactly what happened here.
The promise of proteomics is, is fairlyclear,
just based on the central dogmaand what you've heard from Rory and Chris.
I mean, there's clear utilityin looking at the proteins, but,
technological limitations and frankly,cost have been significant barriers.
(14:03):
So I have to give all creditto 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 ofto enable the project.
And I think we can't underscorethe importance of,
you know, companiesthat have a really powerful technology.
(14:24):
Sometimes you have to put profits asideand just think about impact.
And I think this is areally good example of that.
I love that.
I think it's a technologythat I've described as a rising tide
lifts all boatsand there are many of those technologies
around, for human healthin the context of genetics,
(14:45):
I've seen few that are dull,I'd say punching above their weight
and, you know, overdelivering what I expected anyway.
So I am very excited about things to,
I'd like to touch back on diversityin running the entire UK Biobank,
for example,with the whole exome data, whole
(15:05):
sequencing data, whole genome sequencingdata.
Yeah, there's a large representationof African diaspora
and South Asian, ancestrythat I just like people to realize.
I just want to point it out,
because that's one of the thingsthat I'm particularly excited about.
And the plans for running this largernext step in proteomics.
(15:28):
So I wonder if, if any of youwould like to make comments on that?
Chris, I'll ask you firstif 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 muchmore similar than dissimilar.
So I find the focus on diversity
(15:51):
a little bit odd in a way.
You know, we are all human.
Blood pressure is strongly
predictive of the risk of strokein all ethnic groups.
But cholesterol is strongly predictive
of the risk of cardiovascular diseasein all ethnic groups.
The reason why, as an epidemiologist,one would want to measure
(16:14):
cholesterol,for example, in different populations
is that the levels are differentin different populations.
So it was really our work in China
showing that very much lower levelsof cholesterol than we see in the West
were associated with very much lower ratesof coronary artery disease.
(16:34):
That drove our studies in the UK.
To look at lowering cholesterol in peoplewith so-called normal
cholesterol levels, and demonstratedthat we could lower their risk.
So the reason for thinking about studiesin different settings,
is to be able
(16:55):
to study a wider range of riskfactor 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.
(17:15):
And so what we need is not, diversityso much in terms of ethnicity,
but diversity in terms of risk exposureis 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.
(17:38):
And that makes it very valuableto 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,
(18:00):
but not enough to really
look at the full range of exposure levels
and to look at the full rangeof disease levels.
But in the same waythat the first 50,000 participants in UK
Biobank having proteomics is a pilot
for doing it in the whole of UK Biobank,
(18:20):
I see UK Biobankas being a pilot for doing proteomics.
In the other large scale studiesthat 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.
(18:42):
I think what we're doingis 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 understandingthe diversity of human disease.
I like how you flip thatfrom thinking about diversity, which is a,
a, you
know,foundationally sort of genetic construct
(19:04):
to looking at representationof disease state,
because that's wherewe're going to be able to understand
more about proteinsshowing 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,
(19:26):
African backgrounds
or Asian backgrounds in UKBiobank will not be the ones that tell us
predominantly about the relevanceof proteins to disease in Africa, Eurasia.
It will be the totality of UKBiobank 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 samplesfrom underrepresented populations,
(19:47):
which is absolutely crucial,but will also capture, in my opinion,
samples from underrepresented illnesses.
So we're going to start by asking300,000 samples, 250,000.
Approximately of those sampleswill be from the baseline visit.
And then an additional approximate 50,000will be from various repeat assessments.
As somebody who, primarily worksin neuroscience and in rare diseases,
(20:11):
we were maybe somewhat underpoweredto study
certain diseases of interestin that pilot proteomics data set.
Let's just take an almost like moistygravis.
Right?
That's an illnessthat I'm quite interested in.
But we only had a couple of dozen casesin that pilot project.
We'll go from a couple dozento hundreds of cases.
Schizophrenia is another good example.
(20:31):
I have a lot of interest in that.
We have maybe 150 cases in the pilot.
We'll go to maybe more than 2000 casesin the full scale project.
So that will be a game changerfor biomarker discovery.
But it's also incredibly excitingbecause of those folks
with repeat samples,there will be approximately up to 80,000,
maybe up to 40,000in the first 300 K cohort,
(20:54):
but ultimately up to 80,000 that will haveplasma proteomics on samples
that are collected contemporaneouslywith whole body MRI scans.
So that will give us next levelbiological granularity.
We can go from microscopic to microscopic.
And that didn't really existin the pilot study.
So you can imaginenot just saying if this blood protein
(21:14):
is changed in peoplewith bipolar disorder, you could say this
blood protein associates with white mattermicrostructure
alterations in the corpuscallosum of people with bipolar disorder.
It just gives a level of granularitythat could really be game changing.
Yeah, it
feels like functional genomics,you know, like
it just feels like we're gettingit doesn't answer all questions.
(21:37):
It's corroborative, perhaps with truemethods of functional genomics.
I just think there's so much potential.
Chris, would you be willing to talk to usa little bit
about how bringing youmentioned genetics helps us?
I think there's a MattNelson paper on this.
There's a couple of other publicationsaround
how genetics helps build confidencein clinical trial success.
(21:57):
How does, bringing proteins,
genetics and clinical outcome datalike we have in the UK Biobank?
How does that help your company
or pharma company in general,have more confidence
in the therapeutic targetsthat they're building molecules for?
So there's a multitude of waysthat we're using these kinds of data.
(22:20):
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 havesupporting evidence from human genetics,
what the protein data allow us to dowhen they're combined with
genomics is actually pinpointthe proteins that we should be targeting.
Obviously, most of the drugsthat we develop are targeting proteins.
(22:44):
They're not targeting genes.
So just finding the gene that's linkedto your disease and having high confidence
in the gene linked to your diseasedoesn't get you all the way.
Ultimately, you need to figure out whichprotein has a causal link to disease.
So we employ techniqueslike Mendelian randomization
that help identify or establishthat causal association with disease.
And we've done this across the boardfor, countless disease areas.
(23:06):
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 targetsfor Parkinson's
disease that weren't previously identifiedusing traditional Gwas.
So galectin three is a good example there.
We published in that recentlyin nature columns.
But we've also identifiedinflammatory targets for schizophrenia
(23:29):
and Alzheimer's diseaseand a variety of other conditions.
I would say that one of the thingsI'm most excited about
in terms of the applications of proteomicsin the context of pharma,
is how we're applying eye on the proteindata themselves in a sort of an unbiased
manner to find insights, new insightsinto different kinds of complex illnesses.
So, the example I often point towardsis major depressive disorder, depression.
(23:53):
We are currently writing of a paperwhere we've identified
three different, kinds of, depressionbased on the proteomics,
one that has a strong
inflammatory componentand one that has a metabolic component,
and one that seems to involve disruptionsto synapses and neurons,
that could potentially lead to newand tailored treatments for depression.
Pending some further analysis.
(24:14):
You can imagine a world where, you recruitinto your clinical trial based on,
an underlying proteomics signature,not just a clinical, signature.
So in principle,
I can absolutely see the trajectoryof improving clinical trial success.
And I'm excited to see, oncewe've had these data around a while,
what the actual impact is.
(24:34):
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 epidemiologistsand someone who has spent time,
you know, in the worldof caring for patients and individuals.
And Chris doesa very entrepreneurial thinker in this
(24:56):
space who's had firsthand experiencewith these data.
You know,what do you think of the most exciting
near futurepossibilities for clinical impact?
Well, I think it comes to the right person
point that Chris has made.
And he gave a beautiful examplethere of the depression.
(25:19):
So you,
there's the right treatmentfor 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 treatmentfor a specific subtype,
I think is exciting,
(25:41):
that probably will take some time,
before you get treatmentsthat are specific for particular subtypes
where I can see very rapid,
emergence of value from the proteomic
data is is coming back to this rightperson.
Can we identify the peoplewho are at risk of developing a disease
(26:05):
much more preciselythan we do at the present time?
Can we use the proteomic data,
combined with other data to identifythe people who will develop a disease,
and therefore be able to intervenewith treatments?
We already have, in a focused way,but early in the condition,
(26:26):
and I think that may well be somethingthat comes out of these data
very rapidlyand could be implemented very rapidly.
Who should we be giving cholesterollowering drugs to at the moment?
We wait until they get to a certain age,pretty much.
Or we waituntil they have a cardiovascular event.
But could we use the genetic data
(26:48):
and the proteomic data to identifythe people who we should intervene
in before their arteriesflare up in order to avoid them?
Ever getting to that pointwhere they have an event.
It makes sense that the, the geneticsand the polygenic
risk scores are going to goingto tell some of the story.
I think proteins, as we've talked about,are catching
(27:12):
additional information that are telling usabout the person today.
Well, they are they combine the genetics,the lifestyle, the environment
that pretty much, you know, to a largeextent the common pathways.
Yeah.
And we've seen lots of publications comingout recently with the first pilot data
with polygenic riskscores, protein risk scores and show,
(27:32):
that they,that they complement each other,
that they're really,supportive of each other.
Yeah.
No, Chris, I mean, I'm curiousto get your perspectives and thoughts.
I mean, I know that we've certainly hadsome 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-termpossibilities and what could be tractable?
(27:53):
Yeah, I was going to say I mean,you and I have talked
for hours and hours over the phoneand over, over a few beers.
On the topic of diseaseprediction is something we both are
incredibly passionate about.
And I do think, as Rory says,
that we'll see the implicationsof those prediction tools.
I would say by the end of the decade,I think even shorter term,
we'll probably seethe most clinical update
(28:13):
uptake in the very short termin pharmaceutical trials.
And I'll say that I'll put my moneywhere my mouth is.
I think we're already doing this.
We're alreadyemploying proteomics on trials to help
better understandthe impact of the drugs that we are.
You know, that we're putting through phaseone, phase two, phase three.
I'm applying it in our neurosciencetrials, a change showing how
(28:33):
different drugs impact the blood proteome,
with potential implicationsfor repurposing and for drug filings.
I think I just saw a paper publishedin Nature Medicine yesterday, which did
this for, semaglutide showedthe proteomic impact of semaglutide.
So you'll see more and more of that overthe coming years.
I'm sure.
Yeah.
And if I could just share from, you know,my viewpoint, which is one of supporting
(28:55):
a lot of scientists,both in the pharmaceutical space and then
in the, you know, more traditionallyacademic research grant driven space,
there's a real, and a coming together,
merging is probably the betterword of these worlds, right?
Where there's folks that have thesebeautifully characterized cohorts
(29:17):
where if they have the accessto population data from UK Biobank,
they can then kind of hone in on a diseasearea of interest that they've spent
perhaps a good chunk of their careersunderstanding, leverage, proteomic.
Yes. Look at itin the context of a large population.
And then there's oftena, you know, triad of, of,
(29:37):
collaborationwith drug development companies.
And I think that'sa really powerful combination because,
you know, you're lending someone'sdisease expertise
that's bolsteredwith the weight of a population cohort.
And then that can really inform far
more efficient drug development decisions.
For folks, you know, thatthat see the value in this. So,
(30:00):
I can just share that.
I think that's incrediblyexciting 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 aas much of the population as possible.
(30:23):
I mean, that's certainly some time away,
but I think it may come more quicklythan people think.
We now have an amazing team
that represents, you know,many aspects of Thermo Fisher Scientific,
but what comes to mindis the complementarity of Olink,
you know what Evan callsthe next generation proteomics.
Where does mass spec fit in?
(30:44):
If you can share within that drugdiscovery pipeline
for corroborating anythingyou're seeing in the UK Biobank data,
is there anything that you can shareabout that?
Yeah, certainly.
I think Mass Spec is still viewed inmany ways as the, the, the gold standard.
Within pharma.
We have a, growingmass spec team at our change
(31:05):
a site in Cambridge,Massachusetts, led by Harris Bell team and
in many ways, the mass spec
sits alongside the affinitybased proteomics for discovery.
We have an ongoing project for,movement disorders, where we are
employing both affinity based proteomics,Olink as well as mass
(31:25):
spectrometry, to identifypotential subtypes of movement disorders.
And the data do very much complementeach other.
We see similar subtypesusing both methods, but with the mass
spec, you know, you can often take itjust 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 furtherlooking at proteome forms, etc..
(31:46):
Well, I mean, I think along the lines
of, you know, where this is all going.
I think another important pieceof that puzzle is,
you know, to get the attention
and capture the imaginationof the general public outside of this,
you know, population research community,drug development community.
(32:07):
I think there may have to besome sort of killer application
or some sort of moment that raisespeople's awareness of the power
and the potential impact of proteomicsand how it could perhaps
impact their own lives.
I mean, Rory,as someone who's, you know, spent
as much time as anybodythinking of population epidemiology
(32:27):
and the impact of the resourceyou and others have built in the UK,
what do you think a killer app could be?
I always laugh about,
people's perception of healthand the way in which medicine has gone.
So here's, someone who is training
cardiology and has been doing
(32:49):
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 back40 years, we had nothing, really
that was useful for controlling bloodpressure, for controlling cholesterol.
You had a heart attack,you got into a coronary care unit,
you were monitoredand given some pain relief.
(33:11):
The progress in the last40 years has been phenomenal.
And I think the general public doesn'treally know that.
And maybe that's the right way.
Maybe the thing will bethat what we need to do, as with genetics
and with proteomics, is they justget incorporated into, the system.
(33:33):
We shouldn't be trying to train the public
or indeed most doctors in geneticsor proteomics or whatever
we need to be, or build systemswhere it's like turning on the light.
It just part of the standard thingsthat happen.
So I think the more invisible it is,
(33:56):
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 precisepopulation health.
We will be ensuring that we've identified
(34:18):
the people who are at riskwell before they develop the disease.
We will have the kinds of treatmentsthat Chris is talking about
that are specific for the conditionthey are going to develop.
And we will be able to implementthose treatments
in a more precise way for the individualswho will benefit from them.
(34:38):
And the morethat 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 inthe future is the killer app.
(35:00):
So woven through, you know, the abilityto understand what proteins are doing well
through our, predictive capabilitiesand woven through improved
clinical trials is the way to really makethe 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.
(35:21):
You want this stuff.
You want genetics and proteomicsto 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 betterand our governments will get better.
(35:41):
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 proteomicsequivalent of the folks that work
in chat rooms on the internet in 1993.
Right.
We'll probably be looking for,
(36:03):
more subtle signs, or,a more subtle moment.
I think there might be twoor all of those two different scenarios.
I think the first scenario will be onein which we can unequivocally show
the proteomics saves millions of dollarsin health care and drug development costs.
Longer term, it's still I shouldn'treally post this to Olink, but it is still
(36:24):
a relatively expensive technologyto implement a higher throughput.
So we need to show that that expensepays off.
And whether it's through reducing the timeit gets to phase three,
reducing the number of patientswe need for a trial,
or increasing the likelihood
that a drug candidate actuallywill turn into a successful treatment.
We just need to show that proteomicssaves money.
Or the second, maybe more powerfulexample is if we can show
(36:45):
that proteomics saves lives.
So maybe somebody discoversstage one cancer using
a proteomic test and gets treated earlyenough to go into complete remission.
And that detection of stage one cancerwasn't possible through any other means
but a proteomic test.
Or maybe, probably a genomic modelingthat identifies a drug target
that turns into a cure for a diseaselike multiple sclerosis.
(37:09):
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 thatactually they have a wrong diagnosis.
It's Lewy body dementia,
and they should be on this treatmentinstead of this treatment.
So I think we will get there.
I think proteomics canand will save lives.
And when that happens,it'll finally be mainstream.
(37:30):
I love it.
So Chris
I love how you just have really beenthinking about these things so clearly.
You're so succinct in how you summarizethe impact you expect in the future.
So I'd like to kind of wind upcan start with you, Chris's.
If there were no resource limitationsand the UK Biobank farmer proteomics
project has been run on the full UK
(37:53):
Biobank with a longitudinal representationin 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 sittingtoday that you would want to enable next?
I promise I will answer the question, butI'm going to take 30s to just give Rory
(38:17):
an American and I and the whole UKbut Biobank team some credit.
I think it's already a world class cohort,and I don't think proteomics
at this unprecedented scalecould happen in any other population.
Biobank.
And they've enabled thatkind of an innovation by encouraging open
access, by embracing firm collaborations,and by really just incorporating
(38:38):
this multi modal frameworkthat I still believe is unparalleled.
I don't know of any other studiesthat 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 timewe were stitching together
scans from different labsaround the world.
And now there's this one study from,
you know, three different sitesacross the UK, all with the same scanner.
(38:59):
It's mind boggling.
So it's a really hard act to follow.
I think that the Beatles have alreadyleft the stage right.
So you're going to need the Stonesand Queen and Led Zeppelin and
some Frankenstein.
Put them
on the stage and you might stand a chanceof following up successfully.
So I guess in Biobank terms. Right.
I think that that Frankenstein,
that that would probably be a cohortthat already
(39:20):
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 mayberecruitment of more nonwhite participants.
I think at the time of recruitmentfor UCP,
it was very representative of the, UKpopulation.
(39:40):
But maybe increasing the nonwhiteparticipants 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 skillsfor someone who works in neuroscience,
I'd love to see the unifiedParkinson's Disease Rating Scale updates,
(40:01):
or maybe the hospital scalefor depression, things like that.
So fantastic.
And Rory, no resourcelimitation exceeded all your expectations.
What's next for the UKBiobank or health care?
As a, epidemiologist?
Well, the great thingabout being involved in UK Biobank
is that my expectationshave always been exceeded by the way
(40:24):
in which the scientists aroundthe world have used the data.
And, I mean, that was whatthe Wellcome Trust and the MRC wanted.
They wanted the data to be used by as manydifferent imaginations as possible.
And I think that has been really excitingto watch.
Just how different people have approachedthe same data in different ways
and discoveredreally interestingly different things. But
(40:49):
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 combinedwith imaging is very interesting.
But I think also what will be interestingis the change from baseline
(41:10):
to that repeat sample and changesin proteomic data
and how that predicts diseasesubsequently, in the longer term,
we will have that repeat data on 100,000
or so peoplewho've come to our imaging assessments.
But I think what we should be trying to do
(41:30):
is getting repeat sampleson the whole of the cohort.
Because my view is that
where a proteomic measures
from 20 years ago are likelyto be very strongly predictive of disease,
changes in proteomic measures
are likelyto be even more strongly predictive.
(41:51):
And more specifically predictiveof a particular disease.
Is and the cohort is now maturing.
So what I would like to seeis getting all of the cohort back,
getting biological samplesfrom all of them, assessing all of them
in terms of their frailty and their aging,so that one could look to see
(42:12):
how do the baseline
samples relate to aging processesin all of the participants,
but then look to see how the changesin the proteomic data
between baseline and, say,now are associated
with development of diseasein the next five, ten, 15, 20 years.
(42:33):
And I think change in proteomics,unlike genomics,
is going to be a massively powerfulsource 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 trustwith the participants.
(42:55):
The altruism of a half million UKBiobank 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 pointthat 100,000 of them have been willing
to travel up to 100 miles, spentfive hours going through an imaging visit,
(43:19):
and then 60 to 70% of them
are willing to come and do it againis unbelievable.
Yeah. It's amazing.
I think, the UK Biobank participantsare the ones who really deserve
all of our respectand all of our gratitude for what
they're doingfor the health, around the world.
Rory put it very well that, you know,we should all be grateful
(43:41):
for the resource and the altruismthat's enabled it with UK B.
And I think in the course of thisdiscussion, I'm struck by perhaps two core
points of, of impact on the worldsthat, that this work can have.
One of them is,
you know, as Chris mentioned, precisionmedicine, right drug, right patient.
And that's along the lines of a disease,endo type exercise where
(44:04):
if you can get enough dataon enough people,
there's probably more than just 1or 2 kinds of Alzheimer's, right?
There's probably lots of subtypesfor all of these common diseases
that are creating real societal challengeand dissecting those differences,
and eventually coming up with treatmentsto address those differences.
It will have incredible impact.
(44:26):
So, so, so that's one vectorthat I'm very excited about.
And I think, you know, to really advancethat, 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 proteomicsis in its dynamic nature.
And the longitudinal datathat we're going to get a really nice
(44:48):
taste of from the full project here,I think will point
very clearly that perturbationcohorts, cohorts
that do have multiple time points overas long a period as is feasible,
will really start to help us understandthe dynamic.
The proteins whose dynamics arereally important for diagnostic purposes.
(45:10):
So I think we need to doa lot more of everything.
And I'm not just saying that becauseI work for a company that supports that.
I just think we really, as a communitywill 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 innovationthat John Heimer did
(45:32):
to try to make things happen,irrespective of commercial gain.
And I hope that we can continue,partnerships
like the one we've builtand the friendships and relationships,
they've have built with Chris andand Rory.
I mean, that's the kind of stuff thatmatters far more than everything else.
So, yeah, an opportunity of a lifetime.
It really is such a privilege.
(45:53):
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 lunchand 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 yearthat it became a reality.
(46:13):
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, movedmountains at Olink to make it happen. So,
thanks, Evan.
Thank you. Chris.
Very kind. Awesome.
So, you know, speaking of sort ofwhat's next in terms of cohorts,
(46:34):
I'll just make a call out to thosewho are listening to this podcast
that Olink has an absolute passion,commitment, excitement
around the matchmaking functionof being able to bring, cohorts
to our pharma partners,bringing those to our non-farm farm of,
nonprofit partners, to biotech partners,those folks
(46:56):
who are in search of the right samplesto demonstrate,
an understanding of various diseases.
And so I think we need more cohorts.
We need an understanding of the valueand the
uniqueness of all of the cohortsthat we can,
connect you.
We can build those connectionsbecause that's, I think, really,
(47:20):
an opportunity to bring people together.
With that,
I will say thank you
all for being here to talk about thisphenomenal international resource
that many folks are queryingover and over again
to build an understandingof the insights over time.
Sarantis will be back with us next time.
And with that, I will bring this episode
(47:42):
of Proteomics in Proximity to a close.
Thank you.
Well, that wraps
up this episode of Proteomicsin Proximity.
Huge thanks to our guests and authorsof such impactful publications.
I also want to thank you for tuning in.
Really appreciate you being here.
If you enjoyed the content of this
(48:03):
episode, please think about sharing itwith friends or colleagues
you think might be interestedin the content.
In addition, if you'd be willingto head over to Apple or Spotify
or wherever you digest your podcastsand give us a rating and review,
this will help others find the podcast
when they're searching for proteomicsor precision medicine podcasts.
And mostly I want to saywe would love to hear from you.
(48:26):
So we have a dedicated email addresspip@olink.com.
Please reach out.
Let us know what you're interestedin hearing about what you care about,
and any feedback on the episodesthat we have already done so far.
This is all about you,and so we're really keen
to make sure that we're meetingwhat you'd like to hear about.
Thank you so much, and we'll see you soon.
Welcome to the Proteomics in Proximity podcast,
where your co-host Cindy Lawleyand Sarantis Chlamydas
from Olink Proteomics,
talk about the intersection of proteomics with genomics for drug target discovery,
the application of proteomicsto reveal disease biomarkers,
and current trends in using proteomicsto unlock biological mechanisms.
(00:26):
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 UKBiobank and Chris Whelan from J&J.
We also have my colleague from Olink,Evan Mills.
(00:46):
I would really like each of youto introduce yourself.
Talk a little bit about yourwhy and maybe a little bit
about why you're here todayand 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,
(01:08):
in analyzing proteomics in UK Biobank.
So, fundamentally, I'm a cardiovascularepidemiologist in clinical trials.
So I've been at the Universityof Oxford for the last 40 odd years.
And back in
2005, I was asked by the Wellcome Trustand the medical Research Council
if I would take on the roleof establishing UK Biobank.
(01:32):
So this cohort of half a million, Britishmen and women,
who providedlots of information from questionnaires,
allowed us to measure themin all sorts of ways and to provide,
biological samples,in particular blood samples
that we stored, 20 years ago.
(01:54):
We then been following them upwith their consent,
through linkage to all of their medicaland 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 understandthe determinants of different diseases
(02:15):
and better ways to preventand treat those diseases.
The samples have had biochemistrydone, hematology
done, genetics done on them,including sequencing.
But what's happening now,I think, is a massive step forward,
the abilityto measure thousands of proteins
on these very large numbers of individuals
(02:39):
is going to be, a huge, improvement
in our ability to understandhow to better prevent and treat disease.
I love that.
So, Chris, how about you background.
And then I'd love it if you could talkabout, 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.
(03:00):
So yes, I'm Chris Whelanand I lead the Pharma Proteomics project.
I co-founded that consortium about,five years ago now.
Formally, my PhD was in neuroscienceand in genetics.
But over the last few years,I've really transitioned and deep dived
into proteome mix.
I think that deep dive was drivenby a desire
to understand, human health and diseaseat a much finer grained level.
(03:23):
I ultimately, I want to live in a worldwhere we can directly monitor and detect
and treat illnesses in a more powerful waythan currently possible.
And, UK Biobank is enabling that.
And why do you see proteomics?
You know, this proteomics projectthat that, Rory referenced,
where do you see thisas beneficial to pharma?
Just at a very high level, because I think
(03:45):
we're going to dig into thismore as we along our discussion today.
But I'd love just
your why
because you it's taken a lot of workto put this together.
There are, you know, 13pharma partners in the first project.
The pilot of over 54,000 samples.
And now there's 14pharma partners in this latest iteration.
(04:06):
Sure thing. Yeah, absolutely.
I mean, I'll sound like a broken recordsoon, but I'm all about precision
medicine, finding the right drugfor the right patient at the right time.
And I think proteomics will help usget there quicker than on the other tools
that are currently available.
I think it's the key that unlocksprecision medicine.
So you need a lot of, statistical powerto do proteomics in a,
(04:27):
sort of solid manner.
And I can't think of a better cohort
in which to do a really well powerstudy than UK Biobank.
So, you know,as we all know, we've just announced
the latest iteration of the UKb PGP project.
So it was 13 partners last timearound, its 14 partners this time around.
And the last timeit was about 55,000 samples.
(04:48):
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, yourbackground, whatever you'd like to share.
Sure.
Thanks again, Cindy it's nice to be back on the podcast.
I had a previous experience,
(05:10):
so, I've been with proteomics companies,
and I would consider next generationproteomics companies for about 11 years.
I started my career as a researchscientist in neuroscience and oncology.
But my goal was
always to do somethingthat could actually impact patients.
So I moved into a pharmaceutical,sales role, which was not very satisfying,
(05:31):
to be frank.
But I've been in the lifescience tools business for about 16 years.
And, my goal is to put the best toolsin the hands of the best scientists
on the planet to make meaningfulchange towards improving human health.
And so, fortunately,Chris and I had lunch,
one fateful day in Boston, I think it wasprobably, gosh, five years ago now.
(05:52):
And Chris just asked the question.
He said, hey, I'm on the UKBsteering 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 couldpossibly measure 50,000 samples.
And at the time it was a bit of a pipedream, but I kind of knew what was coming.
(06:15):
And I said, I think we can.
And so that began this beautiful processthat brought us to where we are
now, where, I've been in the fortunateposition of, representing a technology
that's really, enabling quite a bitfor the research community and,
working with Rory and team,you know, the combination of really,
game changing tools
(06:37):
with unique to the world resourceswith, people like Chris that have
the passion and motivation to make thingshappen, has brought us to where we are.
So, I'm very fortunateand excited to see what comes after this.
Amazing.
As an aside, I will say the episodes
that Evan and Chris were on, respectively,
(06:58):
are two of the episodesthat I get the most inquiries
on that we get the most hits on.
There are very popular episodes.
In fact, somebody sent me an emailsaying that they wanted to work
for Evan after his podcast episode,so you should go back.
Plus,
we'll put a link to that episodeand Chris's
episode in the show notes,because those were very good episodes.
(07:19):
The work that you all have
really spearheaded
and, consolidated resources to do
required money.
And that money in part has comefrom pharmaceutical companies.
(07:39):
In part, it's come from an investmentin. Olink.
I think Evan, you and I both knowthat's been, you know, a lot of
of a subject of internal conversationswhere we're really about
advancing precision medicine here at Olinkas well, and understanding diversity.
And I think this next stepwill have a lot of diverse samples in it.
You know, ten times the ones that they hadin the first project.
(08:03):
But but when thinking about funding,certainly
the UK government has been a big supporterof the UK Biobank.
And so Rory in particularyou if you're in an elevator
and you need to talk to someone from theNHS or from from Wellcome Trust
or from one of the funding agencies that
(08:25):
where you'reyou're familiar with their goals.
What is your pitch about why proteomicsshould 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
(08:46):
say is, why should they engage with the UKBiobank?
So. So what's so important about UKBiobank?
It's not a researchproject or, it's not a national resource.
It's an international resource.
So it's something that's used by thousandsof scientists around the world.
It is the Hubble telescope
(09:06):
or the CERN acceleratoror biological Science.
Now, industry in academiago 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 partof creating that,
(09:26):
through the Medical Research Counciland obviously
the Wellcome Trust charity.
I think there are two pointsthat one can make to them
by putting resource into UKBiobank, creating UK Biobank.
They've leveraged enormous investment,from external sources.
(09:47):
For the sequencing for
the proteomicsnow for imaging of participants,
so from a financial perspective,their investment is leveraging additional
investment in a resource that is of valueto UK scientists and global scientists.
More importantly, I think
(10:08):
from all of our perspective
is they're leveraging better health.
They're providing data that is allowthat is allowing scientists
around the world to work out betterhow to prevent
and treat disease.
And I think what's really importantabout the proteomics,
(10:29):
as Chris has alluded to,
is that in a way, it's the common pathway.
There's been lots of excitementover 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.
(10:53):
And the proteins will be that commonpathway.
And I think that's why the analysisof proteomics, thousands
of proteins, thousands of pathwaysto tell us how lifestyle environment
genetics leads a particular individualto determine a particular disease.
And that's where I thinkwe're going to see massive
(11:17):
knowledge generated,
which will help us to work outhow to better prevent and treat disease.
And that would be my rather long elevatorpitch.
But, I was in a tall building.
That's a long elevator ride.
I will say you've created an environmentwhere the sharing is controlled
(11:39):
and managed and safethat welcomes international participants
to feel comfortableinteracting with those data.
And that's, I think, a fundamental piecethat I've seen
that people really appreciate there.
Chris, I'd love your thoughts on
how you convince leadership withinnot just your company.
How do you support those scientiststhat are representing other companies
(12:05):
in speaking to their leadershipabout being in a part of
this team effort, this consortium?
Somewhat ironically,for a proteomics consortium,
we're actually predominantly populatedby human geneticists.
That's actually been a huge,
driver in,
convincing our leadership teamsthat and the value of proteomics.
(12:29):
We've been
advocating for the last 5
to 6 years on the value of human geneticsfor drug discovery.
I think we've all seen the papers and thepresentations that I've suggested that,
if your drug targethas supporting evidence
from human genetics, it'sat least twice as likely to ultimately
make it to the marketor be approved by regulatory bodies.
(12:51):
But there's still a lot of missing piecesbetween, you know, the genetic variant
and the actual disease phenotype.
And I think the proteomics is increasinglybeing recognized as a tool that we can
use to bridge that gap and help understandthat and much more finer grained,
molecular level,
what's happening between that pathwaybetween gene and disease, phenotypes.
So there's growing traction is growingappreciation from our heads of R&D,
(13:15):
that this is a very important, potentiallytransformative new research tool, even.
Any thoughtsyou want to share on any of that?
I mean, you had to convince our internal
leadershipof the importance of this project.
I don't think it you know, I think theythey came on board pretty quickly.
But no, but it's a fair questions in theI mean, to enable really
(13:38):
transformative projects sometimes requiresa commercial entity to take some risk.
And I think that'sexactly what happened here.
The promise of proteomics is, is fairlyclear,
just based on the central dogmaand what you've heard from Rory and Chris.
I mean, there's clear utilityin looking at the proteins, but,
technological limitations and frankly,cost have been significant barriers.
(14:03):
So I have to give all creditto 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 ofto enable the project.
And I think we can't underscorethe importance of,
you know, companiesthat have a really powerful technology.
(14:24):
Sometimes you have to put profits asideand just think about impact.
And I think this is areally good example of that.
I love that.
I think it's a technologythat I've described as a rising tide
lifts all boatsand there are many of those technologies
around, for human healthin the context of genetics,
(14:45):
I've seen few that are dull,I'd say punching above their weight
and, you know, overdelivering what I expected anyway.
So I am very excited about things to,
I'd like to touch back on diversityin running the entire UK Biobank,
for example,with the whole exome data, whole
(15:05):
sequencing data, whole genome sequencingdata.
Yeah, there's a large representationof African diaspora
and South Asian, ancestrythat I just like people to realize.
I just want to point it out,
because that's one of the thingsthat I'm particularly excited about.
And the plans for running this largernext step in proteomics.
(15:28):
So I wonder if, if any of youwould like to make comments on that?
Chris, I'll ask you firstif 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 muchmore similar than dissimilar.
So I find the focus on diversity
(15:51):
a little bit odd in a way.
You know, we are all human.
Blood pressure is strongly
predictive of the risk of strokein all ethnic groups.
But cholesterol is strongly predictive
of the risk of cardiovascular diseasein all ethnic groups.
The reason why, as an epidemiologist,one would want to measure
(16:14):
cholesterol,for example, in different populations
is that the levels are differentin different populations.
So it was really our work in China
showing that very much lower levelsof cholesterol than we see in the West
were associated with very much lower ratesof coronary artery disease.
(16:34):
That drove our studies in the UK.
To look at lowering cholesterol in peoplewith so-called normal
cholesterol levels, and demonstratedthat we could lower their risk.
So the reason for thinking about studiesin different settings,
is to be able
(16:55):
to study a wider range of riskfactor 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.
(17:15):
And so what we need is not, diversityso much in terms of ethnicity,
but diversity in terms of risk exposureis 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.
(17:38):
And that makes it very valuableto 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,
(18:00):
but not enough to really
look at the full range of exposure levels
and to look at the full rangeof disease levels.
But in the same waythat the first 50,000 participants in UK
Biobank having proteomics is a pilot
for doing it in the whole of UK Biobank,
(18:20):
I see UK Biobankas being a pilot for doing proteomics.
In the other large scale studiesthat 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.
(18:42):
I think what we're doingis 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 understandingthe diversity of human disease.
I like how you flip thatfrom thinking about diversity, which is a,
a, you
know,foundationally sort of genetic construct
(19:04):
to looking at representationof disease state,
because that's wherewe're going to be able to understand
more about proteinsshowing 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,
(19:26):
African backgrounds
or Asian backgrounds in UKBiobank will not be the ones that tell us
predominantly about the relevanceof proteins to disease in Africa, Eurasia.
It will be the totality of UKBiobank 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 samplesfrom underrepresented populations,
(19:47):
which is absolutely crucial,but will also capture, in my opinion,
samples from underrepresented illnesses.
So we're going to start by asking300,000 samples, 250,000.
Approximately of those sampleswill be from the baseline visit.
And then an additional approximate 50,000will be from various repeat assessments.
As somebody who, primarily worksin neuroscience and in rare diseases,
(20:11):
we were maybe somewhat underpoweredto study
certain diseases of interestin that pilot proteomics data set.
Let's just take an almost like moistygravis.
Right?
That's an illnessthat I'm quite interested in.
But we only had a couple of dozen casesin that pilot project.
We'll go from a couple dozento hundreds of cases.
Schizophrenia is another good example.
(20:31):
I have a lot of interest in that.
We have maybe 150 cases in the pilot.
We'll go to maybe more than 2000 casesin the full scale project.
So that will be a game changerfor biomarker discovery.
But it's also incredibly excitingbecause of those folks
with repeat samples,there will be approximately up to 80,000,
maybe up to 40,000in the first 300 K cohort,
(20:54):
but ultimately up to 80,000 that will haveplasma proteomics on samples
that are collected contemporaneouslywith whole body MRI scans.
So that will give us next levelbiological granularity.
We can go from microscopic to microscopic.
And that didn't really existin the pilot study.
So you can imaginenot just saying if this blood protein
(21:14):
is changed in peoplewith bipolar disorder, you could say this
blood protein associates with white mattermicrostructure
alterations in the corpuscallosum of people with bipolar disorder.
It just gives a level of granularitythat could really be game changing.
Yeah, it
feels like functional genomics,you know, like
it just feels like we're gettingit doesn't answer all questions.
(21:37):
It's corroborative, perhaps with truemethods of functional genomics.
I just think there's so much potential.
Chris, would you be willing to talk to usa little bit
about how bringing youmentioned genetics helps us?
I think there's a MattNelson paper on this.
There's a couple of other publicationsaround
how genetics helps build confidencein clinical trial success.
(21:57):
How does, bringing proteins,
genetics and clinical outcome datalike we have in the UK Biobank?
How does that help your company
or pharma company in general,have more confidence
in the therapeutic targetsthat they're building molecules for?
So there's a multitude of waysthat we're using these kinds of data.
(22:20):
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 havesupporting evidence from human genetics,
what the protein data allow us to dowhen they're combined with
genomics is actually pinpointthe proteins that we should be targeting.
Obviously, most of the drugsthat we develop are targeting proteins.
(22:44):
They're not targeting genes.
So just finding the gene that's linkedto your disease and having high confidence
in the gene linked to your diseasedoesn't get you all the way.
Ultimately, you need to figure out whichprotein has a causal link to disease.
So we employ techniqueslike Mendelian randomization
that help identify or establishthat causal association with disease.
And we've done this across the boardfor, countless disease areas.
(23:06):
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 targetsfor Parkinson's
disease that weren't previously identifiedusing traditional Gwas.
So galectin three is a good example there.
We published in that recentlyin nature columns.
But we've also identifiedinflammatory targets for schizophrenia
(23:29):
and Alzheimer's diseaseand a variety of other conditions.
I would say that one of the thingsI'm most excited about
in terms of the applications of proteomicsin the context of pharma,
is how we're applying eye on the proteindata themselves in a sort of an unbiased
manner to find insights, new insightsinto different kinds of complex illnesses.
So, the example I often point towardsis major depressive disorder, depression.
(23:53):
We are currently writing of a paperwhere we've identified
three different, kinds of, depressionbased on the proteomics,
one that has a strong
inflammatory componentand one that has a metabolic component,
and one that seems to involve disruptionsto synapses and neurons,
that could potentially lead to newand tailored treatments for depression.
Pending some further analysis.
(24:14):
You can imagine a world where, you recruitinto your clinical trial based on,
an underlying proteomics signature,not just a clinical, signature.
So in principle,
I can absolutely see the trajectoryof improving clinical trial success.
And I'm excited to see, oncewe've had these data around a while,
what the actual impact is.
(24:34):
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 epidemiologistsand someone who has spent time,
you know, in the worldof caring for patients and individuals.
And Chris doesa very entrepreneurial thinker in this
(24:56):
space who's had firsthand experiencewith these data.
You know,what do you think of the most exciting
near futurepossibilities for clinical impact?
Well, I think it comes to the right person
point that Chris has made.
And he gave a beautiful examplethere of the depression.
(25:19):
So you,
there's the right treatmentfor 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 treatmentfor a specific subtype,
I think is exciting,
(25:41):
that probably will take some time,
before you get treatmentsthat are specific for particular subtypes
where I can see very rapid,
emergence of value from the proteomic
data is is coming back to this rightperson.
Can we identify the peoplewho are at risk of developing a disease
(26:05):
much more preciselythan we do at the present time?
Can we use the proteomic data,
combined with other data to identifythe people who will develop a disease,
and therefore be able to intervenewith treatments?
We already have, in a focused way,but early in the condition,
(26:26):
and I think that may well be somethingthat comes out of these data
very rapidlyand could be implemented very rapidly.
Who should we be giving cholesterollowering drugs to at the moment?
We wait until they get to a certain age,pretty much.
Or we waituntil they have a cardiovascular event.
But could we use the genetic data
(26:48):
and the proteomic data to identifythe people who we should intervene
in before their arteriesflare up in order to avoid them?
Ever getting to that pointwhere they have an event.
It makes sense that the, the geneticsand the polygenic
risk scores are going to goingto tell some of the story.
I think proteins, as we've talked about,are catching
(27:12):
additional information that are telling usabout the person today.
Well, they are they combine the genetics,the lifestyle, the environment
that pretty much, you know, to a largeextent the common pathways.
Yeah.
And we've seen lots of publications comingout recently with the first pilot data
with polygenic riskscores, protein risk scores and show,
(27:32):
that they,that they complement each other,
that they're really,supportive of each other.
Yeah.
No, Chris, I mean, I'm curiousto get your perspectives and thoughts.
I mean, I know that we've certainly hadsome 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-termpossibilities and what could be tractable?
(27:53):
Yeah, I was going to say I mean,you and I have talked
for hours and hours over the phoneand over, over a few beers.
On the topic of diseaseprediction is something we both are
incredibly passionate about.
And I do think, as Rory says,
that we'll see the implicationsof those prediction tools.
I would say by the end of the decade,I think even shorter term,
we'll probably seethe most clinical update
(28:13):
uptake in the very short termin pharmaceutical trials.
And I'll say that I'll put my moneywhere my mouth is.
I think we're already doing this.
We're alreadyemploying proteomics on trials to help
better understandthe impact of the drugs that we are.
You know, that we're putting through phaseone, phase two, phase three.
I'm applying it in our neurosciencetrials, a change showing how
(28:33):
different drugs impact the blood proteome,
with potential implicationsfor repurposing and for drug filings.
I think I just saw a paper publishedin Nature Medicine yesterday, which did
this for, semaglutide showedthe proteomic impact of semaglutide.
So you'll see more and more of that overthe coming years.
I'm sure.
Yeah.
And if I could just share from, you know,my viewpoint, which is one of supporting
(28:55):
a lot of scientists,both in the pharmaceutical space and then
in the, you know, more traditionallyacademic research grant driven space,
there's a real, and a coming together,
merging is probably the betterword of these worlds, right?
Where there's folks that have thesebeautifully characterized cohorts
(29:17):
where if they have the accessto population data from UK Biobank,
they can then kind of hone in on a diseasearea of interest that they've spent
perhaps a good chunk of their careersunderstanding, leverage, proteomic.
Yes. Look at itin the context of a large population.
And then there's oftena, you know, triad of, of,
(29:37):
collaborationwith drug development companies.
And I think that'sa really powerful combination because,
you know, you're lending someone'sdisease expertise
that's bolsteredwith the weight of a population cohort.
And then that can really inform far
more efficient drug development decisions.
For folks, you know, thatthat see the value in this. So,
(30:00):
I can just share that.
I think that's incrediblyexciting 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 aas much of the population as possible.
(30:23):
I mean, that's certainly some time away,
but I think it may come more quicklythan people think.
We now have an amazing team
that represents, you know,many aspects of Thermo Fisher Scientific,
but what comes to mindis the complementarity of Olink,
you know what Evan callsthe next generation proteomics.
Where does mass spec fit in?
(30:44):
If you can share within that drugdiscovery pipeline
for corroborating anythingyou're seeing in the UK Biobank data,
is there anything that you can shareabout that?
Yeah, certainly.
I think Mass Spec is still viewed inmany ways as the, the, the gold standard.
Within pharma.
We have a, growingmass spec team at our change
(31:05):
a site in Cambridge,Massachusetts, led by Harris Bell team and
in many ways, the mass spec
sits alongside the affinitybased proteomics for discovery.
We have an ongoing project for,movement disorders, where we are
employing both affinity based proteomics,Olink as well as mass
(31:25):
spectrometry, to identifypotential subtypes of movement disorders.
And the data do very much complementeach other.
We see similar subtypesusing both methods, but with the mass
spec, you know, you can often take itjust 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 furtherlooking at proteome forms, etc..
(31:46):
Well, I mean, I think along the lines
of, you know, where this is all going.
I think another important pieceof that puzzle is,
you know, to get the attention
and capture the imaginationof the general public outside of this,
you know, population research community,drug development community.
(32:07):
I think there may have to besome sort of killer application
or some sort of moment that raisespeople's awareness of the power
and the potential impact of proteomicsand how it could perhaps
impact their own lives.
I mean, Rory,as someone who's, you know, spent
as much time as anybodythinking of population epidemiology
(32:27):
and the impact of the resourceyou and others have built in the UK,
what do you think a killer app could be?
I always laugh about,
people's perception of healthand the way in which medicine has gone.
So here's, someone who is training
cardiology and has been doing
(32:49):
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 back40 years, we had nothing, really
that was useful for controlling bloodpressure, for controlling cholesterol.
You had a heart attack,you got into a coronary care unit,
you were monitoredand given some pain relief.
(33:11):
The progress in the last40 years has been phenomenal.
And I think the general public doesn'treally know that.
And maybe that's the right way.
Maybe the thing will bethat what we need to do, as with genetics
and with proteomics, is they justget incorporated into, the system.
(33:33):
We shouldn't be trying to train the public
or indeed most doctors in geneticsor proteomics or whatever
we need to be, or build systemswhere it's like turning on the light.
It just part of the standard thingsthat happen.
So I think the more invisible it is,
(33:56):
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 precisepopulation health.
We will be ensuring that we've identified
(34:18):
the people who are at riskwell before they develop the disease.
We will have the kinds of treatmentsthat Chris is talking about
that are specific for the conditionthey are going to develop.
And we will be able to implementthose treatments
in a more precise way for the individualswho will benefit from them.
(34:38):
And the morethat 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 inthe future is the killer app.
(35:00):
So woven through, you know, the abilityto understand what proteins are doing well
through our, predictive capabilitiesand woven through improved
clinical trials is the way to really makethe 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.
(35:21):
You want this stuff.
You want genetics and proteomicsto 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 betterand our governments will get better.
(35:41):
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 proteomicsequivalent of the folks that work
in chat rooms on the internet in 1993.
Right.
We'll probably be looking for,
(36:03):
more subtle signs, or,a more subtle moment.
I think there might be twoor all of those two different scenarios.
I think the first scenario will be onein which we can unequivocally show
the proteomics saves millions of dollarsin health care and drug development costs.
Longer term, it's still I shouldn'treally post this to Olink, but it is still
(36:24):
a relatively expensive technologyto implement a higher throughput.
So we need to show that that expensepays off.
And whether it's through reducing the timeit gets to phase three,
reducing the number of patientswe need for a trial,
or increasing the likelihood
that a drug candidate actuallywill turn into a successful treatment.
We just need to show that proteomicssaves money.
Or the second, maybe more powerfulexample is if we can show
(36:45):
that proteomics saves lives.
So maybe somebody discoversstage one cancer using
a proteomic test and gets treated earlyenough to go into complete remission.
And that detection of stage one cancerwasn't possible through any other means
but a proteomic test.
Or maybe, probably a genomic modelingthat identifies a drug target
that turns into a cure for a diseaselike multiple sclerosis.
(37:09):
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 thatactually they have a wrong diagnosis.
It's Lewy body dementia,
and they should be on this treatmentinstead of this treatment.
So I think we will get there.
I think proteomics canand will save lives.
And when that happens,it'll finally be mainstream.
(37:30):
I love it.
So Chris
I love how you just have really beenthinking about these things so clearly.
You're so succinct in how you summarizethe impact you expect in the future.
So I'd like to kind of wind upcan start with you, Chris's.
If there were no resource limitationsand the UK Biobank farmer proteomics
project has been run on the full UK
(37:53):
Biobank with a longitudinal representationin 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 sittingtoday that you would want to enable next?
I promise I will answer the question, butI'm going to take 30s to just give Rory
(38:17):
an American and I and the whole UKbut Biobank team some credit.
I think it's already a world class cohort,and I don't think proteomics
at this unprecedented scalecould happen in any other population.
Biobank.
And they've enabled thatkind of an innovation by encouraging open
access, by embracing firm collaborations,and by really just incorporating
(38:38):
this multi modal frameworkthat I still believe is unparalleled.
I don't know of any other studiesthat 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 timewe were stitching together
scans from different labsaround the world.
And now there's this one study from,
you know, three different sitesacross the UK, all with the same scanner.
(38:59):
It's mind boggling.
So it's a really hard act to follow.
I think that the Beatles have alreadyleft the stage right.
So you're going to need the Stonesand Queen and Led Zeppelin and
some Frankenstein.
Put them
on the stage and you might stand a chanceof following up successfully.
So I guess in Biobank terms. Right.
I think that that Frankenstein,
that that would probably be a cohortthat already
(39:20):
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 mayberecruitment of more nonwhite participants.
I think at the time of recruitmentfor UCP,
it was very representative of the, UKpopulation.
(39:40):
But maybe increasing the nonwhiteparticipants 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 skillsfor someone who works in neuroscience,
I'd love to see the unifiedParkinson's Disease Rating Scale updates,
(40:01):
or maybe the hospital scalefor depression, things like that.
So fantastic.
And Rory, no resourcelimitation exceeded all your expectations.
What's next for the UKBiobank or health care?
As a, epidemiologist?
Well, the great thingabout being involved in UK Biobank
is that my expectationshave always been exceeded by the way
(40:24):
in which the scientists aroundthe world have used the data.
And, I mean, that was whatthe Wellcome Trust and the MRC wanted.
They wanted the data to be used by as manydifferent imaginations as possible.
And I think that has been really excitingto watch.
Just how different people have approachedthe same data in different ways
and discoveredreally interestingly different things. But
(40:49):
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 combinedwith imaging is very interesting.
But I think also what will be interestingis the change from baseline
(41:10):
to that repeat sample and changesin proteomic data
and how that predicts diseasesubsequently, in the longer term,
we will have that repeat data on 100,000
or so peoplewho've come to our imaging assessments.
But I think what we should be trying to do
(41:30):
is getting repeat sampleson the whole of the cohort.
Because my view is that
where a proteomic measures
from 20 years ago are likelyto be very strongly predictive of disease,
changes in proteomic measures
are likelyto be even more strongly predictive.
(41:51):
And more specifically predictiveof a particular disease.
Is and the cohort is now maturing.
So what I would like to seeis getting all of the cohort back,
getting biological samplesfrom all of them, assessing all of them
in terms of their frailty and their aging,so that one could look to see
(42:12):
how do the baseline
samples relate to aging processesin all of the participants,
but then look to see how the changesin the proteomic data
between baseline and, say,now are associated
with development of diseasein the next five, ten, 15, 20 years.
(42:33):
And I think change in proteomics,unlike genomics,
is going to be a massively powerfulsource 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 trustwith the participants.
(42:55):
The altruism of a half million UKBiobank 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 pointthat 100,000 of them have been willing
to travel up to 100 miles, spentfive hours going through an imaging visit,
(43:19):
and then 60 to 70% of them
are willing to come and do it againis unbelievable.
Yeah. It's amazing.
I think, the UK Biobank participantsare the ones who really deserve
all of our respectand all of our gratitude for what
they're doingfor the health, around the world.
Rory put it very well that, you know,we should all be grateful
(43:41):
for the resource and the altruismthat's enabled it with UK B.
And I think in the course of thisdiscussion, I'm struck by perhaps two core
points of, of impact on the worldsthat, that this work can have.
One of them is,
you know, as Chris mentioned, precisionmedicine, right drug, right patient.
And that's along the lines of a disease,endo type exercise where
(44:04):
if you can get enough dataon enough people,
there's probably more than just 1or 2 kinds of Alzheimer's, right?
There's probably lots of subtypesfor all of these common diseases
that are creating real societal challengeand dissecting those differences,
and eventually coming up with treatmentsto address those differences.
It will have incredible impact.
(44:26):
So, so, so that's one vectorthat I'm very excited about.
And I think, you know, to really advancethat, 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 proteomicsis in its dynamic nature.
And the longitudinal datathat we're going to get a really nice
(44:48):
taste of from the full project here,I think will point
very clearly that perturbationcohorts, cohorts
that do have multiple time points overas long a period as is feasible,
will really start to help us understandthe dynamic.
The proteins whose dynamics arereally important for diagnostic purposes.
(45:10):
So I think we need to doa lot more of everything.
And I'm not just saying that becauseI work for a company that supports that.
I just think we really, as a communitywill 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 innovationthat John Heimer did
(45:32):
to try to make things happen,irrespective of commercial gain.
And I hope that we can continue,partnerships
like the one we've builtand the friendships and relationships,
they've have built with Chris andand Rory.
I mean, that's the kind of stuff thatmatters far more than everything else.
So, yeah, an opportunity of a lifetime.
It really is such a privilege.
(45:53):
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 lunchand 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 yearthat it became a reality.
(46:13):
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, movedmountains at Olink to make it happen. So,
thanks, Evan.
Thank you. Chris.
Very kind. Awesome.
So, you know, speaking of sort ofwhat's next in terms of cohorts,
(46:34):
I'll just make a call out to thosewho are listening to this podcast
that Olink has an absolute passion,commitment, excitement
around the matchmaking functionof being able to bring, cohorts
to our pharma partners,bringing those to our non-farm farm of,
nonprofit partners, to biotech partners,those folks
(46:56):
who are in search of the right samplesto demonstrate,
an understanding of various diseases.
And so I think we need more cohorts.
We need an understanding of the valueand the
uniqueness of all of the cohortsthat we can,
connect you.
We can build those connectionsbecause that's, I think, really,
(47:20):
an opportunity to bring people together.
With that,
I will say thank you
all for being here to talk about thisphenomenal international resource
that many folks are queryingover and over again
to build an understandingof the insights over time.
Sarantis will be back with us next time.
And with that, I will bring this episode
(47:42):
of Proteomics in Proximity to a close.
Thank you.
Well, that wraps
up this episode of Proteomicsin Proximity.
Huge thanks to our guests and authorsof such impactful publications.
I also want to thank you for tuning in.
Really appreciate you being here.
If you enjoyed the content of this
(48:03):
episode, please think about sharing itwith friends or colleagues
you think might be interestedin the content.
In addition, if you'd be willingto head over to Apple or Spotify
or wherever you digest your podcastsand give us a rating and review,
this will help others find the podcast
when they're searching for proteomicsor precision medicine podcasts.
And mostly I want to saywe would love to hear from you.
(48:26):
So we have a dedicated email addresspip@olink.com.
Please reach out.
Let us know what you're interestedin hearing about what you care about,
and any feedback on the episodesthat we have already done so far.
This is all about you,and so we're really keen
to make sure that we're meetingwhat you'd like to hear about.
Thank you so much, and we'll see you soon.
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