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Welcome
to the Proteomics in Proximity podcast,

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where your co-host Cindy Lawley
and Sarantis Chlamydas from Olink

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Proteomics talk about the intersection of
proteomics with genomics for drug target

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discovery, the application of proteomics
to reveal disease biomarkers,

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and current trends in using proteomics
to unlock biological mechanisms.

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Here we have your host,
Cindy and Sarantis.

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Welcome, everybody.

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I'm back from holidays
and it's my first episode for 2025.

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Happy to see you all again.

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Happy to see Cindy.

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And I'm really excited to discuss
with Jenny and discuss about proteins and,

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yeah,
looking forward to hear from you, Jenny.

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Excellent.

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So it's Cindy here,
also here with Sarantis and our vice

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president of product management,
Jenny Samskog.

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Jenny, there's a little bit of a question
about how to pronounce

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your first name, so I'd love it
if first you told us about that.

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Secondly,
if you could tell us about, your role,

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what you've seen evolve in proteomics,
you've got a pretty prestigious title.

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And today we want to talk a little bit
about what's coming up.

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In the future, we have a this recent
launch that we'd love you to characterize.

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And then we'll talk a little bit

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about some of the meetings
where we'll be attending.

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Please take it away.

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Thank you so much for having me.

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I'm really excited to be in this podcast
with you guys.

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So my first name is Jenny.

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So it's a soft J, that's Swedish.

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And I

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would just like to comment a bit on,
you know,

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where I come from.

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So you understand my history

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and I would say my main common denominator

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is really protein science
and product development.

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So I did start my career in mass
spec proteomics as a researcher.

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And after that I refocused to support,

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by biopharmaceutical research
and manufacturing.

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And my main function has so far
been within product management,

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which in essence means
developing new products and ensuring

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that the existing products that we have
are meeting our customer expectations.

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And you're so good at leading a team
that listens to customers.

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So I just want to acknowledge
and appreciate you for that.

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It's such a pleasure to be here
and have a have a product management

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function that really, really listens.

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And I would say,
thank you, Cindy, but I would really say,

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you know, I joined Olink, what can it be
like three years ago or something.

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And their focus on innovation

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and advancing proteomics
is very special and very unique.

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It was

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for me, it was a match made in heaven
because I could combine having

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great products out in the market

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that contributes to cutting edge science.

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But the culture of innovation at Olink

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has been there since start
and no credit to me there.

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But it's really nice to be able
to continue that culture of innovation.

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You picked us and we picked you.

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It's a match made in heaven.

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I absolutely love that.

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What's your why? Why proteomics?

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Why do you see such promise in this space?

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Well, you know, I think it's been,

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I just have to go back to where I started.

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So I did my research a long time ago,

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within proteomics
and, as CMS or mass spec.

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And at the time, it was
a fascinating area, but it was early days.

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So at that time, you know, identifying,

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you could identify a handful of proteins.

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And then I was happy, if I could say
like 30 proteins or something

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out from the mass spec.

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And not only that, but, you know,

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the way we identified the proteins
could be based on one peptide.

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And that peptide.

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I had sequenced myself
in the mass spectrum.

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So there were very limited
amount of digital tools to support that.

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Not much like intelligence software
or anything like that.

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So that's where I sort of started.

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That's where I,

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and then I left that for it
for quite some years, actually, to go

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to this more biopharmaceutical, industry
and then came back to Olink.

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And it was, you know, it's

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groundbreaking
how much things have happened since then.

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So, the fact that you can study
thousands of proteins,

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the connection that we automatically
almost have to genomics.

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It's definitely a new era.

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So, I would say it's

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a huge

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thing that kind of happened in proteomics
since beginning of 2000 until now.

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Yeah.

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I'd, I'd say that, and as you know,
I've got a history in the genomic space

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that we've been trying to get at
proteomics from the genomics side as well.

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From our first RNA seq experiment. Right.

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So those first sequencers
that Illumina made, the 1G,

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they were going out the door for folks
that were doing digital gene expression

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at the time, had been using gene arrays,
gene expression arrays

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and were keen to to understand the links
to real time biology.

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And now, as part of Thermo
Fisher Scientific, we have both the mass

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spec size and some amazing innovations
in the astrol and the stellar there,

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as well as as this,
proximity extension assay component.

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But what do you

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think it was there actually,
that breaking point

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that makes a difference for proteomics
to be more democratic?

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Is it the NGS itself?

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Or the NGS plus other protocols
that would be integrated.

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What is your feeling there?

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Well, you know, if we're talking as well
about mass spec here now,

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I mean it's not our a core area,
but it's definitely our college area.

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And you know, within mass
within proteomics, mass spec is still a

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gold standard but also here and remember,
this is not my area of expertise anymore.

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But there's a huge amount of things
that have happened here.

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And I would say mainly or a lot of things,

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of course, on the technology side
to make sure that we actually can,

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have a much greater proteome coverage,
obviously.

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But also, the digital tools, I should say,

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how do you understand the results,

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how did you quality
assess the results, etc.?

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And the supporting tools to do that.

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I think that's been for me.

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When you've been away for a few years
and you come back to see that

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both in within Olink, obviously
who is really spearheading this market.

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But also, what I have seen from

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the outside has happened in the mass spec
area as well.

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Yeah.

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So just specifically around that Olink,

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component within the Thermo Fisher

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environment,
you know, the proximity extension assay,

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first launched on the qPCR readout,

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in 2020, launched on the NGS readout.

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So that transition to be able to look
at more proteins across the proteome,

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particularly in plasma CSF,
some of these liquid media were

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mass spec may not be able to see those low
abundant proteins.

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I think that that was game
changing for me,

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and that attracted me to this team
and this technology.

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Jenny, you just had an announcement from
your team about this new reveal products.

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Can you tell us a little bit about where
that fits in to the democratization?

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Yes. And so yeah, and thank you for

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for highlighting the democratization
because that's one thing.

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So proteomics has been you know, it's

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not for
everyone or hasn't been for everyone yet.

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One of our overarching goals,

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within product development is to make sure

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or to enable our customers
to utilize proteomics,

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and make it accessible
to a broader research community.

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And some of these accessibility

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challenges that we're trying to address,

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have been noted elsewhere
many, many times.

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One of them being cost.

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So, to be able to run large
proteomics studies or to be able to run

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proteomics clinically,
the cost needs to go down.

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There is also, apart from that,
a perceived complexity

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within proteomics, right or wrong.

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And there's also,
a need to better understand the data

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and to get more support in understanding
and trusting the data.

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So those are the three things that, you
know, at least we can talk about today.

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And and

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before I go into our new product,
I just want to mention

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and we haven't really talked about that,
you know, we talked about,

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what has happened within proteomics,

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in the last years.

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But I really want to mention something
that really made a big difference.

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Is of course, the,

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you can be first project,

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where the data has been public
now for over a year.

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And we have so many,

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new publications
coming out from that project already.

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So, and already now it's, you know,
and that is sort of a game changer

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within proteomics
I really just want to highlight that.

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Well, and those publications
are highlighting which diseases folks

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can dig into.

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So just for context, the UK
Biobank Pharma Proteomics project

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a few years ago, 13 pharma partners
agreed to run Olink as the technology

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of choice against almost 60,000 samples
in the UK Biobank.

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Now, there has been

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publications around,

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the 54,000 samples

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that have been part of the flagship paper
that came out in nature

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in October of 2023,
and then there have been 200 publications

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that I know about over 200,
but that's been cited.

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That flagship
paper has been cited over 300 times.

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And so that certainly builds,

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more of a comfort
with the actual data themselves.

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It doesn't allay people's fear.

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And I'll say geneticists fear,
but just because I talked to a geneticist

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around what we call pre
analytical variation, and

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I think you allude to this
in your complexity comment.

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Jenny, you mentioned right or wrong,

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they're perceived as complex
and I certainly think that's true.

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From the genetics point of view.

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And I know
Sarantis has a history in this as well.

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Actually that was also my question, I'm
guessing that the daily life

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is not only happiness
in product management.

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You have a lot of challenges
to go through.

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And you mentioned the cost.

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You mentioned,
the time that you spent on developing.

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But any other challenge,
especially from the technical variation,

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that you may be facing?

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That'll be great to hear.

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Yeah.

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So I think that that is really one
important aspect,

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especially as a supplier,
to really make sure that we can guide,

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our customers in understanding their data.

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So maybe we can go through that
a little bit because.

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So when I talk about trusting the data,

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that's

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very critical but very often overlooked.

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And proteins are different from genes.

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They are a little bit more sensitive.

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You have to really take care
when you do the sample collection

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and how you handle the samples,

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and really ensure that, you know, you have
you can assess the data quality

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at each stage to build that confidence.

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So one of the things that we're doing
is to develop tools to help our customers,

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help our researchers understand
if they have pre analytical variation

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and then guide them through, what
that could mean.

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Are they going to discard the data.

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Can they use them anyway.

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So it's sort of a like an understanding,
like an intelligent support

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of understanding your own
samples, your own results.

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And so it's really critical,

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within proteomics

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to really take that into consideration.

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It's a great point.

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And I think at the end, our main goal
is the precision medicine right at the end

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is like, having high quality data
where we can enable precision medicine.

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I'm sure Cindy,
you have a lot to share, about this field

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where you are really looking
closely recently on that even more,

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you know, and happy
to hear your thoughts about

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how do you see this precision medicine
being enabled by proteomics?

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And where do you see
these going, proteomics in this respect.

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Yeah, absolutely.

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So I think we're better
characterizing disease risk

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in individuals because we're capturing
real time information.

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And so the comparisons of polygenic
risk score to protein risk scores

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have been really helpful in that regard.

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There's some papers out of Claudia
Langenberg's lab, as well as

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Ben Sun, who's one of the one of the joint
steering

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committee members in the UK
Biobank Pharma Proteomics project.

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He's published with the team

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at BioXcellerate and Optima Partners
around protein risk scores.

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I think that's going to help us
in understanding

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how to better recruit for clinical trials
so that we can have clinical trials

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that are smaller
but powerful sufficiently powerful

242
00:13:35,458 --> 00:13:38,458
to see success in candidates.

243
00:13:38,583 --> 00:13:42,625
And of course the ability
to have more successful clinical trials.

244
00:13:42,625 --> 00:13:43,958
What do we say the candidates,

245
00:13:43,958 --> 00:13:47,291
you know, 90% of candidates fail
when they hit clinical trials.

246
00:13:47,875 --> 00:13:50,708
If we can improve clinical trials
by just 10%, we'll be the best

247
00:13:50,708 --> 00:13:51,833
drug makers in history.

248
00:13:51,833 --> 00:13:55,125
And then I would say that
changes everything downstream,

249
00:13:55,125 --> 00:13:58,125
because now we're really dialing
in the right treatment for the right

250
00:13:58,125 --> 00:14:01,125
patient at the right time, which Chris
Whelan talks quite a bit about.

251
00:14:01,333 --> 00:14:06,833
And he's in our just recent episode
of the podcast talking about just that

252
00:14:06,833 --> 00:14:12,500
and how proteomics is enabling this
and so ultimately those are the pillars

253
00:14:12,500 --> 00:14:16,250
I see being moved, the pillars
of ultimately precision medicine,

254
00:14:16,250 --> 00:14:19,750
which interact with clinical trials
and risk stratification.

255
00:14:20,041 --> 00:14:22,166
And then each of those interact
with each other.

256
00:14:22,166 --> 00:14:25,166
And I see all of those moving

257
00:14:25,375 --> 00:14:28,083
upon the foundation

258
00:14:28,083 --> 00:14:31,375
of an understanding of how
genetics, proteomics and outcome data

259
00:14:32,666 --> 00:14:36,041
are associated and linked.

260
00:14:36,916 --> 00:14:37,375
Thanks.

261
00:14:37,375 --> 00:14:39,083
Yeah, thanks for asking that.

262
00:14:39,083 --> 00:14:41,541
And, you know,

263
00:14:41,541 --> 00:14:44,541
there was there's been a lot of discussion

264
00:14:45,500 --> 00:14:49,041
from our customers
and the research community regarding

265
00:14:49,250 --> 00:14:53,250
how can we, understand
different technologies in this area.

266
00:14:53,500 --> 00:14:56,625
How can we understand
how they complement each other

267
00:14:58,458 --> 00:15:01,458
and can you help us?

268
00:15:01,666 --> 00:15:03,000
Sort of guide us

269
00:15:03,000 --> 00:15:06,416
how we trust the data
or how we analyze the data.

270
00:15:06,583 --> 00:15:09,791
So I think that's going to be
and that's normal

271
00:15:09,791 --> 00:15:12,791
because proteomics is maturing in itself.

272
00:15:12,791 --> 00:15:15,666
So I think

273
00:15:15,666 --> 00:15:18,041
that would lead us back
to a little bit on the mass

274
00:15:18,041 --> 00:15:22,833
spec side where the complementarity
between, for example, our technology

275
00:15:22,833 --> 00:15:25,833
in combination with mass spectrometry,

276
00:15:25,875 --> 00:15:29,250
could help us to better proteome coverage.

277
00:15:29,458 --> 00:15:32,583
It could help us assess platforms

278
00:15:32,583 --> 00:15:36,500
through mass spec, while we would maybe
take more of the plasma side.

279
00:15:36,500 --> 00:15:40,250
So I think those kind of things,
and then again,

280
00:15:40,583 --> 00:15:44,166
as suppliers and enablers
to the research community here,

281
00:15:44,416 --> 00:15:47,416
I think we have a role to play

282
00:15:47,666 --> 00:15:50,666
to make sure that we really showcase that

283
00:15:51,208 --> 00:15:56,000
these are, you know, what we show you,
what you see with our technology is

284
00:15:56,375 --> 00:16:00,500
you can trust that and you can
and we will also guide you

285
00:16:00,833 --> 00:16:03,041
in terms of understanding
how that performs

286
00:16:03,041 --> 00:16:06,791
versus other technologies
and how they complement each other.

287
00:16:06,791 --> 00:16:09,833
And I think that's going to be something
that, as we are maturing,

288
00:16:11,166 --> 00:16:11,541
we're going

289
00:16:11,541 --> 00:16:14,541
to see more
of and that's going to be a lot of them.

290
00:16:14,541 --> 00:16:17,750
And well, investments in digital tools.

291
00:16:18,125 --> 00:16:22,291
For that integration and for, for AI,
machine learning, we hear a lot.

292
00:16:22,291 --> 00:16:22,875
Mike.

293
00:16:22,875 --> 00:16:25,500
And also, I wanted to ask you Cindy,
for sure you have the overall

294
00:16:25,500 --> 00:16:29,541
this trend of suddenly
a lot of people, due to the fact

295
00:16:29,541 --> 00:16:31,583
that we have a lot of technologies
and other technologies.

296
00:16:31,583 --> 00:16:33,916
Now we're talking about precision
medicine, right?

297
00:16:33,916 --> 00:16:36,375
And there are a lot of events
happening around this, especially in ways

298
00:16:36,375 --> 00:16:38,416
that they didn't used to have before.

299
00:16:38,416 --> 00:16:41,541
What is your feeling
and what is your feedback on that?

300
00:16:41,541 --> 00:16:42,833
Because you are more in the field and,

301
00:16:42,833 --> 00:16:44,458
you know, in discussion
with a lot of people.

302
00:16:44,458 --> 00:16:46,291
How do you see this moving forward?

303
00:16:46,291 --> 00:16:50,000
So I think these two topics
are very intimately linked.

304
00:16:50,000 --> 00:16:53,750
You know, your reference
to our activities in the field

305
00:16:53,750 --> 00:16:56,000
and working with customers
and showing up at conferences

306
00:16:56,000 --> 00:17:01,375
and our messaging Sarantis, and Jenny,
your comments about having

307
00:17:01,375 --> 00:17:04,375
a responsibility in funneling data

308
00:17:04,500 --> 00:17:08,041
that are as accurate as possible into,

309
00:17:08,458 --> 00:17:11,708
the algorithms for machine
learning and artificial intelligence

310
00:17:11,708 --> 00:17:15,458
that will change our understanding
of these large data sets, right?

311
00:17:15,750 --> 00:17:17,333
We're not data rich.

312
00:17:17,333 --> 00:17:21,083
Certainly not as data rich is, say,
the self-driving car industry,

313
00:17:21,958 --> 00:17:24,125
as we hope to be in the future.

314
00:17:24,125 --> 00:17:25,375
But we're getting there.

315
00:17:25,375 --> 00:17:29,250
And as we get there,
we have this responsibility to only put

316
00:17:29,250 --> 00:17:36,083
the most specific, well-characterized data
into, those algorithms.

317
00:17:36,083 --> 00:17:38,708
And I think that's
where we on the side of caution.

318
00:17:38,708 --> 00:17:42,375
I think that's why we have ostensibly
fewer proteins in our assay,

319
00:17:42,375 --> 00:17:46,916
because we're very careful about
getting those assays into our, products.

320
00:17:46,916 --> 00:17:50,291
And I think in many ways,
that's your team, maybe not your team

321
00:17:50,541 --> 00:17:54,208
before you joined, but you certainly have
supported and resonated for that.

322
00:17:54,208 --> 00:17:56,708
And I think customers appreciate that.

323
00:17:56,708 --> 00:17:59,333
And just knowing that
if we're detecting something, especially

324
00:17:59,333 --> 00:18:02,333
if it's a intracellular protein
or a membrane bound protein,

325
00:18:02,500 --> 00:18:05,791
if we're detecting it in plasma,
where it shouldn't be,

326
00:18:06,291 --> 00:18:09,416
that has the potential
to be an enormous opportunity

327
00:18:09,416 --> 00:18:12,875
for discovery, by customers
that are seeing it there.

328
00:18:12,875 --> 00:18:16,541
And so our detection
or our lack of detection

329
00:18:16,708 --> 00:18:19,541
should reflect, I think, true biology.

330
00:18:19,541 --> 00:18:23,000
And I think that's our messaging Sarantis
at meetings.

331
00:18:23,125 --> 00:18:23,583
Yeah.

332
00:18:23,583 --> 00:18:28,458
So JP Morgan, we just had JP Morgan
I think the messaging at JP Morgan

333
00:18:28,458 --> 00:18:33,541
or the take homes that I heard there were,
essentially that these companies

334
00:18:33,541 --> 00:18:36,208
are in many of the pharma
companies are presenting,

335
00:18:36,208 --> 00:18:38,083
they're moving into a growth phase.

336
00:18:38,083 --> 00:18:41,791
I think we've had two years of challenges
and funding and,

337
00:18:42,083 --> 00:18:45,083
and pullback and contraction
and, and caution

338
00:18:45,500 --> 00:18:48,958
and I think there's
this this bullish opportunity with Suisse,

339
00:18:48,958 --> 00:18:51,458
some uncertainty
around the political climate

340
00:18:51,458 --> 00:18:54,291
and the change in leadership
here in the US.

341
00:18:54,291 --> 00:18:56,541
But some optimism.

342
00:18:56,541 --> 00:18:59,541
And it just felt very buoyant there.

343
00:18:59,625 --> 00:19:02,541
And then we have right around the corner
the Precision

344
00:19:02,541 --> 00:19:06,416
Medicine World Conference,
which is founded by Tal Bahar.

345
00:19:06,416 --> 00:19:10,666
And they're really building
on what that momentum,

346
00:19:12,125 --> 00:19:13,041
felt like at

347
00:19:13,041 --> 00:19:18,500
JPMorgan or around opportunity and Vision
in order to take action.

348
00:19:18,500 --> 00:19:21,708
And so to really foster, an environment

349
00:19:21,708 --> 00:19:24,833
of partnership,
in this precision medicine space.

350
00:19:24,833 --> 00:19:27,333
So I think that's, that's very exciting.

351
00:19:27,333 --> 00:19:30,750
And Jenny
will be talking a lot about reveal.

352
00:19:30,875 --> 00:19:34,916
Can you just give us like a high level
overview is where does reveal fit

353
00:19:34,916 --> 00:19:38,750
into our product portfolio
and where can people learn more about it.

354
00:19:39,041 --> 00:19:39,916
Thank you, Cindy.

355
00:19:39,916 --> 00:19:45,208
So again we talked about accessibility
being one of our main goals.

356
00:19:46,083 --> 00:19:49,125
And as part of that, we are adding,

357
00:19:49,375 --> 00:19:52,958
a new product to our, discovery portfolio.

358
00:19:52,958 --> 00:19:55,125
So everything that is,

359
00:19:55,125 --> 00:19:58,125
detected through and sequencing,

360
00:19:58,666 --> 00:20:00,208
and that is Olink Reveal.

361
00:20:00,208 --> 00:20:03,916
Olink
Reveal is the little sister of Explore HT.

362
00:20:04,791 --> 00:20:08,750
It's an inflammation oriented panel,
so curated,

363
00:20:08,750 --> 00:20:12,666
the assays are curated
based on cis-pQTL associations,

364
00:20:14,166 --> 00:20:16,625
with a strong connection to UKB.

365
00:20:16,625 --> 00:20:18,500
A very strong, inflammation focus.

366
00:20:18,500 --> 00:20:21,500
As I said, it's a thousand plex panel.

367
00:20:22,666 --> 00:20:25,666
So it's a good,

368
00:20:27,125 --> 00:20:29,000
very good protein coverage,

369
00:20:29,000 --> 00:20:32,041
of course, less depth than Explore HT.

370
00:20:32,083 --> 00:20:35,791
But, you know, on the, accessibility side,

371
00:20:36,291 --> 00:20:39,000
it's much more, what can I say.

372
00:20:39,000 --> 00:20:41,125
It's more of a mass market product.

373
00:20:41,125 --> 00:20:45,416
And the reason for that being,
so we focused a lot on reducing the cost

374
00:20:45,416 --> 00:20:46,583
per sample.

375
00:20:46,583 --> 00:20:51,291
So the cost is actually less than $100
per sample. Wow.

376
00:20:51,916 --> 00:20:55,375
Which means that it would be much easier
to add this,

377
00:20:55,791 --> 00:20:58,791
for other cohort studies,

378
00:20:58,958 --> 00:21:01,958
where we have less funding, for example,
but still,

379
00:21:02,250 --> 00:21:05,750
and I think, you know,
we should always, aim to add proteomics

380
00:21:05,750 --> 00:21:09,000
as one tool in all the big population
health studies.

381
00:21:09,625 --> 00:21:12,375
So that really enabled that,

382
00:21:12,375 --> 00:21:16,208
but not only cost,
I would also say what is related to cost,

383
00:21:16,208 --> 00:21:21,250
but I will also say something about the,
perceived complexity of proteomics.

384
00:21:22,458 --> 00:21:23,083
So we have

385
00:21:23,083 --> 00:21:26,125
focused a lot with Olink Reveal
to make it super simple.

386
00:21:26,125 --> 00:21:29,208
So you should be able
to just go in the lab

387
00:21:29,541 --> 00:21:33,375
if you have a NGS sequencer
and set it up and run it.

388
00:21:33,375 --> 00:21:35,375
To get results really quickly.

389
00:21:35,375 --> 00:21:40,166
So you can even run it manually
or with a simple automation solution.

390
00:21:40,166 --> 00:21:45,500
So no big investments to start up,
but something that any genomics lab

391
00:21:45,500 --> 00:21:47,000
already has.

392
00:21:47,000 --> 00:21:50,000
So it's an easy, simple,

393
00:21:50,000 --> 00:21:54,541
way of adding proteomics to your project.

394
00:21:54,541 --> 00:21:57,708
Actually, as you say,
the democratizing protein actually

395
00:21:57,708 --> 00:22:01,333
at the end, right, is like a nice example
of how we democratize protein.

396
00:22:01,333 --> 00:22:02,708
So that's great.

397
00:22:02,708 --> 00:22:04,250
Yes it is, it is.

398
00:22:04,250 --> 00:22:05,583
We've been waiting for this.

399
00:22:05,583 --> 00:22:08,000
This is very exciting. Congratulations.

400
00:22:08,000 --> 00:22:13,041
I know it was a long trip for your team,
and a lot of other teams.

401
00:22:13,041 --> 00:22:13,708
Congratulations.

402
00:22:14,916 --> 00:22:17,333
It's a really great tool. Yes.

403
00:22:17,333 --> 00:22:21,416
No, it's been, it's a project
that has been ongoing for quite some time

404
00:22:22,666 --> 00:22:25,166
at R&D and we're super proud of this.

405
00:22:25,166 --> 00:22:30,000
And really required a lot of data
analysis of the data that are out there

406
00:22:30,250 --> 00:22:33,750
that are publicly available
where we're allowed to go in and play with

407
00:22:33,750 --> 00:22:37,250
and see what are the ones that have
the highest disease associations,

408
00:22:37,250 --> 00:22:40,916
what seem most promising
for having future disease associations,

409
00:22:40,916 --> 00:22:44,833
where these cohorts just haven't been able
to afford to get into proteomics.

410
00:22:44,833 --> 00:22:48,083
So I think this will offer
quick publications.

411
00:22:48,083 --> 00:22:52,458
And I think tracking the publications
in review will be an exciting,

412
00:22:53,375 --> 00:22:55,708
time to see labs

413
00:22:55,708 --> 00:22:59,166
doing proteomics
that have never even ventured in and then,

414
00:22:59,166 --> 00:23:02,875
of course, the opportunity to validate
orthogonally with mass spec

415
00:23:03,583 --> 00:23:06,666
I think will be, also amazing.

416
00:23:06,916 --> 00:23:07,875
That's a great point.

417
00:23:09,375 --> 00:23:09,625
And I

418
00:23:09,625 --> 00:23:12,708
think the choice of inflammation
is really crucial

419
00:23:12,708 --> 00:23:16,208
because inflammation, as all of us
know, is connected to our disease almost.

420
00:23:16,625 --> 00:23:20,458
And that offers a possibility
from different types of researchers

421
00:23:20,458 --> 00:23:23,708
for different disease
areas to explore proteomics finally.

422
00:23:24,041 --> 00:23:26,541
That's a great tool. Yeah.

423
00:23:26,541 --> 00:23:28,625
I mean even in Alzheimer's disease, right.

424
00:23:28,625 --> 00:23:32,541
Where there are clearly endotypes
and some of them are associated

425
00:23:32,541 --> 00:23:33,958
with information and some of them are not.

426
00:23:33,958 --> 00:23:34,833
Being able to stratify

427
00:23:34,833 --> 00:23:37,833
those patients in advance
of clinical trials, for example, might be

428
00:23:38,708 --> 00:23:39,625
some application.

429
00:23:39,625 --> 00:23:44,041
I know that several pharma have reported
that, and Chris Whelan talked about this,

430
00:23:44,041 --> 00:23:45,375
but they have been able to do post

431
00:23:45,375 --> 00:23:49,333
clinical trial proteomics on Explore HT,
which does require automation.

432
00:23:49,583 --> 00:23:52,375
So that's 5400 proteins

433
00:23:52,375 --> 00:23:56,250
over 5400 proteins
using a next generation sequencer.

434
00:23:56,500 --> 00:23:59,791
And folks are seeing stratification
of these,

435
00:24:00,000 --> 00:24:03,000
of these disease areas
after the clinical trial.

436
00:24:03,000 --> 00:24:07,208
And they're seeing that these different
endo types of this disease

437
00:24:07,541 --> 00:24:11,416
are, are responding differently,
to the treatment.

438
00:24:11,416 --> 00:24:12,208
And I think that

439
00:24:13,458 --> 00:24:16,250
is laying some amazing groundwork.

440
00:24:16,250 --> 00:24:19,125
I think it will help a lot for

441
00:24:19,125 --> 00:24:20,833
biomarkers. It surrogates biomarkers.

442
00:24:20,833 --> 00:24:24,416
That would be really a great tool
for following protein biomarkers

443
00:24:24,416 --> 00:24:25,583
really closely.

444
00:24:25,583 --> 00:24:27,416
And I have a question
actually for both of you.

445
00:24:27,416 --> 00:24:30,041
I think we’re discussing about now.

446
00:24:30,041 --> 00:24:32,000
We discuss about tools that we’re
developing now

447
00:24:32,000 --> 00:24:34,541
with a perspective in the future
But how do you see the future?

448
00:24:34,541 --> 00:24:35,958
What do you see the challenges

449
00:24:35,958 --> 00:24:39,083
and the wins we may have
from the proteomics lab in the future?

450
00:24:40,083 --> 00:24:42,666
I think Jenny first.

451
00:24:42,666 --> 00:24:44,541
Know, yeah.
This would be a great wrap up question.

452
00:24:44,541 --> 00:24:45,791
I love this this is wonderful.

453
00:24:45,791 --> 00:24:46,166
Yeah.

454
00:24:46,166 --> 00:24:49,708
No, I, I would say I mean for the future,
I think it's going to be

455
00:24:50,291 --> 00:24:53,250
or it would have to be, a much more focus

456
00:24:54,625 --> 00:24:57,083
on combining different data sets.

457
00:24:57,083 --> 00:24:58,250
So again, coming back

458
00:24:58,250 --> 00:25:02,125
to what we talked about with,
the focus on machine learning and so on.

459
00:25:02,125 --> 00:25:05,125
So I think we we're going
to see much more,

460
00:25:05,625 --> 00:25:11,000
support to combine proteomics,
genomics, transcriptomics data

461
00:25:11,000 --> 00:25:15,500
with, disease genotyping, for example,
we're going to see much more regarding,

462
00:25:16,333 --> 00:25:19,750
predictive power, on proteomics.

463
00:25:19,750 --> 00:25:23,000
And obviously, how is that translated

464
00:25:23,000 --> 00:25:26,000
into, clinical proteomics.

465
00:25:26,291 --> 00:25:29,291
So it's going to be
I think we're going to see,

466
00:25:29,541 --> 00:25:33,583
you know, also just on the first UKB
study, we've already seen that happening.

467
00:25:34,500 --> 00:25:36,750
That you're identifying these

468
00:25:36,750 --> 00:25:40,541
really nice protein signatures
with a very strong

469
00:25:40,625 --> 00:25:43,791
predictive power early on to say,
you know,

470
00:25:43,916 --> 00:25:46,916
if this patient will actually

471
00:25:47,291 --> 00:25:50,416
get a certain disease
several years in advance.

472
00:25:51,208 --> 00:25:55,041
So I think it's going to be like,
you know, from this discovery

473
00:25:55,916 --> 00:26:00,416
to this more, clinical applications
that's going to happen quickly now.

474
00:26:01,208 --> 00:26:03,750
We already had talked about
how much has happened in a few years time.

475
00:26:03,750 --> 00:26:04,000
Right.

476
00:26:04,000 --> 00:26:07,291
So and just looking forward
and then in five years,

477
00:26:08,166 --> 00:26:11,000
I can't even imagine,
you know, what we're going to do.

478
00:26:12,375 --> 00:26:15,500
but I think it's going to go like, you
know, it's going to be more multiomics.

479
00:26:15,500 --> 00:26:19,583
It's going to be much more support
for clinical proteomics.

480
00:26:20,000 --> 00:26:23,541
And of course, we as a supplier,
have a responsibility

481
00:26:24,666 --> 00:26:26,875
to help that to happen.

482
00:26:26,875 --> 00:26:28,125
Great, great.

483
00:26:28,125 --> 00:26:29,875
Cindy, what do you think?

484
00:26:29,875 --> 00:26:31,375
What is the future?

485
00:26:31,375 --> 00:26:33,416
Well, I'll piggyback on something
Jenny said.

486
00:26:33,416 --> 00:26:37,000
So the
the idea of being able to predict disease

487
00:26:37,250 --> 00:26:39,000
many years in advance of getting disease.

488
00:26:39,000 --> 00:26:43,416
I mean, that was really hot news
when Keren Papier

489
00:26:43,416 --> 00:26:47,250
and Ruth Travis and Karl Smith-Byrne
and Josh Atkins, their paper came out.

490
00:26:47,625 --> 00:26:51,666
There's, you know, seven plus years,
it was a median of 12 years, 12 years

491
00:26:51,916 --> 00:26:54,708
in many cancers
of being able to predict disease.

492
00:26:54,708 --> 00:26:59,166
And of course, those are those are many of
those predictive of genetic,

493
00:27:00,125 --> 00:27:03,125
dispositions of folks that

494
00:27:03,416 --> 00:27:07,041
are more likely to get disease,
not necessarily that they actually have

495
00:27:07,041 --> 00:27:10,666
the disease on board,
although they also time

496
00:27:10,666 --> 00:27:13,958
stratified to be able to get
at a little bit of the detail there.

497
00:27:13,958 --> 00:27:15,916
And I just saw
they had a preprint on prostate

498
00:27:15,916 --> 00:27:19,833
cancer that characterize
some of the pathways in the immune system

499
00:27:19,833 --> 00:27:23,666
that are predictive of a likelihood
of getting prostate cancer.

500
00:27:23,666 --> 00:27:26,666
So I can't wait for that
to come out in publication.

501
00:27:26,791 --> 00:27:29,708
So that brings up, the recent

502
00:27:29,708 --> 00:27:32,833
announcement
around us running the entire UK Biobank.

503
00:27:32,833 --> 00:27:34,500
That's 600,000 samples.

504
00:27:34,500 --> 00:27:40,375
That's 500,000 individual with 100,000
repeat samples at a 15 year mark.

505
00:27:40,375 --> 00:27:42,000
Is my understanding,

506
00:27:42,000 --> 00:27:43,333
being able to see that across

507
00:27:43,333 --> 00:27:46,416
all of the diseases that are represented
within the UK Biobank.

508
00:27:46,458 --> 00:27:50,333
And some of them longitudinal also, Cindy,
some of them

509
00:27:50,333 --> 00:27:51,833
also longitudinal also followed, right?
If I’m not mistaken.

510
00:27:51,833 --> 00:27:53,250
That's the longitudinal component,

511
00:27:53,250 --> 00:27:56,791
is the one that over the 100,000
that are followed up at 15 years.

512
00:27:56,791 --> 00:27:59,458
Yeah.
So and many of those have imaging data.

513
00:27:59,458 --> 00:28:00,791
Right. That's great.

514
00:28:00,791 --> 00:28:02,833
And outcome data. Right.

515
00:28:02,833 --> 00:28:05,916
So being able to characterize that,

516
00:28:07,250 --> 00:28:10,583
that set of samples which have around
8,000

517
00:28:10,583 --> 00:28:15,541
African diaspora samples, have around
8,000 South Asian samples.

518
00:28:15,541 --> 00:28:19,041
These are diverse, sets of samples
that, as Rory Collins

519
00:28:19,041 --> 00:28:22,166
says, aren't enough diversity
for us to really characterize everything.

520
00:28:22,166 --> 00:28:26,250
But across the entire UK
Biobank, we do, you know, effectively

521
00:28:26,250 --> 00:28:30,000
have longitudinal representation,
because if you get enough samples, you get

522
00:28:30,250 --> 00:28:32,416
folks
that are in different stages of disease.

523
00:28:32,416 --> 00:28:36,208
So though it isn't longitudinal
in an individual, it can be, you know,

524
00:28:36,208 --> 00:28:39,666
by being cross-sectional
and large enough in size can represent

525
00:28:39,666 --> 00:28:42,666
some of the longitudinal aspects of

526
00:28:42,750 --> 00:28:44,750
disease progression.

527
00:28:44,750 --> 00:28:47,166
So that's
what I'm really looking forward to.

528
00:28:47,166 --> 00:28:50,875
And I expect those data
to be published around 2027.

529
00:28:51,583 --> 00:28:54,833
Which means that that the world
will have access to

530
00:28:55,333 --> 00:28:58,333
that international resource,

531
00:28:58,375 --> 00:29:01,125
which is some of the
and we talk about it as the UK Biobank,

532
00:29:01,125 --> 00:29:04,750
but it is the internationally access to UK
Biobank.

533
00:29:04,750 --> 00:29:07,375
So it's an exciting time.

534
00:29:07,375 --> 00:29:12,500
And just to add to that
the statistical power of 600,000 samples,

535
00:29:13,125 --> 00:29:16,625
would, you know, imagine
what that would mean for understanding

536
00:29:16,625 --> 00:29:20,625
rare diseases, for example,
which hasn't been possible, really.

537
00:29:20,625 --> 00:29:25,166
I mean, I know, we had that you could see
that also in the, in the sort of smaller

538
00:29:25,166 --> 00:29:29,541
UKB, set from before,
but with very few samples.

539
00:29:29,541 --> 00:29:33,541
So I think that is also something
that is extremely important.

540
00:29:33,750 --> 00:29:38,250
Already with these, the first papers
that we have or the small sample size

541
00:29:38,250 --> 00:29:41,958
that you mentioned, Jenny.

542
00:29:42,000 --> 00:29:43,583
Yeah, it's a small one.

543
00:29:43,583 --> 00:29:47,916
I mean it we were able to see
there are great papers

544
00:29:47,916 --> 00:29:52,750
from Claudia Langenberg, that we had
improvement on diagnosis of disease.

545
00:29:53,458 --> 00:29:56,416
Even better than clinical outcomes
sometimes.

546
00:29:56,416 --> 00:29:57,750
And that was really impressive.

547
00:29:57,750 --> 00:30:00,500
Well, it was really amazing
for the first time to see such a thing.

548
00:30:00,500 --> 00:30:00,958
Right.

549
00:30:00,958 --> 00:30:04,375
Imagine that was tenfold more sample size.

550
00:30:04,375 --> 00:30:06,583
What's going to happen.
That more diseases, right.

551
00:30:06,583 --> 00:30:09,333
More representation
of those diseases. Exactly.

552
00:30:09,333 --> 00:30:10,500
And ability to

553
00:30:11,916 --> 00:30:14,333
you know, propose these protein scores
that that

554
00:30:14,333 --> 00:30:19,375
that will improve any over anything
a doctor has available to them today.

555
00:30:19,500 --> 00:30:22,250
Yeah. This is yeah it's a beautiful time.

556
00:30:22,250 --> 00:30:25,250
And of course we are biased by our protein

557
00:30:26,250 --> 00:30:27,000
excitement.

558
00:30:27,000 --> 00:30:29,625
But yeah, we're happy to be a part of it.

559
00:30:29,625 --> 00:30:34,458
So with that, I will wrap up this episode
of, Proteomics in Proximity.

560
00:30:34,666 --> 00:30:38,250
We will, as we all mentioned,
we will be at Precision

561
00:30:38,250 --> 00:30:39,958
Medicine World Conference in Santa Clara.

562
00:30:39,958 --> 00:30:42,416
That's February 5th through the 7th.

563
00:30:42,416 --> 00:30:43,250
We will have a booth.

564
00:30:43,250 --> 00:30:45,500
Our booth

565
00:30:45,500 --> 00:30:48,500
will be near the stage for track three.

566
00:30:48,500 --> 00:30:51,625
It'll be between track
three and track four in Hall C,

567
00:30:51,916 --> 00:30:55,291
and very close
to a little networking station.

568
00:30:55,291 --> 00:30:57,750
So reach out on LinkedIn, reach out to me.

569
00:30:57,750 --> 00:30:59,625
Reach out to Sarantis

570
00:30:59,625 --> 00:31:02,291
If you want to set up meetings

571
00:31:02,291 --> 00:31:05,291
with our team, I will be there in person

572
00:31:05,333 --> 00:31:08,458
and would be excited to talk to folks
that are there.

573
00:31:08,666 --> 00:31:10,708
Would be great.
That would be a great event.

574
00:31:10,708 --> 00:31:12,625
Really? Yeah. I wish you were here.

575
00:31:12,625 --> 00:31:15,583
I wish I was there, but I’m
looking forward to hear your feedbacks.

576
00:31:15,583 --> 00:31:16,583
I’m sure you have great feedback
from that.

577
00:31:16,583 --> 00:31:18,833
Yeah, we could do an episode live.

578
00:31:18,833 --> 00:31:21,000
Yeah,
that would be great idea. Great idea.

579
00:31:22,958 --> 00:31:23,916
All right.

580
00:31:23,916 --> 00:31:26,250
Thank
you Jenny. Thank you for tuning in. Yes.

581
00:31:26,250 --> 00:31:27,250
Thank you Jenny, thank you.

582
00:31:27,250 --> 00:31:30,250
Thank you so much. Great to have you.

583
00:31:30,833 --> 00:31:32,041
Well, that wraps

584
00:31:32,041 --> 00:31:35,041
up this episode of Proteomics
in Proximity.

585
00:31:35,250 --> 00:31:39,458
Huge thanks to our guests and authors
of such impactful publications.

586
00:31:39,791 --> 00:31:42,333
I also want to thank you for tuning in.

587
00:31:42,333 --> 00:31:44,666
Really appreciate you being here.

588
00:31:44,666 --> 00:31:46,416
If you enjoyed the content of this

589
00:31:46,416 --> 00:31:50,291
episode, please think about sharing it
with friends or colleagues

590
00:31:50,291 --> 00:31:52,541
you think might be interested
in the content.

591
00:31:52,541 --> 00:31:56,583
In addition, if you'd be willing
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592
00:31:56,583 --> 00:32:00,083
or wherever you digest your podcasts
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593
00:32:00,083 --> 00:32:02,125
this will help others find the podcast

594
00:32:02,125 --> 00:32:05,500
when they're searching for proteomics
or precision medicine podcasts.

595
00:32:05,666 --> 00:32:09,333
And mostly I want to say
we would love to hear from you.

596
00:32:09,416 --> 00:32:11,333
So we have a dedicated email address.

597
00:32:11,333 --> 00:32:14,333
pip@olink.com please reach out.

598
00:32:14,416 --> 00:32:18,375
Let us know what you're interested
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599
00:32:18,375 --> 00:32:22,458
and any feedback on the episodes
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600
00:32:22,708 --> 00:32:26,125
This is all about you,
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601
00:32:26,125 --> 00:32:29,125
to make sure that we're meeting
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602
00:32:29,333 --> 00:32:31,458
Thank you so much, and we'll see you soon.