Dissecting and Overcoming Different Shades of Cancer Immune Evasion

Transcript

• 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.