Dissecting and Overcoming Different Shades of Cancer Immune Evasion

May 20, 2024

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00:00Represent some of the work that my lab
00:02has been up to in the last few years.
00:05And as you will see it's it's quite
00:08different and perhaps different from
00:10what you expect from our prior work.
00:12But it's sort of the next wave that I,
00:14I, I sort of view important in
00:16building on what we have done before.
00:18So today I will talk about really
00:21different shades of cancer immunivation
00:22as you'll see and some strategies that
00:25we're developing to overcome these
00:27from multiple at different angles.
00:30So just very briefly,
00:31you know if I had to summarize sort of
00:33what our lab is doing at this point,
00:35it's really you know doing large
00:37scale genomics to understand
00:39cancer genetics using single cell
00:40genomics and things like that.
00:42But that's really the beginning
00:44really want to use this information
00:45to inform you know mechanisms
00:47that underlie two major areas.
00:49One is immunobiology and the other one
00:52is metastatic organotropism which is
00:54really critical to consider when we use
00:57an immune base or or even other therapies.
01:00The approach,
01:00as you will see in a minute also is
01:03typically so that our questions are
01:05really inspired by clinical problems
01:07and then broken down into models.
01:10Frequently you have to develop
01:11those models or methods to study
01:13these things systematically.
01:14And ultimately,
01:15of course,
01:16the goal is to bring this back to patients.
01:19So I divided the talk into three chapters
01:23that are loosely linked to each other,
01:25as you'll see in a moment.
01:27And I want to start with the first
01:29one because I think it really
01:32exemplifies how we can use information
01:34from large genomic data to inform
01:37precisely what mechanisms and sort
01:39of things we should study in the lab.
01:43So a few years ago we were really
01:45interested in understanding
01:46mechanisms of resistance to immune
01:49checkpoint inhibitors and you know
01:51the details don't matter so much,
01:53but the approach was that we use single
01:55cell RNA sequencing in these patients.
01:58We looked at the cancer cells specifically
02:00and we came up with this you know
02:02signature which we called ICR signature,
02:04immune checkpoint resistant signature
02:05and again the the genes don't matter
02:08so much but you know and and I think
02:10we learned a lot in this study but
02:12we're what we were left with is a
02:13hypothesis that came out of the data
02:15that was that this cancer cell intrinsic
02:18program was somehow conferring T cell
02:21exclusion or poor T cell infiltration
02:24and impaired T cell activity.
02:26But when you have these gene lists
02:28which I know all of you have been at,
02:30at one point in your project,
02:32the question is how do you actually
02:34prioritize what you're going to
02:36look at right and and what not.
02:37And typically what we do and this
02:39is what we had done for this paper,
02:41we focus on something that's plausible
02:43for which there are reagents or other
02:46practical or pragmatic reasons.
02:48But we really wanted to make
02:50this process of how
02:52we validate things a bit more unbiased
02:55and to do that we developed this method
02:58which we dubbed Perturb site seek.
03:00So basically what this method allows
03:02you to do is couple CRISPR CAS 9
03:06perturbations with single cell RNA
03:08and protein profile using the site
03:10seek and a 10X genomics platform.
03:13So what you would get from this method
03:16would be that you could perturb a
03:18gene and then ask well what does it
03:20do to the entire cells transcriptome
03:22and part of the surface proteome.
03:25And the way we use this method to
03:28validate some of those findings that
03:30we had in patients is leveraging
03:32it in in in patient derived models
03:34where we had isolated in addition
03:36to doing the sequencing the cancer
03:38cells from a Melanoma patient and
03:40the tumor infiltrating lymphocytes.
03:42The advantage is that this is a
03:43you know fully autologous system.
03:45It doesn't require any sort of
03:47engineering if you will to do a
03:50Co culture experiments into these
03:52cancer cells we generated.
03:53We generated a library that would
03:55target each of the genes that we had
03:58identified in the signature in patients.
04:00So we could test in one you know pooled
04:03experiment the relevance of any one of
04:06those 248 genes that were in the signature.
04:08And not only that but also we would
04:10get you know the transcriptome
04:11and part of the surface podium.
04:14And the premise here would be that
04:16cells that survive Co culture with
04:19their autologous T cells must harbour a
04:22perturbation that confers that mechanism.
04:24And this is exactly what we did.
04:27And I'm just going to give you this
04:28is of course published at this point.
04:30But what what this would get us is
04:32a sense of the immune fitness and
04:34the phenotype associated with this.
04:36I'm just going to give a snippet of
04:38results because I want to show you how
04:40this helped us inform kind of what we
04:42did next in the last couple of years.
04:44So here we're looking at gene
04:47knockouts on the bottom.
04:48So genes knocked out here associated
04:51with increasing fitness against
04:53autologous T cells or tilts.
04:55And on the Y axis here we
04:57increased immune pressure.
04:58So there's an effector to target ratio of 1
05:01to 1 to the one and four to one and so on.
05:03And as you would expect,
05:05mutations or deletions in all of the
05:06genes that I indicate here with the red,
05:09Red Arrows,
05:10all of those had been known before,
05:12right.
05:13Mutations or deletions in those
05:15genes are strongly associated
05:16with immune evasion and they had
05:18been associated clinically with
05:20resistance to immunotherapy.
05:22So that was really good because
05:23it validated our approach that we
05:25could recover all of those hits.
05:26Basically in one experiment,
05:28we got really interested in another
05:30hit that was less expected.
05:31That was loss of a gene called CD 58.
05:34So I'm going to get,
05:34I'm going to talk about that more.
05:36But on the flip side,
05:37we could also couple how these
05:39perturbations change the phenotype.
05:40And as you can imagine this
05:42is a huge matrix, you know,
05:43perturbation by gene expression by by,
05:45you know, protein profile.
05:46So I'm just going to show you a tiny,
05:48tiny snippet from that which I
05:51want to use to guide you to the
05:53to the experiments that we did.
05:55So the way to read this here is that
05:56on the bottom you have knockouts
05:58of those genes and then on the
06:00Y axis you have a few selected
06:02features that I I want to present.
06:04So for example, if you knockout CD 58,
06:07then there will be no CD 58
06:09protein left in that cell.
06:11Logical.
06:11But what was really interesting was the
06:14observation that cells that lose this gene,
06:17CD 58 had concurrently more
06:21protein by of a gene encoded
06:24by a protein encoded by CD 274,
06:27which of course is PDL 1.
06:28So that seemed like a double whammy.
06:30You lose something good that
06:32confers immune evasion,
06:34but then you also gain something that
06:35is a Co inhibitory ligand of course.
06:38So what is CD58?
06:39It turns out we actually don't
06:41know that much about it in cancer.
06:44What we do know physiologically is
06:46that it is a Co stimulatory protein
06:48that ligates to CD2 on T cells,
06:51and when it does so,
06:52it can become actually the most
06:55potent Co stimulatory protein.
06:57So loss of this gene of protein,
06:59it's plausible that that could
07:01result in immune evasion in
07:02a number of different ways.
07:04So we sought to validate this
07:06and this is work that has since
07:08then been LED and then published
07:10by my first MDPHD student who
07:12just graduated a few months ago.
07:14So here basically what she did is
07:16she took these Melanoma cells,
07:17she knocked out CD58 and then she Co
07:20cultured the cells with autologous T
07:22cells or engineered T cells at that point.
07:24And as you can see loss of the
07:26gene in fact convert a better
07:27survival of these cancer cells.
07:29And when you rescue these,
07:31the gene, either it's GPI anchored
07:34or transmembrane isoform,
07:35then you rescue the sensitivity
07:38to T cell cleaning.
07:40Furthermore,
07:40we also wanted to demonstrate
07:42that this interaction with CD2
07:45was in fact required for this
07:47immune evasion phenotype and that,
07:49you know,
07:50CD 58 loss didn't confer a loss
07:53of fitness through some other
07:54mechanism that we didn't know.
07:56So to test this we repeated the same
07:58experiment that I show on the left,
08:00only this time we rescued the
08:02knockout cells with a variant of
08:05CD58 harboring and mutation K34A
08:08which is unable to actually to CD2.
08:10And as you can see when you rest,
08:12when we rescue with the mutant,
08:13the cells continue to be resistant
08:15to D cell Co culture suggesting that
08:18this is very specific to that interaction.
08:21So one of the reasons I think this
08:23gene is not well understood is that
08:26there is no known mouse homolog.
08:28So we can't use the, you know,
08:30models that we typically like to use in,
08:33you know, studying, you know,
08:34immunotherapy and so on syngenic model.
08:37So to to you know, circumvent this,
08:39what we did is we used an
08:41immunocompromised mouse that has
08:43transgenic expression of human Illinois 2,
08:46which of course is required
08:48for T cell survival in vivo.
08:50So into these animals we could implant
08:53either the parental or the, you know,
08:56genetically modified cancer cell lines
08:58and then adoptively transfer the mouse
09:00with the patient's own tilts, right.
09:02So we could study this interaction
09:05in vivo and as you can see,
09:07the tumors that had the CD 58 loss
09:10were completely resistant to ACT.
09:12They also had an approximately hundredfold
09:15lower infiltration with T cells,
09:17validating some of the predictions
09:19that we had made in patients.
09:21And all of these effects could be rescued
09:24by RE expressing CD58 in the cancer cell.
09:27So overall this suggested that loss
09:29of CD 58 on the cancer cell conferred
09:31impaired T cell infiltration,
09:33proliferation and resistance to ACT.
09:35So coming back to that interesting
09:38interaction which I mentioned earlier,
09:40this interaction between CD58 and PDL 1.
09:44So we did a very simple experiment
09:48in which we knocked out CD58 and
09:50simply asked how much PDL one is
09:52on the surface of these cells.
09:54And in fact, when we knocked out CD58,
09:56we found that these cells do have more CD 58,
10:00excuse me, PDL one protein on the surface.
10:02And this effect again could be
10:05rescued by RE expressing CD58 itself.
10:08So the question then is,
10:10you know what sort of regulates this
10:12interaction and how do you go about this?
10:15Because we there's no like nothing really
10:17to help us inform of where to even start,
10:20right.
10:20What we could exclude pretty
10:22quickly is that there was no direct
10:24interaction between the proteins.
10:26So there had to be some sort of
10:28mediator that regulates that.
10:30So to do this systematically we
10:32did a genome scale loss of function
10:35screen that would show us or point
10:37us towards genes or proteins that
10:40are required for this interaction.
10:41So the design of the screen was that
10:43we took these Melanoma cell lines that
10:45express CAS nine and then we introduced
10:47the genome scale guide library to
10:49knock on every gene in the genome,
10:51let the cells, you know,
10:53edit for a couple of weeks.
10:55And then we sorted out the CD 58
10:58negative or CD 58 positive cells,
11:00sequenced the guide RNH guide
11:02RNAs in each of these pools.
11:04And the premise here is that
11:06in the CD 58 low pool,
11:08there must be perturbations that are
11:10somehow involved in regulating CD 58.
11:13So when you lose that gene,
11:14you see a reduction in CD58.
11:16And this is precisely what we saw.
11:18So here is a result of that screen.
11:21Reassuringly,
11:21the top hit of the screen was
11:24knockout of CD 58 itself,
11:26knockout CD58.
11:27There will be CD50 negative and a
11:29bunch of others.
11:30The one that really caught our attention,
11:32but we because we saw it
11:34also to physically interact
11:36with CD58IN in a mass spec Co IP screen,
11:38is this gene or protein called
11:41CMTM 6 super interesting?
11:42I know David had actually done
11:45some work that looked at the
11:47prognostic value of this protein,
11:48but it was unclear and I think we
11:50have the answer to why that might
11:52be So what was really interesting,
11:54just, you know, a year or two
11:57before we had made this observation,
11:59there were two Nature papers published
12:02showing that the same gene or protein
12:05CMTM 6 was in fact required for
12:08maintaining PDL one protein on the surface.
12:11So this was a plausible, you know,
12:14logical sort of hit to to go
12:15after and this is what we did.
12:17So when we knockout CMTM 6,
12:19we see a reduction in both CD58 and
12:22PDL one protein surface abundance.
12:24And when you rescue the gene CMTM 6,
12:27you rescue that and you have you you
12:29you bring them back to the baseline.
12:31And to really prove that this
12:32is required for the interaction,
12:34we generated a number of additional
12:36genetic perturbations,
12:37double mutants which we rescued where
12:40we rescued only one gene at a time
12:43where we could in fact demonstrate
12:45that CMTM 6 was required for
12:47mediating this reciprocal interaction.
12:49You know the the issue with these
12:51types of down signals, right,
12:53is always, well,
12:54how do you think about making
12:56a therapy from that,
12:57right?
12:58Because ultimately that's always the goal.
13:00So I'm going to skip a lot of
13:02data that we show in the paper.
13:04But Long story short,
13:06we identify we found that the binding
13:09A sequences on CD58 and PDL one for
13:13CM takes actually CMTM 6 actually
13:16differ and so we imagined that
13:18we could leverage that knowledge.
13:20So it turns out that CMTM 6 binds
13:24to a specific amino acid domain
13:28on North terminal domain in PDL 1
13:32spanning the amino acids 20 to 32.
13:35So when we scramble that area,
13:36turns out that, you know,
13:38CMTM 6 can no longer bind to PDL 1.
13:41So our we imagined that if we could
13:44selectively disrupt the interaction
13:46between PDL one and CMTM 6 at that site,
13:49it should result in reduction in PDL
13:521 without affecting the levels of CD 58.
13:55And this is precisely what we saw.
13:57So we took these Melanoma cell lines,
13:58we knocked out PDL One,
14:00and then we rescued either the wild
14:02type orph or a orph where we scrambled
14:05that region that is unable to bind CMTM 6.
14:08And as you can see, the WILD type
14:10orph rescues PDL ONE expression,
14:12but the mutant does not.
14:15And then in a Co-op experiment,
14:17we could also directly show that this
14:20variant where we scramble that sequence
14:22is unable to has a significantly
14:25lower binding of Co-op to CMTM 6.
14:28So just to summarize this part,
14:31I hope I was able to show you that
14:33we were able to go from you know
14:36sequencing data to using the right
14:38functional tools to really inform
14:39precisely kind of what to go after.
14:42But you know what we were left
14:44with is actually the question,
14:45how many of these interactions do we miss,
14:47right. Every time you knockout a
14:49gene and you observe A phenotype,
14:52you know how do we know that
14:53that's not mediated through a
14:55number of these interactions.
14:56And the, the the clinical or
14:58therapeutic correlate of that is,
15:00you know giving somebody a single
15:02agent immunotherapy or you know even 2
15:04drugs and asking well what do these,
15:06what does inhibition of these two proteins
15:08do to everything else that's going on,
15:10on the surface or within within the cell.
15:13And this is actually something we're
15:15trying to address systematically.
15:17OK.
15:17So switching gears a little bit and coming
15:20to the second chapter which is a much,
15:22much more recent chapter in the
15:25lab that is leveraging novel
15:28base editing tools to hopefully
15:32improve cell based immunotherapies.
15:35So all of you know that cell based
15:38immunotherapies are now a critical
15:40component of the treatment of many
15:42hematologic malignancies and most
15:44recently there was also an approval
15:46for the the treatment of till
15:49transfer for patients with Melanoma.
15:50And you know the the typical you
15:52know workflow is so that you take
15:54something out of the patient and
15:55you don't do something with the
15:57cells and you you put them back in.
15:58And in the context of CAR T cells,
16:00of course that's taking PBMCS and putting
16:02a CAR into the cells and reinfusing them.
16:05In the context of till therapy,
16:07it is isolating tills
16:09from metastatic lesions,
16:10expand them ex vivo and then
16:12give them back to the patient so
16:13that they have in common.
16:15What they also have in common is the,
16:17the observation that has really
16:19emerged in the last few years is
16:21that there are very specific T cell
16:24features before you put this therapy
16:26into the patient that are strongly
16:28predictive of whether or not that
16:31cell product is going to work.
16:33And and this was published by the
16:35Rosenberg Group A few years ago
16:37and there's nothing shocking about
16:38some of the observation.
16:40But it really is sort of the rationale
16:43for thinking about how we can improve
16:45T cell function itself to build
16:48better cell cell therapies on top.
16:50And of course,
16:51we're not the only ones thinking about.
16:53There's a lot of groups take CAR T
16:55cells until therapies and engineer
16:56them in a number of different ways.
16:59You know,
16:59frequently in the last few years
17:01what people have done is, you know,
17:03for example,
17:03knocking out an inhibitory receptor
17:05such as CTLA 4, right?
17:07And I picked one study from Carl
17:09Jun's group could have picked,
17:11you know,
17:11hundreds of other papers where they
17:13try to improve CAR T cell therapy by
17:16deleting some of these inhibitory receptors.
17:18But the challenge with knocking
17:21out a gene especially in T cells
17:24is that one of the off target
17:27effects of CRISPR CAS 9,
17:28which is due to the double
17:30stranded DNA breaks that
17:32take place is that you actually get
17:34a pretty high rate of aneuploidy.
17:35So 7 to 14% of cells in of T cells in
17:39a pool that you you know engineer with
17:42CRISPR CAS nine will be anemployed.
17:43And of course aneuploidy or chromosomal
17:46instability as I will talk about
17:48later is is a hallmark of cancer.
17:50And that comes with all sorts of concerns.
17:53Of course, what got me really
17:56interested in thinking about how to
17:58improve cell therapies are papers
17:59like the one that I'm showing you
18:01here from the Jonathan Powell group.
18:03So rather than deleting a gene or
18:06over expressing a gene which which
18:08comes with other issues, you know,
18:10they made a really interesting
18:12observation that is they found that
18:15some of their mice had sporadic
18:17mutations in a gene called TSE 2.
18:19The mutation itself really
18:20doesn't matter so much.
18:21But what they were able to show is actually
18:24when you take T cells from the mouse
18:26that has this germline mutation and you
18:29adoptively transfer mice with the wild
18:31type gene that harbor Melanoma tumors,
18:34those mutant T cells are much,
18:36much more potent in eliminating not only
18:39melanomas but they also show this in
18:41context of leukemias and and other diseases.
18:44So what that suggested to us is
18:46that maybe we don't have to,
18:48you know,
18:48take the wheels off the car.
18:50I mean knocking out an entire gene with
18:52all of its unintended consequences,
18:55maybe it's sufficient to introduce very
18:57specific mutations in genes that will
19:00significantly alter T cell function.
19:02And this is the hypothesis that
19:05we sought to to test in this
19:07project that just like in mice,
19:09there must be either naturally a cure
19:12occurring or synthetic protein variants
19:14that may enhance T cell function and
19:17therefore may enable the production of
19:20more effective cell therapies on top.
19:23And the method that you know
19:25would be required to do that,
19:28One of the ways to do that
19:29is using base editors.
19:31So there's different types of base editors.
19:33These are CRISPR CAS 9 dependent base
19:39editors that either citadine or adenosine
19:42deaminase linked proteins that unlike
19:45CRISPR CAS 9 don't introduce double
19:47stranded DNA breaks but rather they
19:50induce deamination events in very
19:53specific windows guided by guide RNAs.
19:56And ultimately in the in the example of
19:59citadine base editors you get C to T
20:01changes and in the context of denizine
20:03base editors you get A to G changes.
20:06So what this allows you to do
20:09is introduce at some specificity
20:11mutations at defined loci in a gene
20:14rather than knocking out the gene.
20:16And one of the challenges that that
20:18had existed with these base editors
20:21was the issue that their efficiency,
20:23especially in primary human T cells,
20:25was rather low.
20:27So we This was led by an MDPHD
20:30student in my lab.
20:31Zach Walsh has shown all the
20:33way to your left.
20:34He took it upon himself to try
20:36to improve the efficiency of
20:38these base editors because these
20:40would be the right tools to
20:42really introduce some of these
20:43mutations in a targeted fashion.
20:45And he's done that through a really sort of
20:48smart way of delivering the base editor.
20:51I'm not going to go through all the details,
20:52but suffice it to say, you know,
20:55we're able to achieve extremely high
20:57efficiency, relatively speaking,
20:59between 80% and and 99% with these base
21:03editors introducing very precise variants.
21:06This is a paper that is accepted,
21:08that will be published next week.
21:10But so, you know,
21:11equipped with these methods,
21:13we then imagined, well,
21:14what do we want to actually,
21:16you know, edit in these T cells?
21:17What do you even start?
21:18We we can't do this on the genome scale.
21:20There's too many bases, right?
21:22It would be an impractical experiment.
21:25So what we decided to do is rather
21:27be guided by experiments of nature.
21:30And what I mean with that is,
21:31you know,
21:32there's a lot of variants out there
21:34that are reported to be either
21:37definitively associated with immune,
21:38clinical, immune syndromes,
21:40either autoimmunity or immunodeficiency
21:42and everything in between or variants.
21:45And these are most of them that are
21:47variants of uncertain significance
21:49where there may be an association,
21:51but we don't know exactly because
21:53we can't prove each of them,
21:54you know, emotionally one at a time.
21:56So what we decided to do is put
21:59together sort of a library of 30,000
22:01variants that are out there across
22:04102 genes spanning all major T
22:06cell functions and introduce all
22:08of them with these base editors
22:10in a massively parallel fashion.
22:12And then ask how each of these
22:16variants changes known hallmarks of
22:18T cell mediated anti tumor immunity
22:22including the activation of T cells,
22:24the proliferation, cytochrome production,
22:26long term expansion, persistence, etcetera.
22:29And the the,
22:30the premise here is that we want to
22:33identify variants from this pool
22:35that improve most and perhaps all
22:37of those favorable features that
22:39we know are important to build
22:42good cell therapies on top.
22:44And so just you know a word
22:46about negative control.
22:47So in addition to the
22:48variants that we introduced,
22:49we had a number of
22:51different negative controls.
22:51I think it's always important
22:53to think about these when you
22:54do these large scale screens.
22:55So here's a distribution and
22:57the log fold change of negative
22:59controls that we had introduced.
23:01So these include splice acceptor
23:03and splice donor variants.
23:05Or when you when you mutate these
23:07these splice sites then what you
23:09get is truncated proteins that get
23:11basically knocked out or lack of a
23:13better word or they get truncated
23:15proteins that that are non functional.
23:18So as you can see these are
23:21significantly depleted at a very
23:22high lock fault rate ratio,
23:24while mutations that introduce silent
23:26changes or empty window changes
23:28don't change the distribution at all.
23:30In addition,
23:31another good control for T cells
23:33of course is introducing mutations
23:35that result in disruption of the
23:37CD3 complex because the cells need
23:39the complex to to be activated and
23:41proliferate and do what they do.
23:43And these are also significantly
23:44depleted and and this was highly
23:46consistent between different
23:48donors that we did the screen,
23:50so we did them multiple donors.
23:51So here are a few results from that
23:54screen now and I'm going to show
23:56a couple of very selective ones.
23:58So here we are looking at the lock fold,
24:01a change of genes and the designated
24:04variants that were introduced and the
24:07you know the negative lock 10 FDR.
24:09So the higher you go the most statistically
24:12significant things were in the screen
24:14and again there were many sort of
24:17expected depleted genes such as CD3
24:19or you know row A and what have you.
24:21But what caught our attention was this
24:25number of these mutations that were
24:28enriched meaning they improved T cell
24:31function were found in the PIC three
24:33CD gene and also in the PIC 3R1 gene.
24:36These two genes encode for the two domains
24:39of the immune cells specific PI3K delta.
24:44And this is another way to look at is this
24:46time we're only looking at pick three CD,
24:49this time looking at the amino
24:51acid sequence from left to right
24:53and where each of these mutations
24:55that either enrich on the top or
24:57deplete in the screen on the bottom.
24:59And as you can see,
25:00there's a number of of enriched
25:02variants here that are associated
25:05with a favorable TC cell phenotype,
25:08you know spanning residues 524 to 529.
25:11But then this is also this other variant,
25:14the C416R that was strongly enriched.
25:17And on the flip side,
25:18we have a mutation that that causes
25:20a loss of function of this gene.
25:23When you look at where these
25:25where this all of these gain of
25:27function mutations are located,
25:29they're not anywhere in the kinase domain.
25:31As you might imagine,
25:33since this is the catalytic
25:35sort of subunit of PR3K delta,
25:37they are all aligned and this is
25:39a prediction from alpha fold.
25:41They're all aligned at the interface
25:43between these two gene products
25:46of pick three CD and pick 3R1.
25:49So you know,
25:50we of course then went on to to
25:52validate these observations.
25:53You know one of the expectations
25:55would be that you have higher
25:56output from the PI3K pathways.
25:58So we looked at downstream signalling
26:01at phosphor akt and phosphorus 6 and
26:03we tested many different variants.
26:05But I'm just,
26:06you know highlighting here in
26:07the red box the loss of function
26:09mutation and then the green box
26:10the gain of function mutation.
26:12And as you can see the gain of function,
26:14you see more phosphor AKT and more
26:16phosphorus 6 while it is both of these
26:19are reduced in the loss of function.
26:21In the same vein and again I
26:23highlighted that with the green
26:24boxes the gain of function and in
26:26the red boxes the loss of function.
26:28And this is just a selection of the data.
26:29But we can see that the gain
26:31of function variant
26:32was associated with improved TNF alpha
26:34production and proliferation and so on.
26:36You know an initial validation
26:38of the of the screen.
26:39So now of course the question
26:41is can we use this information
26:42and improve cell therapies?
26:44Can they be better of cell killers?
26:46And to do this we used a simple
26:48coke culture experiment like the one
26:50that I had presented to you earlier.
26:53Only this time we engineered the T
26:55cells to express a very specific T
26:58cell receptor against Nye cell one,
27:00which is a commonly expressed
27:02neo antigen on Melanoma.
27:03So we can really test the specificity
27:06and then we either use the native T
27:08cells or we introduced one of a number
27:11of variants that we had identified in
27:13the screen and then Co culture them.
27:15And as you can see again the gain
27:17of function in green was strongly
27:19associated with a higher degree of Poly
27:22functionality here summarized as the
27:23fraction of cells that expressed TNF alpha,
27:26renzon B and IL 2 while the loss
27:28of function showed a reduction.
27:30And then this also translated in
27:33improved license of Melanoma cells.
27:35So here I think you can see my cursor.
27:37Yep.
27:37So here is the gain of function
27:39variant and we're looking at the
27:41number of surviving Melanoma cells.
27:42As you can see that strongly reduced
27:44the loss of function does not
27:47enhance the activity of the T cells.
27:50What was really gratifying and
27:52this sort of closing the loop
27:53to the first part of my talk,
27:55we were also able to show that this
27:57gain of function variant was able to
28:01overcome resistance from CD58 loss.
28:02So we did all of these coke culture
28:04experiments, all repeated them,
28:06only this time we knocked out CD58 and
28:09then the coke culture and the C416R,
28:12the game function variant and T cells
28:14was in fact able to almost completely
28:16radical themselves and you know,
28:18without the labouring the point too much.
28:20We also tested the same strategy in
28:22a number of different CAR T cells and
28:25we find the exact same thing whether
28:27you use the CD9 CAR or CD22 CAR
28:29against different leukemia models.
28:31Introducing these variants in the T
28:33cell that is the basis for making that
28:36product improved their functionality
28:38and their ability to lyse these.
28:40Looking as summarise this portion
28:44of the presentation,
28:45Hope was able to show you that we
28:48are now able to base edit primary
28:51human T cells with a high efficiency
28:54that unbiased discovery of variants
28:57from a from a big pool of variants
28:59may be able to identify those that
29:01improve T cell function and those
29:04perhaps could be used to improve cell
29:06therapies broadly in the future.
29:13OK. So now I'm going to switch to a
29:18somewhat different area of the of the
29:20lab or work in the lab that we're doing.
29:23But you know the common theme is that we are
29:26interested in what causes immune evasion
29:28and and lack of response to immunotherapies.
29:31And the the reason we got into this,
29:34again it's a clinical one.
29:35As you as all of you know,
29:37brain metastasis are a common
29:40problem across cancers,
29:41but very common in Melanoma.
29:43In fact, the incidence is probably as high as
29:4675% in patients who have advanced disease.
29:50And while the combination of
29:52immunotherapies are you know,
29:53showing efficacy against brain metastasis,
29:57there's still a lot of work to do.
29:59You know one of the reasons for that
30:00is that those regimens are very toxic
30:03and and despite the activity in some
30:05patients we we still see you know
30:07forms of Immunivision that that seem
30:09to be pretty distinct in the brain.
30:11So the the you know the the motivation
30:14was really to study a brain metastasis
30:16but I'll show you how that sort of got us
30:19into this field of chromosomal instability.
30:21So a couple years ago we we published a
30:25paper in this was led by Jana Johannes
30:27and Yiping postdocs in my lab where we
30:31asked a simple question in patients,
30:33what is the difference between an
30:35untreated brain metastasis and an
30:37untreated extracranial metastasis.
30:39What we didn't want is any sort of
30:41therapeutic intervention in between.
30:42We're really interested just in
30:44the salient biology which has been
30:47quite poorly described actually in
30:49patients compared to other you know
30:51areas in Melanoma at least.
30:53And I just want to point out a couple
30:55of sort of results from this paper.
30:57The first one is when we compare Melanoma
31:01brain Mets MBM versus extracranial Mets ECM,
31:04we found that the brain Mets were
31:06had a higher fraction of the genome
31:09altered FGA and that is a surrogate or a
31:13process called chromosomal instability.
31:15What is chromosomal instability?
31:16It is a a a hallmark of cancer.
31:19It's rather broadly seen across
31:22almost every solid tumor.
31:24And one of the ways by which chromosomal
31:27instability can arise is through errors
31:30that cancer cells make during anaphase,
31:32where they don't segregate chromosomes
31:34properly so that one of the data
31:37cell you know is left with more and
31:39the other one with less material.
31:41The end product of this is aneuploidy,
31:43right, and chromosomal civility,
31:44sort of the perpetual dynamic
31:46process that gives rise to that.
31:48The extra material in the one of the
31:51data cells is frequently packaged,
31:52if it survives,
31:54is frequently packaged in so-called
31:56micronuclei.
31:56So one of the ways to quantify chromosomal
32:00instability more functionally beyond
32:02just genomics is actually look at
32:05the frequency of those micronuclei.
32:07And this is what we did.
32:08This is in the same study where we
32:10had cell lines that were derived from
32:12either a brain or an extracranial
32:14metastasis from the same individual.
32:17We enumerated the rate of micronuclei
32:19and as you can see the one from the
32:21brain in fact had more micronuclei
32:23compared to the one that came
32:24from a lymph node in this case.
32:26And when we put these cells back into
32:29animals in immunocompromised mice,
32:30those cells in fact are more likely to
32:34cause brain metastasis in the mouse than
32:36those that come from outside the brain.
32:38The second sort of a key result
32:40from the study when we looked at the
32:43microenvironment was the observation
32:44that brain that appeared to have a much
32:47more rhotomogenic myelod compartment
32:48as you can see both from the single
32:51cell data to the on the left here.
32:53And then also we validated this
32:55in two independent patient cohorts
32:57by Multiplex immunofluorescence.
32:59So this is all in in Melanoma.
33:02The question then of course is you know
33:04what about other common cancers that
33:07frequently you know metastasize to the brain,
33:09The most common one in terms of
33:11prevalence is non small cell lung cancer.
33:13So naturally we're interested in
33:15asking do some of these concepts also
33:17apply to non small cell lung cancer
33:19And the answer is and this is sort of
33:21in an analogous study that we're that
33:23we are trying to publish right now.
33:26We also asked the same question in
33:28in that disease and this time though
33:30we had a lot more data.
33:32This time we could leverage data from
33:35the MSK impact cohort where we had
33:37genomic data that was linked with the
33:40location where the of the of the disease.
33:43So there's lots of primary tumors and
33:45then a bunch of different metastatic
33:46sites and then all the way on the right
33:49here you see again brain metastasis have
33:51the highest fraction of genome altered,
33:53again a surrogate for
33:55chromosomal instability.
33:56We went on to validate this and
33:58a couple of additional published
34:00cohorts that are out there as well
34:03as in a very large cohort of nearly
34:069500 patients where we had whole
34:08exome and RNA sequencing Through
34:09an industry collaboration,
34:11we find the exact same observation
34:13that brain Mets are more unstable
34:14than extracranial Mets which are
34:16more unstable than the primary tumor.
34:18So.
34:18So clearly this process seems to be
34:22important in conferring a sort of
34:25aggressive phenotype and perhaps
34:27also in modulating the immune
34:29environment in an unfavorable way.
34:31But it's kind of an obscure
34:33concept to to study, right,
34:36because it's such a perpetual
34:38dynamic process.
34:39How do you,
34:40how do you go about actually studying that?
34:42I think the so the first question
34:44that we as ourselves is well what
34:46is a good model to use to study this
34:49and what is a good model that we we
34:52could sort of easily identify, right.
34:55So the way we approach this problem is
34:58we looked at public data again TCGAACR genie,
35:01CP tag and we asked which subsets
35:04of lung non small cell lung cancer
35:08are particularly chromosomally
35:10unstable and are defined by very
35:13distinct genomic subsets.
35:14And it turns out that one particular
35:17mutation or loss in a gene called LKB
35:20one also known as STK 11 was across
35:23the board associated with a higher
35:25rate of chromosomal instability.
35:27What you also need to know about
35:30this particular subset of non small
35:32cell lung cancer which is common
35:34is that these patients virtually
35:37never respond to immunotherapy.
35:39So this is from a paper published
35:41from MD Anderson where they
35:42looked at patients with or without
35:44mutations or deletions in SDK 11.
35:46And as you can see here in red,
35:48these patients do extremely poorly
35:50in response to PD1 inhibition.
35:53And lastly,
35:54this particular subset happens
35:56to also more frequently be
35:59associated with brain metastasis.
36:01So you know, with this information,
36:03we believe that this particular subset is a
36:07really good archetypical sinhi chromosomally,
36:10chromosomal instability,
36:11high disease to study some of those concepts
36:14that that we want to understand better.
36:17And this is what Lindsay,
36:18another MDPHD student in my Lac lab,
36:21took upon herself a couple of
36:23years ago and she started with a
36:25couple of very simple experiments.
36:26We brought in a few human cell
36:29lines which are shown here and
36:30two of them are LKB 1 deficient,
36:32the other one LKB 1 proficient.
36:35And we did imaging and enumerated
36:37the rates of these micronuclear.
36:40I'm showing you here 2 exemplary ones.
36:42And as we would predict from
36:44the genomic data,
36:45the LKB 1 deficient subset in fact
36:47had more of these micronuclear,
36:49suggesting that there are in fact
36:51more chromosomally unstable.
36:52We also got 2 cell lines from Quoc
36:56Wong at NYU where he had, you know,
36:59established the the KP model,
37:01you know which have the K Ras
37:03mutation and P53 mutant.
37:04But on top of that they had developed
37:06a model with deletion of LKB one
37:08and derived A syngenic cell lines.
37:10So we got those into the lab as well
37:13and find that the LKB 1 deficient line
37:15in fact was more chromosomally unstable.
37:18So this seems to be shared between
37:20both the human and the available
37:22best available mouse models.
37:24So now coming back for a second
37:27to these micronuclei,
37:28as I mentioned earlier they are
37:31you know extra material that are
37:33engulfed in these mini nuclei.
37:35But the the,
37:36the envelope of these micronuclei
37:37is very rupture prone.
37:39So the DNA within those is released
37:41at some rate into the cytosol which
37:44of course is an absolute no go
37:46when it comes to you know normal
37:48immunity where you know our bodies
37:50and this is highly conserved are
37:52trained to sense DNA in the cytosol
37:54from all sorts of infections for
37:56example and respond to that.
37:58And and you know cells do that very
38:01efficiently through several pathways,
38:02perhaps the most important one
38:04being the C gas sting pathway.
38:06So here C gas senses cytosolic DNA
38:11and well OK lights off convert,
38:15convert this DNA to C gam which binds
38:17to sting and ultimately triggers a
38:20cascade that typically will result in
38:22the production of type 1 interferons
38:25which of course a very potent
38:27antiviral and anti tumor activity.
38:30Now this is only true when you
38:33activate the pathway very briefly,
38:36but this is not really what
38:38we see in cancer, right?
38:39Because chromosomal instability is perpetual,
38:42this pathway is tonically activated.
38:44And it turns out that when you do that,
38:47you actually flip the
38:48entire pathway on its head.
38:50And and this is what I want to
38:52demonstrate in in in the next few slides
38:54and how we might be able to use this
38:57information to target this process.
38:59So when you tonically activate the pathway,
39:01you in fact see less type 1
39:03inference production and you see a
39:05more aggressive phenotype sort of
39:07flipping the pathway on its head.
39:09So we wanted to test this hypothesis and
39:11we did a couple of simple experiments.
39:13First, we asked you know are LKB 1
39:17deficient cells in fact less capable
39:19of activating type 1 interferon
39:21related pathways And this is what
39:24I'm showing you here.
39:25We have these cell lines that we
39:27stimulated with double stranded
39:28DNA as sort of surrogate for
39:30chromosomal instability.
39:31And then we looked at a couple
39:33of key downstream nodes from in
39:35the C gasting pathway,
39:36phosphor TBK one and phosphor IR 3.
39:39As you can see the LKB 1 proficient
39:41Syn low cell lines are able to
39:44do that rather efficiently while
39:46the LKB 1 deficient lines do not.
39:48This results in a significant
39:50impairment in the LKB 1 deficient
39:52lines with respect to a couple
39:55of important Type 1 interference,
39:56I'm just showing you a couple selected ones.
39:59So suggesting that these,
40:00the Syn high state in fact confers
40:04impaired production of type 1 interference.
40:06So how can we now prove that it is
40:09sin that drives this impairment?
40:11So one way to do this is by
40:14modulating either up or down the
40:16rate of chromosomal instability.
40:18And one way to do this is by over
40:21expressing a variety of different
40:23genetic constructs which had
40:25been previously established.
40:26So in this example we can take a cell
40:30line that is highly chromosomally
40:32unstable at baseline LKB 1 deficient
40:34and over express a gene called MCAC,
40:37also known as KF2C,
40:39which improves the segregation
40:41fidelity that cells have when
40:43they undergo A chromosome.
40:45Segregation in so many words reduce the
40:47number of errors that these cells make
40:50and therefore on a population level,
40:52the rate of chromosomal instability,
40:54which is again measured here as the
40:56number of frequency of micronuclei.
40:58So when you suppress chromosomal instability,
41:01that alone is sufficient to rescue
41:04the ability of these cells to
41:06again produce type 1 interference.
41:09You can do the converse experiment
41:11where you take a cell line that is
41:13relatively chromosomally stable and
41:15express them with a different construct
41:17that makes them more unstable,
41:18as shown here again as a measure
41:21of micronuclei.
41:22And that alone is sufficient to reduce
41:25their ability to produce type 1 interference,
41:28suggesting that it is really
41:30sin that's driving the ability
41:32or inability of these cells to properly
41:34signal through this through this
41:36pathway and produce type 1 interference.
41:38The other way to to approach this of course
41:41is to imagine either deleting genetically
41:44or pharmacologically inhibiting C gas.
41:47And the rationale here is
41:48if you don't have C gas,
41:50then you won't be able to tonically
41:53activate the pathway downstream.
41:55And by at least temporarily relieving
41:57the tonic activation through genetic
41:59deletion of pharmacological inhibition,
42:02you might be able to allow this thing to
42:04come back to an equilibrium where you can
42:07leverage its physiological function which
42:09is producing these important cytokines.
42:11So we tested this in a number
42:13of different ways.
42:14One is by deleting Cgas and then
42:17stimulating the cells with the with
42:19its natural product that is CGAM O,
42:22we delete Cgas.
42:23We let the cells sort of relax for
42:25a week or two and then we ask was
42:27that enough to bring the pathway to
42:29an equilibrium and stimulate it and
42:31show that they're now again able
42:33to produce type 1 interference.
42:35And this is precisely what we find.
42:37So after a few days of of of
42:40of of deleting C gas,
42:42genetically stimulating the cells with C
42:44gaps or stimulating sting in this case,
42:47we see that these cells regained their
42:49ability to produce Type 1 inference.
42:51And this is both true in the human as
42:53well as in the mouse models that we use.
42:56And lastly we,
42:57we tested these concepts also in vivo.
43:00So here we use the KL,
43:02the LKB 1 deficient line and
43:05implanted them into B6 mice.
43:07We treated these animals either
43:09with with isotype control or with
43:11an anti PD1 antibody and just like
43:13in patients there is absolutely
43:15no response to PD1 inhibition and
43:18these LKB 1 deficient Synthi tumors.
43:21Deleting Cgas alone was sufficient
43:23to partly reduce the growth rate of
43:27these tumors but also significantly
43:29sensitized them to PD1 inhibition.
43:32The converse experiment we also did
43:35is taking the KP cell line which
43:38are relatively sensitive to PD1
43:39inhibition as you can see here and
43:41make them more chromosomally unstable.
43:44And this time we show that sin
43:46elevating sin alone is sufficient to
43:49render them resistant to immunotherapy.
43:51This is work that we have done
43:54in collaboration with Sam Bakum,
43:56the other Bakum and and Chrissy
43:59Hong a postdoc in his lap.
44:03Of course we're now interested in moving
44:05these concepts closer to patients.
44:07The problem is that there
44:09is no known, you know,
44:11soluble human selective CS inhibitor.
44:13So we work with our medicinal chemists
44:16to actually develop such an inhibitor.
44:19And what I'm showing you here is the REDUCT
44:22redacted structure of that compound.
44:24And on the bottom,
44:25the functional assay that we use to
44:27determine the activity of the compound.
44:28We're looking at the levels of
44:30CGAMP where we treated the cell
44:32lines that are we've you know I've
44:33shown you throughout this talk with
44:35this with this new compound and we
44:38see that we can pretty potently
44:40suppress the production of of C gas
44:42meaning the activity of C gas.
44:44And just treating the cells with
44:46these with the C gas inhibitor alone
44:50results in reconstitution of these set
44:52of these cell lines to phosphorylate
44:55TBK one and IR three more efficiently
44:58and ultimately result in an improved
45:01ability to produce these important cytokines.
45:05So with that, I want to summarize
45:07this last part of my talk.
45:09Hope I was able to show you that,
45:11you know not all metastases
45:13are created equal.
45:14Brain meds are quite distinct genomically.
45:17That tonic activation of the C gas
45:20thing pathway through sin is is bad
45:23and results in suppression of Type
45:251 interference that can be rescued
45:28through genetic modulation of sin
45:30or inhibition or deletion of C gas.
45:32And that we are, you know,
45:33very interested in moving this into the
45:37clinic using AC gas inhibitor of course.
45:39And I,
45:40you know,
45:41mentioned the people who have
45:42done the work throughout,
45:44but this is their entire lab.
45:45I want to think and of course
45:47all of the collaborators,
45:49both nationally and international
45:50collaborators who we are lucky to work
45:53with and the funding sources that support
45:55this work happy to take questions.
46:06Thank you Ben for a wonderful talk.
46:08I think you're very deserving of the plaque.
46:11While people think what they want to ask.
46:12I'm going to ask you a question
46:14about the CD2 CD 58 access.
46:17If you up regulate CD2,
46:19do you get reciprocal up regulation
46:21of CD 58 because that might
46:22be another way to approach.
46:24If you up regulate CD2 because
46:26you were focused on the tumor
46:28cell with CD 58, but can you
46:29manipulate it via the T cell?
46:31Yes. So we we did the other way around also.
46:34So we when we I guess I should talk here.
46:37So so we did the other way
46:39around where we knocked out CD2
46:41and rescued it in the T cells.
46:44The interesting part there is of course
46:46deletion of CD2 is basically leads to non
46:50responsiveness irrespective of CD 58 status.
46:52But the interesting observation and and and
46:54that might be an artifact of the system is
46:57when you over express CD2 through an ORPH,
47:00then you see actually suppression of CD 58.
47:05So we can't just change the
47:06T cell in that case. Yeah.
47:10All right. Questions.
47:11And I'm sure there's some
47:12online there 50 something people
47:14online I saw some have left bones. I didn't
47:18think the microphones working.
47:21OK Chen. Yeah.
47:24I guess I can click the. Yes,
47:26I can click the very interesting
47:29work. I have a question about the third part.
47:32You have to show those result
47:35in primary tumor setting.
47:37Have you looked at in metastas
47:39setting in brain metastas setting?
47:42No, because we don't have
47:44the right model for it.
47:45And I think that everyone who works on
47:49trying to understand brain metastasis,
47:51this was sort of how we
47:53got interested in this.
47:54One of the challenges is that
47:56to my knowledge at least,
47:58there isn't a good immunocompetent
48:01model that has a sufficiently high
48:03rate of developing brain metastasis
48:05in a number of different ways,
48:07at least through sort of natural
48:09sort of metastatic routes.
48:10So we haven't been able to
48:12actually directly study this in
48:14brain metastasis for that reason.
48:17So I assume some of the young
48:19models are LKB. One loss,
48:21maybe some of those could be positive,
48:26Yeah,
48:30yeah. But even putting the KL the the
48:34mouse cell lines that I've shown earlier,
48:37even if you do an LV injection
48:40or an intracarotic injection,
48:42they don't see the brain for some
48:45reason which we don't really understand.
48:48But what was the second part?
48:49You had another part to your question?
48:52That's yes.
48:53So, so I think it's just a limitation
48:56right now in terms of the available models.
48:59Since
48:59I'm sitting next to Chen, I'll take
49:01advantage to ask a quick question.
49:04So the CMTM 6 in the first part
49:06of the talk is interesting,
49:08but the binding region that you identified
49:10is in the extracellular domain.
49:12I thought it was a stabilizer
49:13binding to the cytoplasmic domain
49:15and the transmembrane protein, but
49:16it's actually it's actually the
49:18extracellular domain and it has two
49:20extracellular domains which we we we
49:23we also mutated and and shown in the
49:25paper that both extracellular domains
49:27of CMTM 6 are required for stabilizing
49:31both PDL one as well as CD 58.
49:33So it's clearly the extracellular
49:35domains that are necessary.
49:36It's a stabilization phenomenon
49:38that is a function of CMTM 6.
49:40So the function of CMTM 6,
49:42I didn't get to the into this
49:44in detail is it's it shuttles,
49:46it shuttles its cargo through
49:49through the recycling endosome.
49:51So typically when proteins are bound
49:53to CMTM 6, they are shuttled back and
49:56recycled back to the cell membrane.
49:59When you lose, I'm shown this in the paper.
50:01When you delete CMTM 6,
50:02you can show that they're more
50:05preferentially lysosomally degraded.
50:19Oh, thank you. Very fantastic talk.
50:22I'm extremely interested in the
50:24the third part of your talk,
50:26the LKP one seems like it's leading to
50:30leads to the chromosome's instability.
50:32But however in my impression now
50:36we think chromosome instability
50:37leads to more like mutation burden,
50:40which is a good thing for you know
50:43response to to immunotherapy.
50:45But in your story seems like it's
50:47on the other way and also you know
50:51some point that we know the PD1
50:53blocking antibody is approved for
50:55all cancer which has the MSI high,
50:58you know your tumors.
50:59So I wonder if you have any
51:01comments between on this.
51:02And also my second question is when you I,
51:07I know you're planning to put
51:09the C gas inhibitor on clinic,
51:12what's your,
51:13what's the approach in your mind
51:15because now we know there's a lot
51:18of steam possibly agonist those
51:20of it has been tested in clinic.
51:22So seems like you know,
51:25I I don't know if you ever discussed with
51:28Thomas Kiosky because he's a you know,
51:33yeah thing guy.
51:35So
51:36I am very happy that you asked both of
51:38these questions because I think it's
51:40really important to clarify a couple
51:42of things and maybe I went too fast.
51:44So to the first point, yes,
51:47TMB to some extent is associated weekly,
51:51but it is associated with
51:53response to checkpoint inhibition.
51:55What I was talking about here is not TMB,
51:59it's not, you know the number
52:01of non synonymous,
52:02you know mutations throughout the genome.
52:04When we talked about chromosomal instability,
52:06a different form of genome instability that
52:10is rather characterized by large changes,
52:12large structural changes,
52:14loss of chromosome arms or gains
52:17of entire chromosomes, etc.
52:19What we were actually able to show,
52:21and that was on the slide but I
52:23went over it probably too quickly,
52:25is that in this particular case of LKB 1,
52:28the mutation is not associated
52:30with a difference in TMB.
52:32It's only associated with a difference
52:35in the rate of chromosomal instability,
52:38at least the the genetic surrogate
52:39of it in in the PCGA cohort.
52:42So I think these are two different forms
52:44of genome instability that are very,
52:46very different. OK.
52:49And then the second part is you
52:52know with respect to sting,
52:55those structures don't work so
52:57well in patients, right?
52:58That's the reality,
52:59right.
52:59The response rate to a sting
53:02agonist is relatively low.
53:03There are and I I had some of them myself,
53:07there are some patients who are
53:09exquisitely responsive to them,
53:11but the vast majority of patients do not.
53:14And I think that the work that I
53:17presented here actually supports why that
53:20might be right because when you have
53:23this tonic activation of the pathway,
53:25as I said,
53:26you sort of you flip the pathway
53:27bit on its head.
53:28And so if you throw a sting agonist on there,
53:31you know it probably doesn't do
53:32very much right to the at least
53:34to the cancer cell and perhaps
53:36it might make the problem worse,
53:37right if if you stimulating the wrong clade,
53:40if you will of the downstream signaling.
53:43What I'm suggesting is that we
53:45might be able to, you know,
53:47rescue some of these in agonists,
53:49but they need to be combined
53:52in the proper way.
53:54So you could imagine that if you,
53:56you know, inhibit sea gas right in
53:58a in a cell line or in a patient,
54:01if you will,
54:02then you allow this pathway to come
54:04back to an equilibrium, you know,
54:06and then coming in with the sting
54:08agonist might be more fruitful.
54:09So I don't think these are
54:11mutually exclusive strategies,
54:12but I think that,
54:13you know,
54:14the work suggests that just
54:15throwing sting agonists on things
54:17will is unlikely to be beneficial.
54:28David, differential output.
54:34How's my treatment quoted? How's
54:36the self burgering out? Atomic CS activity.
54:45Fortunately, somebody figured
54:46that out. It wasn't me,
54:48but it was Sam's lab.
54:50They published a a paper last
54:52year in Nature that where they
54:54show that tonic activity of sting
54:58preferentially induces ER stress.
55:01And through ER stress you
55:03basically you know a tonic ER,
55:06ER stress in this case resulted in the,
55:08in the, you know,
55:10chromatostatic clade.
55:11And they've shown beautifully
55:13in in vitro models,
55:14in fibroblast models that it doesn't
55:16take very much of tonic activation
55:18to get to that point where you
55:20know if you stimulate once you get
55:22a really nice Type 1 interferon.
55:23But even after two or three
55:25stimulations over a couple of days
55:27you very quickly sort of you know,
55:29tacky for LAX on that pathway and
55:31you start activating the the bad
55:33counterpart which is in this case
55:34the chronic ER stress activity.
55:37OK.
55:38I'm going to give the last
55:39question to Ben and Lou.
55:42Perfect.
55:46OK,
55:53we we did not, we actually looked at that.
55:55That was one of my students
55:56just said I'm going to throw it
55:58on the on the flow cytometer.
55:59We don't see, we don't see changes and
56:01see the 58 level depending on Syn.
56:05Ben, thank you for a wonderful talk and be
56:10around for a few minutes if anyone
56:11has extra questions. Thank you.