# Dissecting and Overcoming Different Shades of Cancer Immune Evasion

May 20, 2024ID11696

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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.