Microenvironmental Determinants of Systemic Therapy Response in Kidney Cancer: from human to mouse and back

Transcript

• 00:00Real pleasure to introduce Doctor Ari
• 00:02Hakimi as today's Grand Round speaker.
• 00:05He's the an Associate Professor
• 00:06and Co leader of the Translational
• 00:08Kidney Cancer program and Memorial
• 00:10Sloan Kettering Cancer Center. Dr.
• 00:12Hakimi is a urologic surgeon who's
• 00:14focused on the care of patients
• 00:16with urologic malignancies,
• 00:17especially kidney tumors.
• 00:18He received his medical degree in
• 00:20residency training from Einstein
• 00:22College of Madison and completed
• 00:24his fellowship in Urologic Oncology,
• 00:26Oncology and Memorial Sloan
• 00:27Kettering Cancer Center.
• 00:29His research really aims to
• 00:30understand immune infiltration,
• 00:31inflammation in the tumor microenvironment
• 00:33in RCC and to identify novel
• 00:35therapeutic targets to overcome
• 00:37resistance to systemic therapy.
• 00:39His studies apply bulk single
• 00:41cell and spatial RNA sequencing,
• 00:43flow cytometry and immunogenomic analysis.
• 00:46Really to understand both patient
• 00:48samples and a novel immunocompetent
• 00:50kidney cancer mouse line that his
• 00:52lab has developed really has been
• 00:53a game game changer for the study
• 00:55of kidney cancer and particularly
• 00:57the immunobiology of kidney cancer.
• 00:59I followed Ari's work for many,
• 01:01many years now and to see him go
• 01:02from what was a rising star in kidney
• 01:04Cancer Research to now really one of
• 01:06the world leaders has been a pleasure.
• 01:08And it's a a body of work that
• 01:09I've tremendously admired.
• 01:10So it's really a pleasure to be able to walk.
• 01:12Welcome Doctor Akimi to grand rounds today.
• 01:21All right. Thank you so much.
• 01:22And it's really a pleasure to be here
• 01:24at Yale and especially I was especially
• 01:27enthusiastic come here because of David
• 01:31and I mean talk about rising stars.
• 01:32David is incredible and he's got great
• 01:34mentors here with with Harriet and others.
• 01:36And I think it's it's a real pleasure here.
• 01:39So that's my only disclosures,
• 01:40none of which is pertinent
• 01:42to this talk today.
• 01:43So I'll talk a little bit about the genetic,
• 01:46the genomic and genetic background
• 01:48of kidney cancer,
• 01:50in particular clear cell renal cell
• 01:52carcinoma which is the most common and
• 01:54aggressive form of kidney cancer but
• 01:55also one of the most immunoresponsive.
• 01:57We'll talk a little bit about the
• 01:59role of the micro environment as a
• 02:02predictive response and really focus
• 02:03on myeloid compartment which is one
• 02:06of my lab's interests a little bit
• 02:08from the genomic determinants of this
• 02:10and then some of the insights using
• 02:12both human and mouse strategies to
• 02:15understand this for for future targeting.
• 02:17So kidney cancer is about is the
• 02:206th most common cancer overall or
• 02:23eighth most common cancer overall,
• 02:256th most common in men.
• 02:26There's a 2 to one gender difference
• 02:28and we know that you know within
• 02:30the kidney there are many,
• 02:31many subtypes of kidney cancer and even
• 02:33if you just took the most common subtypes,
• 02:36they're very genetically different.
• 02:37Clear cell represents the most common form.
• 02:40About 6570% of all kidney tumors are
• 02:43a clear cell and we know the most
• 02:45about it from a genetic standpoint.
• 02:47But we also,
• 02:48there's also some very intriguing
• 02:49phenomenon that existed in it,
• 02:50particularly the amount of the
• 02:52immune response.
• 02:53And it's still not clear why these
• 02:55tumors are so immune infiltrated and
• 02:56and why they are so responsive to
• 02:59immunotherapy compared to other tumors
• 03:01that are much more highly mutated,
• 03:03for example like melanomas or or
• 03:05bladder lung cancers where you see
• 03:06a lot of mutations in those tumors.
• 03:08And probably you know everyone
• 03:11references TCGA papers initially in
• 03:14terms of the fundamental understanding.
• 03:16And I think some of the takeaways
• 03:17from this tumor from this analysis
• 03:19which is one of the first tumors to be
• 03:21profiled was that it's really dominated
• 03:22by a few driver mutations related to
• 03:25tumor suppressors On the 3P locus.
• 03:28There's not a lot of mutations.
• 03:29There's some copy number events that
• 03:31are really fundamental and maybe some
• 03:33of which are enriched in metastases.
• 03:35But there's not an obvious clue as
• 03:37to you know why these tumors retain
• 03:40such a high immune infiltration.
• 03:42We also know a little bit about what happens.
• 03:44Thanks to Seminole work from the
• 03:46Sanger Institute where they looked
• 03:48at what were the fundamental events
• 03:49that that are associated with clear
• 03:51cell nasal carcinoma development.
• 03:53And what this paper showed for the
• 03:56really the first time was that
• 03:58the loss of chromosome 3P1 arm is
• 04:02critical to the oncogenesis and then
• 04:04that's followed by VHL loss whether
• 04:06it's mutations or or methylation.
• 04:08But basically 90% of all clear cells
• 04:11have this and then eventually over time
• 04:13additional driver mutations are lost and that
• 04:16leads to different evolutionary subtypes.
• 04:18And in this paper Samara Trashlik and others
• 04:22came up with a relatively complex schema.
• 04:25But you know, fundamentally you can
• 04:27think about it as the tumors lose
• 04:29VHL and then they usually acquire
• 04:31one or two additional hits to form
• 04:33into sort of different trajectories.
• 04:35We know that tumors that lose PBM one and
• 04:38set D2 for example maybe more angiogenic,
• 04:41they may they may tend to be a
• 04:43little bit more indolent overall,
• 04:45maybe possibly more responsive to certain
• 04:47therapies like including even immunotherapy
• 04:49although that's not entirely clear.
• 04:51And then BAP one mutations which occurs
• 04:53well are are typically associated with
• 04:55more high grade aggressive proliferative
• 04:57tumor types and then you can have multiple
• 05:00clonal drivers which also represent a
• 05:02very aggressive form of kidney cancer.
• 05:03So we're starting to get a better
• 05:05framework for the underlying genomics,
• 05:07but none of this really has been
• 05:08shown to be targetable.
• 05:09So for years we just really started to
• 05:12understand what was driving kidney cancer,
• 05:14but we really didn't,
• 05:15wasn't giving us any further insights,
• 05:16weren't an oncogene that you
• 05:17could develop a target to.
• 05:18And while people are certainly working
• 05:21on epigenetic regulation and strategies,
• 05:24it's it's certainly not an obvious pathway
• 05:27forward in kidney cancer at least right now.
• 05:29And at the same time,
• 05:30we also knew clinically that that
• 05:32you know most of the targeted
• 05:33therapies were limited to the micro
• 05:35environment of the of the cancer.
• 05:37And really we've seen a tremendous
• 05:39growth in in outcomes and survival
• 05:41for kidney cancer patients.
• 05:43But it's all really focusing
• 05:44on the micro environment.
• 05:45So even back in,
• 05:46in the 90s when we were studying IL 2
• 05:49both in melanomas and kidney cancers,
• 05:51that was really the only
• 05:53treatment that seemed to work.
• 05:54We had tried all the chemotherapies
• 05:56you can imagine in the 80s,
• 05:5890s and you would see responses
• 06:01even 7 to 10% cures,
• 06:03but with very,
• 06:04very high toxicity in those
• 06:06populations and in those patients.
• 06:08And it wasn't until the advent of
• 06:10of really by work from Bill Kalin
• 06:13and others where we recognized the
• 06:14importance of Hifs that all these
• 06:16focus on VEGF had come around.
• 06:17And then it wasn't really until this
• 06:20notion of immunotherapy came around that
• 06:23we started to see this big revolution
• 06:25in terms of survival and outcomes.
• 06:27But it's all really focused on,
• 06:28on the micro environment.
• 06:31We know that you can take some
• 06:33of these genetic events that I
• 06:35mentioned earlier and risk stratify
• 06:37patients a little further.
• 06:38This is the work that we did several
• 06:40years ago now looking at the impact
• 06:43of some of these common mutations
• 06:45and outcomes for patients that were
• 06:47receiving VEGF therapy and it was
• 06:49you know prognostic maybe you could
• 06:51further stratify patients that
• 06:52were grouped into clinical risk
• 06:54groups by by common mutations.
• 06:56But it wasn't really telling us anything
• 06:58about the underlying immunobiology or
• 07:01angiogenic biology in these tumors.
• 07:02It was really more just a
• 07:04prognostic feature about it.
• 07:06So with this revolution of
• 07:09immunotherapies both by themselves
• 07:11and in combination with VEDF therapy,
• 07:13we've seen the the survival rate and
• 07:15the response rates go up dramatically.
• 07:17So you know the median survival when
• 07:18when I first started my training
• 07:20for metastatic kidney cancer was
• 07:22you know a year and a half or so
• 07:24and now we're pushing five years
• 07:25for for a lot of patients and and
• 07:28potentially curing some patients.
• 07:30And we there's a real need to
• 07:32understand why that's the case and
• 07:34how we can do better because we
• 07:35know that invariably most patients
• 07:37despite this high response rate will
• 07:40eventually develop resistance and you
• 07:43know understanding why that's the case
• 07:45requires you know good models to do.
• 07:47So we know that from an immunotherapy
• 07:51standpoint that it's not ATMB driven
• 07:53tumor at least not obviously and
• 07:55maybe you can break down the types
• 07:57of mutations a little bit more.
• 07:58I was just talking to David about
• 08:00this last night,
• 08:01but you know there's it's not obvious.
• 08:04There have been several attempts
• 08:05to look at TMB as a predictor for
• 08:07responses and most of the large clinical
• 08:08trials that have been performed that
• 08:10have have released their data have
• 08:11not shown this and certainly David
• 08:13has been on the forefront of this.
• 08:14But if you look across some of
• 08:16the major phase three trials that
• 08:18have at least released their data,
• 08:20there's not a really a signal at all
• 08:22with respect tumor mutation burden.
• 08:23We've looked in David and others have
• 08:26looked at whether mutations in PBM one
• 08:28or loss of nine P which is a common
• 08:31event in metastatic kidney cancers,
• 08:33whether that's associated with
• 08:34immune infiltration patterns.
• 08:35There may be some signals there.
• 08:38It's not a clear biomarker though and
• 08:40and that sort of has been lacking from a
• 08:42mutational and copy number standpoint.
• 08:44So I think one of the things that
• 08:46is unique about kidney cancer is
• 08:47that you know we've started to look
• 08:49many years ago now at transcriptomic
• 08:51predictors because the mutations
• 08:53are clearly and copy number of
• 08:55events are clearly not sufficient to
• 08:57determine who's going to respond at
• 09:00least from a biomarker standpoint.
• 09:01And you can just take a very simple
• 09:04metric of the uniqueness of kidney
• 09:06cancer and this I just plot out you
• 09:08know VEGFA and CD8 infiltration.
• 09:10This was an older slide,
• 09:11but I like showing it 'cause I think it
• 09:13shows the uniqueness of kidney cancer,
• 09:15clear cell,
• 09:15at least with respect to some of
• 09:17the micro environmental genes.
• 09:19And we know that they're just
• 09:21dominated by high infiltration of CD8
• 09:24cells and high angiogenic programs.
• 09:26So the question of course and this
• 09:27was shown across cancers and it's
• 09:29really distinct from its from the,
• 09:31from the normal tissue.
• 09:32If you look at for example lung cancers,
• 09:35many,
• 09:36much of the lung itself is very
• 09:38mean infiltrated
• 09:38likely due to smoking or other
• 09:40other carcinogenic features.
• 09:41But the kidney itself,
• 09:43the normal kidney is not
• 09:44particularly mean infiltrated,
• 09:45but the tumors are often very
• 09:47dramatically infiltrated.
• 09:48So there's something very distinct
• 09:50about the actual tumor itself
• 09:52rather than the underlying organ
• 09:54that it's that's derived from.
• 09:56This is work we did when when I
• 09:58was just starting out and we we
• 10:00you know we we we used immune
• 10:02deconvolution strategies to show this.
• 10:04You could take signatures for
• 10:08T cells or for macrophages,
• 10:11NK cells etcetera.
• 10:12And you can start just deconvoluting
• 10:15bulk RNA sequencing data and start
• 10:17to try to understand where tumors
• 10:19or particular samples might might
• 10:21fall in a spectrum and and you
• 10:23can use this to also subgroup.
• 10:25So we use this strategy to kind of
• 10:27think about tumor micro environmental
• 10:29subgroups within kidney cancer and
• 10:30this was our first attempt about
• 10:32eight or nine years ago to look at you
• 10:34know whether there's these enriched
• 10:36groups and whether there's you know
• 10:38other groups within kidney cancer.
• 10:40Maybe that would sort of explain why you
• 10:42see some some really great responses
• 10:43in the streaming top And we we did
• 10:46see that we saw you could clearly see
• 10:48these T cell infiltrated clusters.
• 10:49You could see at this point we really
• 10:51didn't think about angiogenesis,
• 10:52but in retrospect you know you've
• 10:54seen angiogenic cluster and we'll
• 10:56talk more about that in a minute.
• 10:57And you know it did correlate with
• 11:00certain genetic programs mostly
• 11:01antigen presenting machinery programs.
• 11:04So there was an up regulation of antigen
• 11:07presenting machinery transcript,
• 11:08but it wasn't clear still from this
• 11:10point what was actually driving this.
• 11:12We we looked at genetics,
• 11:15common mutations and wasn't at
• 11:17least obvious at the time when
• 11:18we first did this study,
• 11:19although that's evolved a bit and
• 11:21that same approach was applied by
• 11:23Genentech when they first published
• 11:25and then analyze the EMOTION trial.
• 11:27This was the first attempt to
• 11:29combine VEGF and IO therapies.
• 11:30They used tizolizumab and bevacizumab,
• 11:33which is a VEGF monoclonal antibody.
• 11:36And this,
• 11:36this trial was negative in terms
• 11:38of improving the standard of care,
• 11:40but it was biomarker.
• 11:41Biomarker Rich and I give a lot of
• 11:44credit to Genentech for not only doing
• 11:46phenomenal genomic work but also
• 11:48making it all publicly available,
• 11:50which is something that other companies
• 11:51have have been a little reluctant to do.
• 11:53So and maybe it was because it
• 11:55was a negative study,
• 11:55they were willing to share so much,
• 11:56but it really was very helpful.
• 11:58And David McDermott and others from
• 12:02Boston performed really the first
• 12:04type of analysis in the context
• 12:06of systemic therapy to show that
• 12:08these micro environmental groups
• 12:10angiogenesis and T cell infiltration
• 12:12and myeloid programs may stratify
• 12:14patients into different groups but
• 12:16also may associate with response.
• 12:18And one thing I would point out I
• 12:20think it it it's sort of logical
• 12:21that a tumor that may have a lot of
• 12:24T effector cells would respond well
• 12:25to amitotherapy.
• 12:26But you know what they also showed
• 12:29was that myeloid populations as as
• 12:31determined by again a gene program
• 12:33we're we're driving resistance also.
• 12:36So you could be AT effect or
• 12:38high tumor but if you had a high
• 12:41myeloid program it could supersede
• 12:42that impact and and and in fact
• 12:45actually show you know dramatically
• 12:46different responses in that context.
• 12:48So that really sort of laid the groundwork
• 12:50for not only the micro environment
• 12:52relevant but also it could it could
• 12:54predict responses and maybe give us
• 12:56some insight into into resistance.
• 12:58We applied that same strategy initially
• 13:01to just a VEGF cohort only and this was
• 13:03work that I did with with Bob Moecher,
• 13:05one of my mentors at at Sloan
• 13:07Kettering for many years.
• 13:08And this was just looking at
• 13:10the first trial that compared
• 13:12two different VEGF inhibitors.
• 13:14At the time we had really sunitinib
• 13:16and this was the first attempt to try
• 13:19a different strategy or a different
• 13:21VEGF inhibitor and you know it was
• 13:23really more to look at tolerability.
• 13:26There was no difference really in
• 13:27responses but actually Piszopinib
• 13:28but this you know showed a better
• 13:31toxicity profile for patients.
• 13:32So that became the standard of
• 13:33care ELISA Memorial for many years
• 13:35until obviously we developed next
• 13:37generations and immunotherapies.
• 13:38But at this time we took a
• 13:40transcriptomic approach.
• 13:40So we had microarray data from
• 13:44Novartis and we had looked at this
• 13:46question of whether you can identify
• 13:47subgroups and we found you know 4
• 13:49transcriptomic subgroups at the time
• 13:51they tended to really stratify patients.
• 13:53Again,
• 13:53all these patients received EDF first line,
• 13:55so clean,
• 13:56clean cohort and you could clearly
• 13:58see a difference in in groups.
• 14:00And there was a green group here
• 14:01that was very responsive and a red
• 14:02group that was very not responsive.
• 14:04And then there was these yellow and
• 14:05blue in the middle and the green
• 14:07group really had a lot of angiogenic
• 14:08program and that made sense, right?
• 14:10You know, if you have a lot of angiogenesis,
• 14:13it makes sense that you would
• 14:15respond very well.
• 14:16But the red group which was #4,
• 14:18that was the one that didn't respond well.
• 14:20They were actually the worst but they had
• 14:22the second highest amount of angiogenesis.
• 14:23So why why was that the case?
• 14:24Why didn't they stratify nicely by
• 14:27by angiogenic program and and when we
• 14:29compared that group to the other groups,
• 14:31we could see that it was really
• 14:32being dominated by a myeloid program.
• 14:34So despite having high angiogenic
• 14:37phenotype or transcript,
• 14:38they were they were reversing the
• 14:41response based on an infiltration of
• 14:44myeloid myeloids at least inferred
• 14:46by by microarray data.
• 14:49So,
• 14:50so that suggested that it could
• 14:53actually be driving a response overall
• 14:56and the micro environment may be
• 14:58useful in understanding biomarkers in in,
• 15:01in metastatic kidney cancer.
• 15:03And then actually this was something
• 15:04that we did and at the end and
• 15:05actually the my fellow at the time,
• 15:06one of the urology fellows at
• 15:07the time who was working in,
• 15:08in my group actually had the
• 15:10suggestion that we look,
• 15:11we kind of left them all together
• 15:13because there's open Evans Sunitnib,
• 15:14we're sort of both VEGF inhibitors.
• 15:16But we know that the TKIS target
• 15:18lots of different kinases,
• 15:20not they're not super specific
• 15:22and actually if you look at the
• 15:25macrophage and angiogenic groups,
• 15:26you could actually see that
• 15:28Pizzopinib has quite a different
• 15:31stratification than tsunitiv.
• 15:31This suggests to us we we didn't
• 15:33talk about too much in the paper,
• 15:35but it it really suggested that the
• 15:37targets of these TKIS may also actually
• 15:39be driving some of their responses.
• 15:40So some of these kinases are present on
• 15:43on immune cell populations for example.
• 15:45And the fact that these biomarkers
• 15:47were actually different with respect
• 15:49to the different Tkis was something
• 15:51that that we've now followed up on.
• 15:53And I think it's a really
• 15:55interesting finding that he made.
• 15:56And this just leads back to the
• 15:58same concept that what what Dave
• 16:00showed in in this in this beautiful
• 16:02paper from the from the Genentech
• 16:04study others have looked at micro
• 16:06environmental features in other
• 16:08in other more positive trials.
• 16:09So this,
• 16:10this was also sort of a negative,
• 16:11well not negative but has not has
• 16:14not been brought forward further.
• 16:16This was the Javelin 101 study again
• 16:20avolumab and exitinib again another
• 16:22combination of PD one and and and VEGF
• 16:26and they focused on a a lymphocytic
• 16:31signature identified 26 genes again
• 16:34specific signature for specific trial.
• 16:36Some of the challenges of these have been
• 16:38you know applying it to other data sets.
• 16:39But again you could see the the
• 16:42micro environmental features being
• 16:44associated with response here
• 16:46and suggesting you know that we
• 16:48could utilize this as a strategy.
• 16:50And then there have been subsequent
• 16:52efforts by by collaborative
• 16:55groups including Genentech again
• 16:57to integrate what we know about
• 16:59mutations and those evolutionary
• 17:01subtypes I showed you earlier into
• 17:03and the micro environmental feature.
• 17:05So if we have the micro environment
• 17:07and we know the genetics that are
• 17:09that arise as kidney cancers evolve,
• 17:12could they could they be sort of
• 17:14grouped together to form these kind
• 17:16of different molecular subgroups?
• 17:18And I think there's been some
• 17:19attempt to do this.
• 17:20I think it's improving,
• 17:22but you know you sort of have the sense
• 17:24that there are these different angiogenic,
• 17:26stromal and angiogenic alone.
• 17:27So some of these may have this
• 17:29myeloid phenotype that I showed you
• 17:31earlier and just a purely angiogenic
• 17:33tumor maybe the purely angiogenic
• 17:35tumors would respond really well to
• 17:37VEGF alone and those are tend to
• 17:39be the less aggressive tumors PBR 1
• 17:41mutated and then you have the ones
• 17:43that are myeloid and angiogenic
• 17:45and those actually don't respond
• 17:46at all to to VEGF inhibitors and
• 17:48then you have these proliferative
• 17:49ones and and other ones.
• 17:50So we're starting to get a sense
• 17:52that maybe you can subgroup kidney
• 17:54cancers into those features.
• 17:55And then came along this
• 17:57notion of single cell.
• 17:59And there have been a series of papers,
• 18:01one of which David LED,
• 18:02but that came out from the time
• 18:03because we had done all of all
• 18:05this work on bulk RNA sequencing.
• 18:07And as the fields across
• 18:09cancers have evolved,
• 18:10we started utilizing single cell to not only
• 18:13get a better sense of what was happening,
• 18:17but also really understand you know
• 18:18what are the specific features of these of,
• 18:21of the of the micro environment
• 18:22in a much more high resolution.
• 18:23So the advantage of bulk RNAC of
• 18:25course is that you can do big,
• 18:26big numbers of samples because
• 18:28it's relatively inexpensive.
• 18:30Single cell gives you deep dive,
• 18:31but often the cohorts were
• 18:32much more modest in size.
• 18:34So I think there's constantly
• 18:35a need to go back and forth.
• 18:36If you find a signal in one,
• 18:37you have to validate in the
• 18:39other so to speak.
• 18:40And so we we we did this in in
• 18:44clear cell focusing really on
• 18:46patients that had received just dual
• 18:49immunotherapy with PD1 and CTLA 4.
• 18:52We focused on 6 patients initially.
• 18:55When we did this together with Ming Lee,
• 18:57one of my immunology mentors,
• 18:59Christina Leslie,
• 18:59who's a computational biologist and a very,
• 19:01very talented graduate student at the
• 19:03time who's finishing his post doc at Harvard,
• 19:06now Shirag Krishna.
• 19:07And we looked at patients that had
• 19:10either were were high risk and not
• 19:14had not received PD one right away or
• 19:17versus ones that had had Ipinivo. Yeah.
• 19:21And were eventually went on to surgery.
• 19:24One of the unique things I I do
• 19:25as a surgeon is that we're able
• 19:27to get tissue after treatment and
• 19:28kidney cancer has evolved so much
• 19:30so because of the response rates
• 19:32now to upfront immunotherapy that
• 19:33most patients if they come in with
• 19:35metastatic disease will get upfront
• 19:37systemic therapy and then we're being
• 19:39asked to operate on them later on.
• 19:41So that gives us a unique opportunity
• 19:43to study tissue after treatment,
• 19:44which is something I think really
• 19:46unique to kidney cancer amongst many
• 19:48solid tumors we have this opportunity.
• 19:49So we were able to utilize that
• 19:51strategy here and this sort of
• 19:53gave us a broader sense and this
• 19:54has been replicated I think by
• 19:56many other single cell studies
• 19:57including David's really Seminole
• 19:58work in this and you can kind of
• 20:01get a sense of what's happening now.
• 20:02One of the things that's interesting
• 20:04about a quirk of single cell is that
• 20:07you know there's for those of you
• 20:09familiar with the technology is that
• 20:10you know there's generally at least if
• 20:12you do single cell and not single nucleus,
• 20:14you have to do some sort of
• 20:16sorting and a lot of the.
• 20:18Tumor cell populations actually
• 20:19die die off from that process.
• 20:20They're very fragile for some ironically
• 20:23and immune cells will often survive
• 20:25although you lose neutrophils.
• 20:27So kind of interesting quirk
• 20:29of any sort of single
• 20:31cell study that you do, you lose a
• 20:33lot of the cancer cell populations.
• 20:34But we're able to kind of get a
• 20:35good sense of what's going on in the
• 20:37immune cell population and you get
• 20:38a general sense that and this has
• 20:39been replicated by our flow analysis
• 20:41over the many years that about 6040
• 20:44to 60% of the immune compartment is,
• 20:46is made-up of T cell and you have
• 20:49a good amount of Tams in this.
• 20:51And then a whole bunch of other
• 20:53populations including B cells and K
• 20:55cells and dendritic cell populations,
• 20:57but they're really dominated by these T
• 21:00cell and and and and and Tam populations
• 21:03and you could further phenotype them
• 21:04into you know and this has been done.
• 21:06You know,
• 21:07everyone's got their own slightly different
• 21:08way of of phenotyping populations,
• 21:10but this allows to sort of get a
• 21:13sense of what's happening, yeah,
• 21:14in both primary sensitivity and
• 21:16and primary resistant patients.
• 21:18And you know this one again we
• 21:20had epinivo resistant and a mixed
• 21:22response and a complete response,
• 21:24complete response patients are always
• 21:25interesting because why are we operating
• 21:27on them if they have a complete response?
• 21:28Well, when I say complete response,
• 21:30I mean that the tumor's
• 21:32mass itself is not viable.
• 21:33It's it's it, it there's a mass there,
• 21:35we we take it out.
• 21:36But actually under the microscope,
• 21:38there's no tumor left.
• 21:39It's just a conglomerate of
• 21:40immune cells and fibroblasts.
• 21:43And so that's kind of an interesting
• 21:44population to look at because
• 21:45what's what's residual there.
• 21:46And there we found these tissue
• 21:49resident T cell populations that
• 21:51were very abundant in the in the
• 21:54in the residual mass of the of the
• 21:56of that of that kidney even though
• 21:58it was there was no tumor left.
• 22:00And then we found within the
• 22:01patients that were not responding
• 22:03it really you know there was T cells
• 22:05there but it was really dominated
• 22:06by specific Tam populations.
• 22:08This was a primary resistant patient.
• 22:09He had had big tumor, big lymph nodes.
• 22:11We gave him a trial with Epinivo to
• 22:13see if that would help and it didn't.
• 22:14So we still end up operating on him,
• 22:16no response in the tumor whatsoever.
• 22:18And you could see this was really
• 22:20a Tam dominated tumor type.
• 22:21So it started giving us insights
• 22:22that really this may be associated
• 22:24again small numbers.
• 22:25So you really have to start building
• 22:27that out and you can develop
• 22:28signatures which is what we did.
• 22:29We actually took the single cell genes
• 22:32and from the different clusters and
• 22:34overlaid them on some of these genomic
• 22:37signatures that have been published.
• 22:38The javelin when I mentioned the the,
• 22:40the,
• 22:40the genomic ones from Genentech and we
• 22:43started saying like what are the actual
• 22:45populations that they're capturing.
• 22:47You get a better sense that there
• 22:49are some dominant Tam populations
• 22:51and that some of these populations
• 22:53may be potentially targetable.
• 22:54And I'll talk about that in in a
• 22:57moment. But I want to bring your
• 22:59attention to some of these adenosine
• 23:01signatures that were were published
• 23:03from an HUAR inhibitor which is
• 23:05something that has been shown to
• 23:07potentially shift Tam phenotypes.
• 23:08So, so this was sort of an interesting
• 23:10way for us to look at the data and
• 23:12you can develop signatures based on
• 23:13the single cell data and compare them
• 23:16to existing signatures to see if you
• 23:19could further stratify patients and
• 23:21responses across different different data
• 23:23sets And and we were able to do that.
• 23:25And then the question is also, well,
• 23:27is there a relationship between the
• 23:29underlying micro genetic micro environment
• 23:32and these specific immune micro environments?
• 23:35So I I showed you again on bulk,
• 23:37maybe there's these different
• 23:38sub classifications,
• 23:39but we also know there's a lot of
• 23:41heterogeneity in kidney tumors, right.
• 23:42So we know that if you took a kidney
• 23:44tumor and you sequence different regions,
• 23:46Charlie Swanton showed this many years
• 23:48ago in a famous paper New England Journal,
• 23:49intratumal heterogeneity exists.
• 23:51Does that same thing apply to
• 23:53the micro environment as well?
• 23:54And that's something of of course
• 23:56if you're going to develop a
• 23:57biomarker or suggest something,
• 23:59you have to think about that.
• 24:00And this is work that we did in
• 24:02collaboration with Illumina where we
• 24:03really thought about this question of,
• 24:05OK,
• 24:05now we have a good sense of what's
• 24:06going on in the micro environment.
• 24:08We have a good sense of what's
• 24:09going on in the underlying
• 24:10genomics and and how does that,
• 24:11how does that relate to the individual tumor.
• 24:13And one of the reasons why clinically
• 24:15that's interesting is 'cause if you
• 24:17look at at at data sets where the the
• 24:19primary tumor's still in place in
• 24:21the with the patient with metastatic
• 24:22disease and they get immunotherapy,
• 24:24often the Mets will respond well,
• 24:25but the primary tumors maybe
• 24:27only shrink modestly, right.
• 24:28So there's not a,
• 24:29there's not that same level of response.
• 24:31And one hypothesis is that,
• 24:32well,
• 24:33it's because the primary tumor is
• 24:35more clonally diverse and the Mets
• 24:36is just a a clone that was able to
• 24:38metastasize out that was selected for.
• 24:40So when you get a response in the
• 24:42Mets maybe it's because there's just
• 24:44a clone that's really responsive,
• 24:45but the primary may only have that
• 24:47clone in in part of it and sort
• 24:48of been our rationalization for
• 24:49continuing to operate on these
• 24:51patients because I tell them well
• 24:52a you know I like operating.
• 24:55But more more fundamentally it's
• 24:57actually because we think that you
• 24:59know we're we're removing the diverse,
• 25:00the biological diversity of them Even
• 25:01if they've had a good response up front,
• 25:03the chance for them to develop persistence
• 25:05down the road may come from from the primary.
• 25:08And so we tried to look at this with
• 25:10multi regional sequencing again
• 25:12relatively modest numbers but we
• 25:14we utilized the combinations of DNA
• 25:16and RNA and and TCR and different
• 25:18things within within tumors that
• 25:20had been exposed to
• 25:22immunotherapies as part of a
• 25:24trial that we ran and and others.
• 25:26And so we were able to look at the
• 25:28question of whether overall immune
• 25:30diversity is associated with,
• 25:32I'm sorry, overall genetic diversity
• 25:34is associated with particular micro
• 25:36micro environmental phenotypes.
• 25:37Indeed, we found at least in this
• 25:39study that if you were a very high lead
• 25:41diverse tumor from a genomic sample,
• 25:43you were more likely to be
• 25:44a myeloid high tumor,
• 25:45which was interesting and and vice
• 25:48versa with respect to some of the
• 25:51antigen presenting machinery genes.
• 25:52So the ITH tumors were actually
• 25:55lower with respect to the APM genes
• 25:57and actually if you took a specific
• 25:59tumor and you actually marked out
• 26:02the immune infiltration patterns,
• 26:04you could start seeing evolution
• 26:05within that same tumor.
• 26:06So this is an example of a of
• 26:08a tumor that was I TH high.
• 26:09It had a lot of intratumoral heterogeneity.
• 26:11It was AP Bear Monsanti 2 kind of
• 26:14micro environment or evolutionary
• 26:16subtype and we were able to look
• 26:18at individually in different
• 26:19regions of this tumor to see.
• 26:20We found that some of the regions
• 26:22were very T cell infiltrated at
• 26:23least by RNA and some of them
• 26:25were very mild and infiltrated.
• 26:26And we were actually able to
• 26:27track down like what were the
• 26:28individual genetic events that were
• 26:30occurring as this tumor developed.
• 26:31And you could see that, you know,
• 26:32as the tumor developed,
• 26:34there was loss of HLA and maybe some
• 26:36CDK into A&B loss which has been
• 26:38associated with a more immune desert
• 26:41or less immune infiltrated micro.
• 26:43So within the same tumor itself,
• 26:44you could see this evolution and
• 26:46that was correlating with the micro
• 26:48environmental features suggesting
• 26:49that there's this constant interplay
• 26:50And I think David has shown this and
• 26:52others have suggested this constant
• 26:54interplay between the underlying Genoma
• 26:56architecture of a tumor and what's actually,
• 26:58you know,
• 26:59underlying the response micro
• 27:00environmentally in that tumor.
• 27:01Obviously we don't fully tease
• 27:03out the mechanism here at all,
• 27:04but it begs the question that there's
• 27:07an opportunity here to to explore this
• 27:10and what about in in the metastatic question.
• 27:11So that was another
• 27:12question we had in the lab.
• 27:14So I've showed you everything in terms
• 27:15of treatment response potentially.
• 27:17But we also wanted to know are are
• 27:19these micro environmental groups
• 27:21also predicting or or associating
• 27:23with development of metastas,
• 27:24which is a different question,
• 27:25right.
• 27:26You know you could have a micro
• 27:27environment that's really
• 27:28important for treatment response,
• 27:29but it may not be associated
• 27:31with metastatic development.
• 27:32So,
• 27:33so David had had somewhat hinted at
• 27:36this with his work with with Ellie
• 27:38and Tony and others at Dana Farber
• 27:41and they showed it you know very
• 27:43elegantly in this paper that looked
• 27:45our paper focused on the advanced
• 27:47disease and and Ibidevo treated.
• 27:49But David's work was performing single
• 27:52cell sequencing on early locally
• 27:54advanced in metastatic tumors and
• 27:56at least in this work he showed this
• 27:58evidence of T cell exhaustion but
• 28:00also this shift in the macrophage
• 28:01polarity as tumors became more
• 28:03aggressive, more metastatic.
• 28:04So suggesting to us and others that
• 28:06you know maybe some of these Tams and
• 28:09and myeloid populations that were so,
• 28:11so, so driving responses are also
• 28:14associated with metastatic development.
• 28:16And so for this we again utilize
• 28:19our strategy with with going
• 28:21to clinical trials and and we
• 28:23worked with this adjuvant study.
• 28:25So this was one of the series of
• 28:27negative studies unfortunately,
• 28:28but again the benefit of having a lot
• 28:30of genomic data looking at whether
• 28:32giving it a VEGF inhibitor monotherapy.
• 28:34Again Pezopinib in this case was
• 28:36associated with better outcomes
• 28:37in patients with high risk non
• 28:39metastatic kidney cancer.
• 28:39All these patients in this trial
• 28:41had advanced kidney cancers.
• 28:42They had a high risk of relapse but
• 28:47but you know standard at the time
• 28:48was just to observe them and so there
• 28:50was a series of trials to see if you
• 28:52gave a VEGF inhibitor whether that
• 28:54actually was improved their survival.
• 28:57The vast majority of the studies were
• 28:59were -1 was sort of positive but no
• 29:01one has really adopted it and now
• 29:03we've moved on to immunotherapy but at
• 29:05this time this was a very interesting study.
• 29:07So we we compared we had microarray
• 29:09yet again from Novartis,
• 29:10we compared the the again all high risk.
• 29:13So they're they're you're sort
• 29:14of controlling for the potential
• 29:16tumor confounding features right.
• 29:17They're all high risk patients and
• 29:18we compared the ones that relapsed
• 29:20versus the ones that didn't.
• 29:21This is work that one of our one of
• 29:23our fellows LED and who who's now at
• 29:26Rochester with a surgeon scientist
• 29:28track their great guy Phil Rippolt
• 29:30with with Lynn Von from my lab who's
• 29:33a senior member now and we compared
• 29:35the the tumors that record versus
• 29:36didn't and we used an unbiased you know
• 29:38whole genome approach with with this.
• 29:39And so of course you saw things
• 29:42like EMT and mtor,
• 29:44which made sense to us because
• 29:45those are obviously very known
• 29:47and relevant oncogenic processes
• 29:48that that promote metastases.
• 29:49But we also saw a lot of immune
• 29:52inflammatory genes in particular Illinois
• 29:546 and Jack in Stat 3 kind of stood out
• 29:57to us as being a driver of metastasis.
• 30:00Again,
• 30:00we applied the same single cell
• 30:02strategies what we had done before to
• 30:04see you know what are these myeloid
• 30:06inflammation and Illinois 6 pathways,
• 30:09what are they really converging on?
• 30:10And and and indeed it it showed a
• 30:14real enrichment in some of these tan
• 30:16populations that we had defined a
• 30:18little bit better with the single cell data.
• 30:21And suggesting that if you were a tumor
• 30:23that was myeloid high or adenosine high,
• 30:25similar overlapping signatures,
• 30:27you are more likely to develop metastasis.
• 30:30Again controlling for other features,
• 30:32all of the clinical and pathologic features.
• 30:34These are completely independent programs
• 30:37and you could show that if I mean if
• 30:40you were a AMSK inflammatory signature,
• 30:42which was a gene signature we developed
• 30:44from from the micro RAY data strongly
• 30:46overlapping with the myeloid signature
• 30:48from Genentech and others that you could
• 30:51take all these high risk patients.
• 30:52And really I mean it's pretty amazing
• 30:54to see curves like this separate out.
• 30:56But again all of these patients,
• 30:58this is a myeloid load tumor intermediate
• 31:00and then very high and you could
• 31:02see their survival probability.
• 31:03And then we were able to replicate this
• 31:05in multiple other data sets including
• 31:07from one of our former fellows who's
• 31:09at Moffett now and and again showing
• 31:11that if you were controlling for
• 31:12all these high risk features from
• 31:14a clinical pathologic standpoint,
• 31:16you you could still stratify patients
• 31:17by the risk of recurrence in that
• 31:20and it didn't seem to be associated
• 31:22much with the T cell response.
• 31:24So what was driving metastas is
• 31:26at least in this data set,
• 31:27but again it has been valid in others,
• 31:29was not really AT cell driven process.
• 31:31What was at least from a micro
• 31:34environmental standpoint promoting
• 31:35metastasis was was independent seen.
• 31:37We tried all the different T cell
• 31:38signatures that have been shown.
• 31:40We looked at IHC scores,
• 31:41we had IHC from CD8 infiltration
• 31:44patterns here.
• 31:44We were able to see if they were inflamed
• 31:46or excludeded and we really didn't see me.
• 31:48Maybe there's a signal with the
• 31:50desert that those are the tumors
• 31:52that have no T cells at all,
• 31:53but it wasn't clear at least that was
• 31:55that wasn't the clear driver here.
• 31:57It was really much more of the myeloid
• 31:59and Tam phenotypes and actually the
• 32:01angiogenic tumors that were low
• 32:03were also similarly associated,
• 32:05not quite as clean of a signal,
• 32:06but it's certainly it looks like if
• 32:08you're a low angiogenic tumor you're
• 32:10you're much more likely to recur.
• 32:11So this suggested that these micro
• 32:13environmental features also might
• 32:15be useful for adjuvant strategies
• 32:17and indeed a lot of the work now
• 32:19that's going forward in some of the
• 32:21newer adjuvant trials are factoring
• 32:23in things like these transcriptonic
• 32:25signatures into risk adapted models.
• 32:26And I think you know the future of
• 32:28course would would would incorporate
• 32:30some of these into selecting not only
• 32:32who's going to recur or not but maybe
• 32:34whether you give them a combination
• 32:36strategy or a single agent drug.
• 32:37But how can we test this.
• 32:38So ultimately you know I I show a lot of
• 32:41like nice kind of genomic correlative
• 32:44work and maybe some evolution of how
• 32:46we think about kidney cancer from a
• 32:49micro environmental and genomic standpoint,
• 32:50but really how do we test this.
• 32:51And so this is the challenge that I faced.
• 32:53I was kind of writing all these
• 32:55papers and thinking about this a lot
• 32:56and getting advice from mentors and
• 32:58everyone kept saying well you got to,
• 32:59you got to,
• 33:00you got to find a model that that works.
• 33:02And so you know we didn't have a
• 33:04lot of good models at the time and
• 33:06I'm going to talk about one we've
• 33:07we've developed a
• 33:08second which which I think is
• 33:09maybe even better, but I'm going
• 33:10to talk about the first one today.
• 33:12So. So you know there are some
• 33:15genetic models in kidney cancer.
• 33:17The, the one that was was used
• 33:19really until this time was really
• 33:21the Renka model which is for those
• 33:23of you familiar it spontaneously
• 33:25arose in a valve C mouse which is
• 33:28immunocompetent mouse and it was called
• 33:30the renal cortical Adam carcinoma.
• 33:31Back then we we really had very
• 33:33limited understanding but it has
• 33:35been profiled now and we know
• 33:36that it's not a VHL driven tumor.
• 33:38So VHL as I showed you earlier is is
• 33:40the fundamental event in in clear
• 33:42cell you have to have a VHL mutation
• 33:43to be a clear cell really and and so,
• 33:48so it was used forever.
• 33:50GEM models of course we know the
• 33:52genetics so why can't we just use
• 33:54gems and there are there are many
• 33:55many GEM models you can probably
• 33:57find six or seven out there.
• 33:59They're often from mixed mixed backgrounds.
• 34:01They have a very long tumor initiation time,
• 34:04very low lower tumor petitrins
• 34:06compared to other gems and they're
• 34:09very hard to transplant and often
• 34:10they develop cystic renal failure
• 34:12because when you knock out even in
• 34:14a KSP specific or Cree specific
• 34:17kidney specific fashion you will,
• 34:19you'll often develop cystic renal
• 34:20failure which is what you see in in
• 34:22people with hereditary kidney cancers.
• 34:24Many of them especially with
• 34:25VHL will develop many,
• 34:26many cysts in their kidney
• 34:27in addition to tumors.
• 34:28So,
• 34:29so it's hard to use those models.
• 34:31So we teamed up with Scott Lowe
• 34:33together and Scott Lowe is for those
• 34:36of you not familiar with him is very,
• 34:38very compass scientists at Memorial
• 34:39and does a lot of mouse engineering
• 34:42beautiful ways and we used an
• 34:43electroporation strategy at the time
• 34:45which would allowed us to deliver guides.
• 34:47We focused on a actually interesting
• 34:49combination of genes which are not
• 34:51super common in kidney cancer,
• 34:52but they define and I'll show you
• 34:54in a minute some of the myeloid
• 34:55phenotypes which is one of the
• 34:57reasons why we focus on it is there
• 34:59also happen to be a very nice gem
• 35:00from Ian Frus group in in Germany.
• 35:02It's time that utilize VHRB and
• 35:05P53 and showed very nicely a gem
• 35:07that which we had been using in the
• 35:09lab for many years and I'll show
• 35:10you some of that work in a minute.
• 35:12But we had utilized this strategy
• 35:14because a because Scott had utilizing
• 35:16P53 in a lot of different tumour
• 35:18models and he had very good guides
• 35:20for and very good strategies,
• 35:21but also because of the genetic
• 35:23combination and also because of
• 35:25the myeloid phenotypes.
• 35:26This was sort of our strategy at
• 35:28the time and this was not trivial
• 35:30because we had VHL as the backbone
• 35:32and that that that makes cells
• 35:34very tricky to add additional guys.
• 35:36For some reason when when you
• 35:38when you knockout VHL
• 35:39in vitro the cells don't
• 35:41tolerate it very well.
• 35:42They they senesce they they.
• 35:44And so this was about a year and
• 35:46a half of work that Lynn did.
• 35:48And eventually though we were able
• 35:49to develop a tumor that we could
• 35:51transplant and we were able to
• 35:53show transcriptomically that this
• 35:54matched into that myeloid high
• 35:56group that that Bob Moitzer and
• 35:58others from Genentech had shown
• 35:59to be critical for that cluster.
• 36:01The the, the very aggressive one
• 36:03that seems to be resistant to to
• 36:05vegif and I can show you here
• 36:07you know this was that cluster
• 36:08here it's P53 enriched 30%.
• 36:10Again P53 is not commonly seen
• 36:12in localized kidney cancer,
• 36:14but if you look at metastatic
• 36:16cohorts it's about between 10
• 36:17and 30% of those will have them.
• 36:19And so this is really the
• 36:21stromal proliferative cluster.
• 36:22When you do flow cytology analysis on it,
• 36:27they they're very macrophage and rich
• 36:29tumors and they have AP 53 program.
• 36:32When you look at transcriptomics,
• 36:33it's very similar to that group.
• 36:35And as I mentioned there was this
• 36:36adenosine signature that we saw which
• 36:38overlap with the myeloid sector.
• 36:39This,
• 36:40this actually came from this paper that
• 36:42was published in from UCSF from Fong ET al.
• 36:46In combination with with Corvis
• 36:48which is a biotech company.
• 36:50And they had developed an adenosine
• 36:532 receptor blockade therapy for
• 36:56patients with metastatic kidney
• 36:58cancer that were treatment refractory
• 36:59and they had developed a signature
• 37:01which which was very much overlapping
• 37:02with this myeloid signature.
• 37:04So this gave us the thought of OK,
• 37:05well we showed that this myeloid program is
• 37:08so important for metastasis development.
• 37:10Maybe if we targeted this adenosine
• 37:12pathway this could abrogate metastasis.
• 37:14And So what Lynn did in her model
• 37:17when she developed it was was
• 37:19stored sort of started testing
• 37:21CP1444 is this adenosine inhibitor.
• 37:22And we were able to show that you
• 37:24get this dramatic abrogation of
• 37:25metastasis doesn't fully control it,
• 37:27but the the number of Mets and the
• 37:30development of Mets is abrogated
• 37:32quite dramatically using a myeloid
• 37:33depletion strategy or a specific
• 37:35myeloid depletion strategy.
• 37:36It doesn't deplete all Tams,
• 37:37but it does shift the phenotype of
• 37:39some of the Tams quite dramatically
• 37:41suggesting perhaps that this could be
• 37:43a strategy and utilizing a mouse model.
• 37:46In the background of all this,
• 37:47we've also you know really been
• 37:50thinking about how to utilize the
• 37:53micro environment to study resistance.
• 37:56And so again we had to go back to
• 37:58a mouse model and I I showed this
• 38:00engineering model which we developed.
• 38:01But we're in the background of all this.
• 38:02For many years we had been utilizing
• 38:04this gem and we we utilize this
• 38:07gem from Ian Frue ET all this.
• 38:09Again this was a KSP, sorry Cree,
• 38:12ERT 2 KSP 1 Flocks mouse that had lost VHL,
• 38:17PT3 and RB.
• 38:18They develop pretty nice clear cell
• 38:21tumors and we utilize this model to to
• 38:23actually study some of these questions.
• 38:26We want to understand what's
• 38:28happening with both PD1 and VEGF
• 38:31therapies alone and in combination.
• 38:33Again I showed you the the real
• 38:35the relevance of this from a
• 38:37predicting response standpoint.
• 38:38And so we first looked at these
• 38:40tumors genomic this is this is
• 38:43unpublished data but hopefully will
• 38:45be submitted in the next few months.
• 38:47So, so we we started looking at these
• 38:49KVPR tumors that had from this Ian
• 38:51fruit model again validating the fact
• 38:52that they are really representative
• 38:54of this myeloid high tumor that I had
• 38:57shown you earlier from from Bob's work.
• 38:58This is again showing RNA sequencing
• 39:00from from from many tumors that we
• 39:02have from these mice and that they
• 39:04overlap very nicely with the the myeloid
• 39:06high PV 3 driven tumors in in human.
• 39:11And we started treating these mice
• 39:13and really focusing on a combination
• 39:14strategy which I think is near to dear
• 39:17Harriet from her from work in Melanoma.
• 39:19But we we utilized linvanib and and PD
• 39:22one in combination for a few reasons.
• 39:24One we were very interested in levanim
• 39:25and PD one comma that that has the
• 39:27highest overall response rate.
• 39:28If you look at the clinical trials,
• 39:30it's about 75% of patients will have
• 39:33a first line response which is really,
• 39:34really incredible and and also we
• 39:39know that Lenva has potentially a
• 39:41lot of micro environmental targets
• 39:43beyond just veg F So so we're very
• 39:46interested in this question and we
• 39:47utilized a sort of a mouse clinical
• 39:49trial from this work.
• 39:50We also included though ACSF 1 inhibitor
• 39:54BLZ 945 to see if if we just broadly
• 39:56depleting Tam's would be helpful.
• 39:58And I should note that CSF one and
• 40:00our inhibitors have been have been
• 40:02a DUD in the clinic.
• 40:03Primarily because they they tend
• 40:05to deplete lots of Tams and Tams
• 40:06can be good and they can be bad.
• 40:08So we don't really we we weren't
• 40:10really didn't really know what to
• 40:11expect here and I'll just show some
• 40:13of that data and we did single
• 40:14cell on pretty much all of these
• 40:17mice that we developed tumors from
• 40:19in different categories.
• 40:20And we and we this to this,
• 40:21this model actually is quite
• 40:23sensitive to linvatinib and actually
• 40:25the combination is is pretty
• 40:26dramatically responsive here.
• 40:27But they don't respond at all to
• 40:30PD1 and actually CSF ONE inhibitors
• 40:31don't really do anything at all.
• 40:32So we were then also able to take
• 40:36early responders and resistant and and
• 40:38actually start comparing them as well.
• 40:40So we can look at the impacts on
• 40:42the micro environment from these
• 40:44different treatment strategies alone
• 40:45in combination and in resistance and
• 40:48in sensitivity which is which is
• 40:50really I think something you want to do.
• 40:51And we could take single cell data
• 40:53from this same strategy that we
• 40:54applied before and start looking at
• 40:56the differences between responders
• 40:57and non responders between ones that
• 40:59are in combination or or alone and
• 41:02get a sense of what's really driving us.
• 41:04You know one of the interesting
• 41:06things about this single cell data
• 41:07set was that we actually had a
• 41:09lot of neutrophil populations and
• 41:11we don't really know their role.
• 41:12I mean there's been some really nice
• 41:14work from Taha Murgoob and Jed Walchak
• 41:16recently on the on on on neutrophils
• 41:18roles in in immunotherapy strategies.
• 41:22So it's an emergency,
• 41:22but I'm not going to talk about
• 41:23that too much today,
• 41:24but it was a striking finding here
• 41:26and we could further subtype the
• 41:28macrophage clusters within here
• 41:29and understand what's happening.
• 41:31It's a, it's a very Tam dominated tumor
• 41:33type as I mentioned with the P53 and RB.
• 41:37And we can actually further stratify
• 41:39them and to understand what's actually
• 41:41happening in the context of both
• 41:43therapeutic sensitivity and resistance.
• 41:45And you can start to see that certain Tams
• 41:46are associated with response and certain
• 41:48Tams are associated with resistance.
• 41:50So even though if you just deplete all Tams,
• 41:52you would actually lose that effect.
• 41:54But actually if you understood which
• 41:55Tams which tumor associated macrophages
• 41:56are actually associated with response,
• 41:58these Angio hide Tams,
• 41:59Tams that are producing angiogenic genes
• 42:01which maybe have been reflected by some of
• 42:03those Angio bulk RNA sequencing data earlier,
• 42:05they're actually associated with response.
• 42:06Whereas other Tams,
• 42:07maybe those myeloid high Angio
• 42:09hide tumors that don't respond are
• 42:11actually associated with resistance.
• 42:12So now we can start getting further into
• 42:15the phenotypes of these Tams and and
• 42:17the context of treatment strategies.
• 42:18And for the sake of time,
• 42:20I won't talk about the neutrophils,
• 42:21but it is another story and we
• 42:24can actually use neutrophils to
• 42:26associate again further responses.
• 42:27So perhaps there's a a major role for them
• 42:29and I don't have time to talk about it.
• 42:31But then then of course you have
• 42:32to go back to the human right,
• 42:33because I showed you something in mouse.
• 42:34But are there analogous populations
• 42:36in human post treatment, right,
• 42:38Because if you're going to,
• 42:39you can cure lots of mice,
• 42:40but you don't know if if those,
• 42:42if those populations are are
• 42:45relevant to human biology and that's
• 42:47a major challenge for immuno,
• 42:48immuno genomic studies or immunologies
• 42:51immunotherapy related studies because
• 42:53of course the mouse and the human micro
• 42:56environments are can be quite different.
• 42:58So that requires post treatment tissue.
• 43:01So how do you do that?
• 43:01So that's you know some of the beauty
• 43:04about going back and forth in in my group.
• 43:07So this is work that was led by
• 43:09Steven Reese who's graduating from
• 43:11our program this year.
• 43:12He's spent a year with me in the lab
• 43:14and a whole whole including a lot of
• 43:17very talented pathologists and research
• 43:19pathologists and of course Christina
• 43:20Leslie from the Computational Biology.
• 43:23And we and we we took all these patients
• 43:24that we've been operating on over the years.
• 43:26Now we started to define them
• 43:28into categories right of of early
• 43:30response of a complete response,
• 43:32partial response and and no response.
• 43:34These are patients that Pat operates
• 43:36on all the time and and I operate on
• 43:39quite a bit and these are patients
• 43:40that that are post immunotherapy
• 43:42now which is a new new frontier.
• 43:44A lot of our surgery now is now in the
• 43:46in the post IO space that gives you a very,
• 43:48very unique opportunity to actually
• 43:50study what's happening both within
• 43:52and across tumors.
• 43:53And so this this is allows us to
• 43:56assemble cohorts of patients that have
• 43:58been exposed to IO therapy alone to
• 44:00IOTKI therapy and we have some with
• 44:04just TKLO but that's really not done anymore.
• 44:06So,
• 44:07so essentially we can look at
• 44:09responses both defined
• 44:10clinically, radiographically but
• 44:11also pathologically and understand
• 44:13are there what are the populations
• 44:15in human and are they analogous to
• 44:17the mouse of course which is you know
• 44:19something that I you know really want
• 44:21to do what we really want to focus on.
• 44:25And so you can start defining
• 44:26this different ways.
• 44:27This hasn't been,
• 44:28there's not an official Canon
• 44:30here on how to do this.
• 44:31It's sort of been adopted a lot from
• 44:33the Melanoma literature about how
• 44:35to define true pathologic response.
• 44:37A lot of us have looked at you
• 44:38know complete response where
• 44:39there's no residual viable tumor,
• 44:40RVT, residual viable tumor or
• 44:42near complete response.
• 44:43And those patients actually if you
• 44:45if you take their their kidneys out
• 44:47we we showed and and we'll show in in
• 44:48our paper that you know they have really,
• 44:50really durable responses you know many,
• 44:52many years without even off therapy.
• 44:54So that's a it's a good biomarker for
• 44:56how they're going to do down the road.
• 44:57And then you can have these partial
• 44:59responses where they have this in
• 45:00between and you have these non responses
• 45:01where there's really no treatment response.
• 45:03You can see it all within the
• 45:05tumor and you could do single cell
• 45:07sequencing on these cohorts and
• 45:09start to get obviously in the human.
• 45:10You still see a lot more T cells as
• 45:12I showed you earlier from the work
• 45:13that we did and what David has done.
• 45:15But you can see these tan populations
• 45:17here and then you can overlay work that.
• 45:19Andrew Corners,
• 45:20who's an MD medical oncology fellow
• 45:23working with Ming Lee has done
• 45:26a lot of this work now.
• 45:27And we can start seeing what are
• 45:29the differences between the IO only
• 45:31and the IOTKI and the untreated
• 45:32populations in terms of the single cell,
• 45:34again post treatment populations
• 45:36and we can start focusing on some
• 45:39of these same populations that we
• 45:41saw that and then we can actually
• 45:44overlay the mouse Tam signatures
• 45:46onto these populations to see other
• 45:48analogous populations and are they
• 45:50associated with resistance and
• 45:51response both within the tumors
• 45:53and across the different regions.
• 45:54And that's sort of where we're focusing now.
• 45:56And so we can further substratify
• 45:58the Tams just like I showed you in
• 46:00the mouse and to see are the TKII
• 46:02iOS really depleting some of these.
• 46:04This is just looking at them broadly
• 46:06without looking at resistance.
• 46:07But you can start seeing that that the
• 46:09the different populations are being
• 46:11affected in different ways by the
• 46:13different treatments in maybe similar ways,
• 46:15but I'm sure different
• 46:16ways as well as the mouse.
• 46:18But hopefully that will help us hone
• 46:19in on what are the most relevant
• 46:21targets to try in the mouse.
• 46:23And you can see this if you focus
• 46:24on one of the M0 signatures from
• 46:25the mouse that I showed you,
• 46:27that Cape, that GEM mouse.
• 46:28You can see that there's clearly
• 46:31differences in terms of the
• 46:33TKIO combination patients
• 46:34and in in the upper tail and the
• 46:36lower tail on this platter actually
• 46:38associating with resistance and response.
• 46:39So you get a sense that maybe
• 46:41these populations are relevant
• 46:43across you know species.
• 46:44So that I think is sort of
• 46:46where we're headed. So overall
• 46:50my conclusions really are that RNA
• 46:52signatures and immune response
• 46:53are really the the useful ones.
• 46:54Clinically I showed you sort of
• 46:56the genetic recap of kidney cancers
• 46:58and how it might relate to some
• 47:00of these microviomal feature.
• 47:01But that's really what we have from
• 47:03a from a predictive and prognostic
• 47:06standpoint and maybe it'll help us
• 47:08select adjuvant treatment strategies
• 47:09down the road for patients,
• 47:11certainly pick the high risk patients
• 47:13a little bit better perhaps the the
• 47:16phenotype seems to be enriched in
• 47:17the in the map in the metastatic
• 47:19setting and particularly post IO.
• 47:22And and maybe this this cross
• 47:24analysis will allow us to prioritize
• 47:26targets to test pre clinically and
• 47:28then hopefully bring them out to
• 47:30the to the clinic now that more
• 47:31and more companies are interested
• 47:33in in targeting tan populations
• 47:35with different inhibitors.
• 47:37And I want to obviously thank my funding
• 47:39and of course, members of my lab,
• 47:41Ming Lee's lab, the urology department,
• 47:43Christina Leslie from computational biology
• 47:45and all the medical colleges I work with,
• 47:47particularly Doctor Mozer,
• 47:48who's been a wonderful mentor to me for many,
• 47:50many years.
• 47:52Thank you so much.
• 47:53And I'll have you answer any questions.
• 48:09Thanks, Ari.
• 48:09That was a real Tour de force.
• 48:12I have a question about the
• 48:14complexity of your clustering.
• 48:15So I I noted that you had 21 clusters of
• 48:19myeloid cells in one of your figures,
• 48:22I believe it was one of the mouse figures.
• 48:25Yeah. Well so that's sort of the art and the
• 48:30the dark art of of single cell sequencing.
• 48:32You could, you can cluster any
• 48:34way you want and you can set your
• 48:36parameters quite differently. So yeah,
• 48:38and this is all all my Lloyd populations,
• 48:42you're absolutely right.
• 48:42So one of the things we do then of course is,
• 48:45is then go back with my immunology
• 48:48colleagues and actually start
• 48:49to think about what are the,
• 48:50what are what are really representing
• 48:52unique populations versus just
• 48:54slicing and dicing single cell data
• 48:56in more and more complex ways.
• 48:58And we try to validate them by
• 49:01flow and to look at really the
• 49:02the dominant populations there.
• 49:03So it's just that this is just
• 49:05sort of an early iteration of of
• 49:08what would be real clustering.
• 49:09No, I get it and it's,
• 49:10it is really complicated
• 49:12before treatment on treatment.
• 49:13But my other question is spatially
• 49:14are some of the clusters uniquely
• 49:16positioned in a certain area of the
• 49:18large tumours that's in humans,
• 49:20I guess is where I'm interested.
• 49:21Yeah, yeah.
• 49:21So,
• 49:22so in that same cohort that I
• 49:23showed you that we're doing,
• 49:25we're working with Heartland Jackson
• 49:28who's at who's in Toronto who's
• 49:31spatial mass cytometry kind of person.
• 49:33We developed from the single cell
• 49:36data a series of of populations
• 49:38really relying on most of the human.
• 49:40So we we took a conglomerate of
• 49:42the different single cell studies
• 49:43that David has done and others have
• 49:45done and kind of come up with like
• 49:47a meta analysis of what are the
• 49:49key markers of the different Tam
• 49:51populations to reduce it down to
• 49:53maybe five or six that might you know,
• 49:56you tag a Tam by you know CD 68 or
• 49:58something else and then you can add
• 50:00a few additional markers and then
• 50:01look at the spatial orientation
• 50:03in these contexts.
• 50:04So we're we're taking all these
• 50:06regions both within tumors and
• 50:08across tumors and and reducing it
• 50:10down to probably a core.
• 50:11And I think ultimately from
• 50:12a biomarker standpoint,
• 50:13you want to kind of just be able to
• 50:15choose a particular Tam or particular
• 50:17T cell that would be relevant and
• 50:18just reduce it to a couple quick
• 50:20stains that a pathologist could
• 50:22hopefully do as opposed to have to do
• 50:24fancy and very expensive sequencing.
• 50:26So absolutely thinking about the same
• 50:28same questions that you bring up.
• 50:30Thank you.
• 50:31I guess I haven't
• 50:35not. I got an unrelated 1
• 50:37unrelated the mouse cell line.
• 50:39So thank you for sending us and sharing that.
• 50:41Of course, the cell line with us.
• 50:43You're planning on making additional ones
• 50:44with different genetic proteins. Yeah.
• 50:46So that's real community service, yes.
• 50:48Yeah. So, so we, yeah, we are doing,
• 50:50we have a VHLBA P1 CD can to be,
• 50:52which is a more common combination and
• 50:55that one is a sarcomatoid tumor perfectly
• 50:58responds well to CTLA 4 very nicely.
• 51:03So that one yeah we'll be hopefully
• 51:06that's that papers you know we're
• 51:08finishing up this that work but
• 51:09that I think that will be something
• 51:11that people find more exciting just
• 51:13because of the common genetics.
• 51:15In fact when I presented this one
• 51:17initially Bill very Bill Kalin very,
• 51:19very astutely said you know that's
• 51:21not a very common genetic study.
• 51:23I'm like yeah but that's it's a
• 51:25common one for the for the bad
• 51:27tumors that don't respond well.
• 51:28So so this one is is a much more
• 51:31common genetic subtype and I it's a
• 51:34challenge of doing anything engineering.
• 51:35We tried of course all the the more
• 51:38common mutations as has Bill and
• 51:40others you know Crisp bring out PBR
• 51:43and what the tumors just don't grow
• 51:45well and they're very hard so the the
• 51:47nice clear cells are hard to engineer.
• 51:50The bad ones that don't look super
• 51:52clear cell but have they retain the CA
• 51:559 and hip one at least don't don't look
• 51:57you know those are the ones that grow.
• 51:59It's it's a challenge of any
• 52:01syngeneic system.
• 52:02So that's why you know you can rely on
• 52:04gems but gems are just hard to to treat.
• 52:06So limitation of the field
• 52:11sure.
• 52:15Thank you. So so really great work in
• 52:17terms of single cell transcriptomics and
• 52:20even profiling and site off and such.
• 52:22But ultimately the biomarker should be
• 52:25translated into clinic and should be
• 52:27easily performed and reputable and cheap.
• 52:29So how do you envision translating these,
• 52:32yeah, into the clinic? That's great.
• 52:34So a couple couple ways.
• 52:35And I think we're thinking about
• 52:36this a few different ways.
• 52:37So one of course is where obviously
• 52:41reducing it to a few markers that might
• 52:43stand for the most relevant populations
• 52:45and maybe it's a combination of Tams,
• 52:47maybe some neutrophils and some CDA
• 52:49populations that might ultimately come
• 52:51up with a very straightforward IHC panel.
• 52:53The other thing of course is that what
• 52:55a lot of people are thinking about
• 52:57is digital pathology and sort of AI.
• 52:59So if you can define groups of tumors
• 53:02transcriptionally and then you you
• 53:04put it into some model where you have
• 53:06the scan slide scanned in and and put
• 53:08through a machine learning platform.
• 53:10You could maybe even digitally say
• 53:11this is this tumor has this feature
• 53:13even though the pathologist has
• 53:15no idea what they're seeing,
• 53:16but the the model does.
• 53:18And so for that,
• 53:18you know and I know a lot of
• 53:20people are working on this,
• 53:20but we you know for that same
• 53:22Novartis study where I showed you,
• 53:23we showed that the myeloid phenotype
• 53:25is associated with recurrence.
• 53:27We're working with group at Dartmouth
• 53:28that has a machine learning model.
• 53:30We have all the transcriptomic,
• 53:31We have the the slides sent from Novartis
• 53:33which is like 12 terabytes of data.
• 53:35They high,
• 53:36high resolution scanned all the slides
• 53:38from that trial and we've given them
• 53:40the micro environmental subgroups
• 53:41and strategies and they're trying to
• 53:43figure out if they can do that and
• 53:44they have already done it on the TCGS.
• 53:46So you can kind of replicate it because
• 53:48that that might be a way to to say,
• 53:49OK,
• 53:50now you've put it through an AI
• 53:52system and they say this tumor
• 53:54is this thing and this patient's
• 53:56going to recur much more or this
• 53:57patient might respond better.
• 53:58So that that's that's a strategy that I
• 54:00think a lot of us are thinking about.
• 54:02Great question for the
• 54:08community service and making
• 54:09these mouse models and thank you
• 54:11for sharing with us as well.
• 54:12I'm curious from the immuno
• 54:15oncology metabolism world,
• 54:17you know we talked a lot about the obesity
• 54:19paradox in Melanoma and lung cancer.
• 54:21Do patients with obesity respond
• 54:23better to immunotherapy.
• 54:25So I'm wondering if you know knowing that
• 54:28RCC actually is associated with obesity.
• 54:30I'm I'm wondering if you can speak
• 54:32to your mouse models if if you've
• 54:34observed any potential difference
• 54:35in the response to immunotherapy
• 54:37in in these models because it in
• 54:39mice with obesity and if not maybe
• 54:41we can collaborate that. Yeah.
• 54:42So. So it's a great question and
• 54:45something that's very near and dear.
• 54:47So.
• 54:47So I I do have funding through the DoD
• 54:49to look at obesity in kidney cancer
• 54:51and these models and we've we've
• 54:53been utilizing the transplantation
• 54:55models we so we do we did the GEM
• 54:58model first we we did fat feed them
• 55:00they're they're hard to to feed
• 55:02because of the mixed background.
• 55:04So they they gain weight not as nicely
• 55:06as if they were a clean genotype but
• 55:08we do observe earlier onset tumors
• 55:10in those in those mice but then so
• 55:12genetically when we implant them and
• 55:14that's not trivial by the way if
• 55:16you're going to do us an orthotopic
• 55:18transplantation model in a fat
• 55:20mouse because just like humans they
• 55:21develop a lot of perinephric fat.
• 55:23So if you try to open up the mouse and
• 55:25inject it it's like a **** show part
• 55:27of my French but but but essentially
• 55:30it's very challenging so so we've
• 55:32been doing but we do see that the
• 55:34tumors grow faster in obese which
• 55:36sort of makes sense because we know
• 55:39that obesity associated with with
• 55:41better development of kidney cancers
• 55:43but in it does suggest in human at
• 55:46least they seem to be less aggressive.
• 55:48So we're we're now trying to understand
• 55:50immunologically what's going on but
• 55:52I would I think we're talking soon
• 55:53so I'm happy to talk more about that
• 55:55Rachel but I think it would be a
• 55:57really it's a really cool area and
• 55:59we're we're we're focused on on Trem
• 56:012 macrophages which has been shown to
• 56:03be associated with lipid their the
• 56:05lipid associated macrophages and and
• 56:06it's associated with more aggressive tumors.
• 56:08So we can talk more about that,
• 56:10but definitely something that we're,
• 56:11we're thinking a lot about.
• 56:17All right.