Welcome to the

proteomics in proximity podcast, where

your co hosts, Dale Yuzuki,

Cindy Lawley, and Sarantis Chlamydis from

Olink proteomics talk about

the intersection of proteomics with

genomics for drug target discovery,

the application of proteomics to reveal

disease biomarkers, and current

trends in using proteomics to UN,

lock biological

mechanisms. Here we have your hosts,

Dale, Cindy, and

Cerantis.    Here we are with another

episode of Proteomics in

Proximity. I'm your host, Dale Yuzuki, with

my co-host Cindy

Lawley. And Sarantis, tell me this

and Sarantis. And we are here

today to talk about cancer,

and specifically cancer

immunotherapy, and looking at new

biomarkers for

predicting immunotherapy response.

We are looking at an article, uh, published

in 2021,  one called

"plasma proteomics identifies

leukemia leukemia inhibiting

factor LIF as a

novel predictive biomarker of

immune checkpoint blockade

resistance." Now, this particular

topic that is a, uh, mouthful. It

is a mouthful. Okay. The

first law author is Loriot. Like I

mentioned, it was in, The Annals

of Oncology, and it was published in


interesting, right, is we're talking

about immune checkpoint blockade

resistance. So these are PD-1

and PD-L1, 

uh, therapies that have really

revolutionized   cancer

treatment. Specifically, I mean,

backing up a bit immune checkpoint,

uh, blockade, uh,

   inhibitors, right?

These particular

ICB's,     

so-called, is credited with saving the

life of Jimmy Carter, right? He

came down with very serious

melanoma. He's still

alive. I think he's, what,


there, but   his life has been

saved. And this particular paper

is explaining new

predictors of ICB

resistance because it's not

equally effective in other types of cancers.

   Go ahead. Uh,

sorry, I just wanted to add a little bit in

there. So just stepping back. One more step

back is tumors tend to

evade the immune system, right?

So they tend to grow,  

ahh, often unchecked.

 And, uh, one of the

mechanisms for that hiding or

that evasion of the immune system

is this PD-1,

and it's ligand,

PD-L1, uh, that

connection. And so these

checkpoint inhibitors cut that

connection and allow

them the tumor in a

subset of patients, not majority of patients

that are treated. It's actually

only, what is it? 35%

effective. Something around there 

 in a subset of patients, jimmy Carter

included. Uh, this was actually

an effective way to open

up the immune system, to see the

tumor and then infiltrate the

tumor. And so some of the ways of

evaluating how these ICB therapies are

working is by looking at infiltration of

the immune system into a tumor.

Thank you. Yeah. And for

melanoma, it's extremely

effective. It's for the other

cancers where it's not very effective.

Solid tumor, solid tumors.

Very good.   Sarantis, you

were just going to ask. No,  

  just to mention that

it's a therapy based on antibodies, right,

that targets, uh, either PD-1,

PD-1 in conjunction

with other chemotherapies, where, um, the

success rate is getting increased. But

still, as you mentioned Cindy, 35% of the

patient respond to the treatment.

And, uh, I think it's really important

that new biomarkers or, uh,

new, uh, molecules that are

druggable and they can help increase, uh,

the rate and the outcome of

the eventual outcome of the patient 

     goes to this

paper as well. Right. And as far as

predicting,   uh,

ICB,  uh, responsiveness, uh, 

      I

was involved in ctDNA analysis

for a couple of years, right, from 2015 to


all the rage was looking at tumor mutational

burden. That is looking at right.

TMB, TMB, yeah, TMB, TMB

TMB. Looking at whole exomes,

  looking at tissue

biopsies, looking at

circulating tumor DNA is what ctDNA

is and trying to get this

idea of, right, okay, if

there's a high mutational load of the

particular cancer, this person is

more likely to respond to

immune checkpoint inhibitor treatment. 

And they tried to normalize

different enrichment methods, they tried to

get all this work around

it, and it really

wasn't all that predictive. I mean, the R

values were about 0.7.

  And then a, uh,

multiplex immunohistochemistry was all

the rage.  I saw that

wave.   Another what we call

companion diagnostics. So these

are tools that

physicians are trying to use in order

to guide an understanding of

who might be better responsive

to such therapies. Yeah, please. Go ahead,

Dale. And so companies like Akoya

and other spatial right

imaging, I think Millipore also had

one hundred-plex kind of thing.

Anyway, there are all these different

companies doing multiple, like, uh,

particular antibodies and

immunostating. Uh,    that's what

immunohistochemistry is. You're staining

with antibody and detecting it with

fluorescence. And they're trying to get that

and they're able to up the

predictive power just

incrementally. So that is why the

hunt for biomarkers is

still on. And how do I

predict?     

   I'm sorry, another point. Also

here, there's a lot of immunohistochemisry is

based on biopsies, right, and quite

invasive.   And then I think

the most important part here is in this

paper, they see the value of having the

plasma protein that's less invasive and

can, they can support all the findings

of    things, uh, that they can see within

the cell, for example. And the question is,

right, even with the biopsy samples,

even with the sort of invasive methods, it's

still not predictive enough, right.

Where they call it imperfect

predictors. Right. They're still

seeking better

predictors because still, even with those

guided tools,


majority are not. So if we're going to give

the right treatment to the right patient at

the right time, we have to have better tools

for that. And, and can I just say that this

paper is so elegantly

written, I highly recommend it. I just

think these authors, I don't know if you've

seen them speak, both Loriot, as well as,

um, Antoine, uh,

ah, Italiano. Amazing

speakers, both on YouTube, highly recommend.

    Yeah, that's Italiano.

And Loriot they're at the

Gustav Roussey is that in Paris?

or it's multiple instances, it's their

cancer center.    Grand

Paris I think is where the clinical trials

for these cohorts, the validation and

discovery cohorts are based. They're still

recruiting. These are phenomenally

cutting edge.     So looking

at tumor mutational burden, right? From a

genomics perspective, you're looking at IHC

from a tissue histology-based

perspective. There's even effort

around microstaellite instability

to go back to the genomics tool, another

genetic tool, right? Looking at it again,

still not good enough.

And Sarantis, you mentioned, right, the

power of circulating biomarkers,

you might say. Well, haven't been people

been looking at this before? 

 I think. Yes, there

was all the right tools, actually, right? I

mean, I think here is something to say

that's impressive about Olink technology, right?

Because uh, gives this high throughput thing

and before this was Mass Spec, it was a

little bit more difficult.   It's not

so much out throughput. I think

it's more easy nowadays with having this NGS

based technology and things like that,

    right?

And uh, it's a cytokine, right? So cytokines

are these low abundant,  

(absolutely) proteins, right? So very low

abundance and not easily seen with mass spec

without a large amount of sample or

subtraction methods. And so having the Olink

PEA that hooks this proximity

extension assay, that hooks these low

abundant proteins out of solution and

helps us count their abundance,

   uh, is I think,

   just a great use

case. And again, talking

about how you

close the loops, right, and you go a little

bit more to the molecular you have the LIF

protein, but then brings uh,  together

drug targets like STAT3, for

example, right? I mean you open up

completely a pathway of uh,

target proteins that could be druggable

but also could be potential novel

biomarkers. It's not like one protein,

there's a completely (new) pathway. And I

think tools in the future like Olink

Insight that we introduce it will help

a lot of people understand pathways and

how other actually target molecules

right. Can help and enhance drug

development processes. And biomarker

identification and ah, Sarantis,

you mentioned Olink Insight. For those

who haven't heard the podcast with Hilda

Anderson talking about Olink Insight, I

refer to that. I think it's

episode number five.

At any rate,

Sarantis, there's a question that I have,

right, from, again looking

at     soluble

biomarkers in plasma.

Uh, the question I have is,

is this signal sufficient? I mean,

somebody may have advanced tumor

status. I mean this particular paper

used advanced

NSCLC, uh, as

their sort of test

case,  uh, is there enough

signal there? I mean, somebody has a tumor

in a particular area of their body,

but we're looking at the entire sort of

plasma. I mean, the blood volume of

an individual is on the order of what, a

couple of liters, right? Maybe not

true. Yeah, that's true. It's always difficult

to     force

here. They have much samples, right? We try

biopsies much samples we have

plasma samples and they try to see

correlations. And then they did, as Cindy

mentioned, a really elegant way to check

with the assay to see, for example, B

cells or, uh, differentiate

adult T cells, for example. I think

it's, uh, looking in the omic's

perspective gives more power to this

respect.   That'S

my and the imaging as well.

Right?      I think,

Sarantis, you said   the

Insight tool allows

you to see multiple pathways and

interactions among pathways. And we expect

most diseases we've seen are polygenic. And

there's going to be multiple pathways that

are involved in probably a signature of

maybe 20, 20 or so proteins that

we'll need to understand to really

use it for, say, ah, a companion

diagnostic in the future if we move through

clinical utility in this case, It's

rather remarkable that it's one 

cytokine that has that

signal where low versus

high were predictive

of,   um, progression, uh,

free survival. Did I say that right?

PFS, yeah. And overall

survival. And uh, that's kind of

remarkable. But I also want to point out

the tertiary lymphoid structure

correlation. So we've got

this imaging data from these biopsies. As

you say, it's very invasive. Right. And

that you can actually see and

count these lymphoid structures, which are

just sort of localized,  

um, immune

system,   adaptive, uh,  

immune, uh, clusters that

are responding to the tumor.

And that also was a correlate

to that same cytokine, which

I find that to be pretty

remarkable. Absolutely. That's a great

point Cindy. So, Sarantis,

I just want you to take us from the top in

terms of talking about the experimental

design.    Yeah, I

mean, they try a cohort of


patients actually treated with

immune system checkpoint point blockade, uh, anti

PDL-1. And

uh, they use the Olink

Explorer 1536,

like more than 1500

proteins. And uh,

among this, was there like, our,

uh,    cohort, their

discovery cohort, and then there was a

validation cohort with Flamation panel

with more than 290,   uh,

patient 96

proteins, where they see

actually really nice correlation with data

that they can see with the Explore, uh,   

panel. That's also really nice

because our Target and Explore

panel approach, they go hand by

hand, and that's really great and really

nice to see how we reproduce our assays,

even though from high throughput to low

throughput. Right. And that's

really great and nice

finding. And uh,   

uh, one of the first biomarkers that

popping up to their data

set was LIF that, uh,

high levels of LIF in

this plasma was correlated

with the poor clinical outcome of the

patient with the immune blockade.

That was the first and most

remarkable, uh,    

actual data. Yeah, it's a

really good point, Sarantis. Like, they call

it discovery cohort and validation cohort.

But not only is it a discovery

cohort where they discovered the signal and

then validated it, but they also used our

discovery tool. Right. The Explore

at that time, this was one of the first

Explore publications and then they

moved it to a lower plex where they could

get the same markers and of

course, that's going to be a lower cost. So

they were able to do more samples in that

validation cohort, which I like, I hadn't

thought about the fact that it

parallels  the discovery and

validation also parallels the intent,

to some extent of how

groups are using our products. Sometimes

moving to a Target 96, but

sometimes even moving to a

Flex panel or a lower Plex

panel. Um, yeah. Anyway,

I like that characterization that

you made. And if I understand correctly,

the discovery and validation

cohorts from two independent

clinical trials, is that correct?

That's right. Exactly.   

       Both

still recruiting. Right. So still, I would

expect more to. Come, uh, here and different

type of cancer, right? Different type

of solid cancer for

bladder to non-small cell lung

cancer.    They have different

type of prostate, I think. Also they have

different   cancer

types that's like, more

universal. Looked like more universal

biomarkers, actually. Yeah,

I missed that. That's an interesting

aspect, too. Yeah. 

M good, um,  catch.  

      Yeah.

Both out of Gustav Roussey,

as Dale mentioned earlier,   

       this

was the, uh,   MATCH-R

cohort. So I have it written down. I'll

actually read it off, uh, prospective

trial to study the evolution of clonal

architecture of tumors from patients

treated with molecular targeted agents.

So that's the MATCH-R, that was the

discovery cohort where they used the

Explore. And, um, I think you mentioned

Cerantis 90 some odd

 samples, uh,     or

patients in that were used in that.

    Um, and then the

premise, the validation cohort

where they used the Target or qPCR

readout it was the predictive markers of

immune related adverse events

in patients treated with immune

stimulatory drugs. So, again, that's the

PREMISE trial. So yeah, 

   a lot of great stuff coming

out of this group.    So then as far

as, uh, Cindy, you mentioned

this idea of a

tumor lymphoid or

tertiary lymphoid structure,

right? That's right. And

this is then a feature of

good response, is that correct?    

Yeah. Uh,     

I believe the relationship was low

LIF   with tertiary

lymphoid structures.  

Meaning, my understanding,

uh, and Sarantis, please check me on

this. My understanding is when you

have, um, these sort of

ectopic lymphoid organs that

develop in these non lymphoid tissues, that

they're a sign of

immune infiltration from adaptive

immunity. No. Okay.

  So, uh, that's

telling us that there's some element of

the tumor that's visible to the immune

system. And    that's like the

Holy Grail, right? The gold.

Yeah. You can manage to not turn

on autoimmunity in some issue.

Right.   You don't want the immune system to

go haywire and get inflammation out

of control. That's what kills a lot of

COVID patients. Right.   You

want to keep that inflammation in check, but

you want to open up the

tumor to the immune system. So you have

a very targeted kill,

as opposed to sort of traditional

chemotherapy agents that would

take   us to death's door

because it killed, um, non

specifically would kill off all fast

growing cells. Right. Which is why people

lost hair, lost nails. These are fast

growing cells in the body, and you just

carry it. Right. I'm oversimplifying,

obviously. But this is the

promise of the future   of,

uh, cancer treatments. Yes.

And it's like these

tertiary lymphoid structures are

a sort of in vivo

validation. Right. The

marker    

is measuring what we want to

measure, which is this  person

responding or a person not going to respond.

Right.    Yeah,

exactly. Uh, without having to go in and

do a biopsy to see these tertiary

lymphoid structures. Right.   

As you said earlier, Sarantis, if you

can  evaluate it in a blood

draw,    that's

exciting. Yeah. I mean, you think about how

sick these patients are. Right. These were

advanced NSCLCs was a

majority of the samples. I think bladder

cancer was a minority of the

original 95 samples. And I think

there had other tumor types in there

to represent the discovery

phase.   Uh,  Sarantis,

can you mention oh, go ahead, Cindy, go

ahead. Yeah,     you just

reminded me, um, that in the results,

they actually characterized the difference

in progression free survival and overall

survival. And we're talking months. Right.

These are people that are maybe living

two months versus 21

months.   It's a great point.

Ah, and I think even by the time they did

analysis   and of course, you

have to qualify for clinical trials, so you

have to have already had,

um, a treatment fail. Right.

Uh, there's several checkpoints I think they

have through their cancer center that

Antoine talks about. But we're

talking about,

um, people that are very

sick and at very high

risk of

dying.     Uh, and

this gives them can sometimes

maybe, um, give them  

five to ten times more time,

    um, than they would have had

otherwise, and hopefully with a less

invasive, um, therapy.

Although I don't know as much about the side

effects of this. Cindy, you bring

up a good point in that these were

advanced, by advanced, meaning they

were metastatic. Right. They already

had stage four,

or stage three, really high

stage   lung, uh, 

cancer, and had already spread to other

organs. And I think I

was thinking about this this morning, where

at the 50% overall

survival curve, it was maybe

five months. And then,

uh, with the LIF, uh, low,

that's five times, that

over about 20 months.

And so I was asking my boys, taking them to

school, how much is 15 months

worth? Right. Meaning, ah,  

from five month point to 15

months more.   That's

the difference.   And you think when every

month counts, I mean, these people

have right. Advanced,

uh, uh, basically fatal lung

cancer. But with this

treatment, you're given what,

a year, over a year,

how much is a year of your life worth?

Yeah. And it's such a personal

choice, right. Because it also depends

upon what the quality of life

is in that year. Right. Which is

something that's really

yes.   As we get older, we

think more and more about these things.

Right. I think it's a nice way to monitor

right. In the way that you can see the

response and you can have something

to monitor. And I think that's

a useful tool if you would like to combine

other therapies in the future. Right.

Because, you know, when you monitor how

people, they respond, having something

that is less invasive, more easy to

handle, in a way more easy to understand,

and, uh, I think it's a great

tool, and it's a great finding. It opens

new ways of seeing science in the future.

Right. Yeah. Interesting. Good point. Yeah.

Because if it's not working, you get off it.

Right. And you just get that quality

of life back. Perhaps. But

yeah, it just depends upon what the side

effects of any given therapy are. And

certainly the ones today are much

better,      uh, overall

than the ones 20 years ago.   

   Now, since a high

level of this LIF, (leukemia)

inhibitory factor right.

 Leads, uh,  to poor outcome 

  did I read this correctly?

It's actually now considered a drug

target?     

 Yeah. Go ahead, Cindy. Please. Go

ahead. Yeah, I actually don't know.

Please, Sarantis. I think they

have some thinking of I don't know

if it's, um, somewhere  in the discussion the

measure that they're thinking, like, for

antibody against exactly.

  Against monoclonal antibodies

against Liver. It would be a target, I'm

guessing. Yes. Because I think it's like,

uh, cytokine that is like, uh,   

 uh, differentiation regulates

differentiation.       

     It depends on if

it's causal or if it's a

thing,     if it's the

results of this, right. So if

it's a useful thing to monitor to

know    how

someone's responding, then we wouldn't want

to drug it. But I think that's interesting.

Right? Yeah. Uh, you would imagine the

knockout mice are in,

right? Yeah,

that's right. They're in generation II.

They're the F-1. Got the F-1 cross going

on right now.    

Jackson Labs is probably yeah, there you

go. Over this.   Well, there

you go.    

Uh    uh, did they

give also any clue in terms of what their

next steps would be? I mean, since

this is a single marker right.   

Pretty straightforward to implement,

      I

think. Looks like, since the

numbers they're still small, I think.

But Cindy, correct me if I'm wrong there. I

think it needs more bigger numbers

right. And bigger cohorts and different

clinical trials. I don't know. Cindy,

correct me if wrong. You are you are the

expert of the of the big samples and

big cohorts. Yeah. I mean, what was it,


And 292 in

the sort of validation, 

    uh, in cancer,

naturally, the numbers tend to be

relatively small.

Right. In terms of they're not in the

thousands. They're in the hundreds. Cindy,

do you think that     

 these numbers need to be

larger? Oh, for sure.

Absolutely.   Yeah. No doubt.

No doubt at all.   

     Uh,

but I think

the burden of

this and this is a question the

burden of use of this

as a companion diagnostic, which

I think is the immediate

proposal, I think further down the

line, establishing causality rather than

consequential that it's

consequential to the state

that, um,        there

must be a lower burden for

establishing a biomarker as opposed to

establishing a monoclonal antibody

therapy.    And

I don't have that  

set out in data, but I think that's

the media or the near term,

 uh, exciting promise

here     is that it could

be used as a companion diagnostic. And they

actually broke down the use of LIF

high versus low. And

the TPS scores right. Which were the ones

that Dale mentioned. So,

um, the tumor mutation

burden, along with basically trying to

characterize this

ligand expression,  uh,

the tumor mutation burden, the

microsatellite instability, and the

immunohisto-chemistry, using those

which are established scores in

conjunction with LIF,   showed,

uh,   utility. Right. So I thought that

was a logical  progression in

their analysis that I thought was

interesting, but also requires larger

sample sizes, because now you're breaking

your numbers into quadrants, and you need

sufficient sample numbers in each of those

quadrants. Right.    

Yeah. Interesting.   

    Well, any other

final comments before we wrap this one up?

      Cindy, go

ahead. I'm so sorry. I just am so

excited about this paper. Sarantis, I think

I've stepped on you three times. 

  Um, 

 I just highly

recommend that people keep an eye on these

authors, because, like I said, they are

elegant writers.

They speak with

such clarity, and

they have been driving the use of

genomic tools in

demonstrating, um, utility and

cancer for years. Right. Cutting

edge.    Right on that

cusp. So sorry. Sarantis, your

turn.    Oh, I

think the method, for me, the most important

thing go from invasive to non invasive

methods. Uh,   

that's the clever thing on this story. And,

of course, I mean, you can back it up with

multiomics like imaging, get an

assay, but, uh, I think the method is

brilliant. That's pretty much how I

see it.   All right, well,

thank you for joining us today.

Till next time. Take care.

Thank you very much. Thank you.

Bye bye. Bye bye. 

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