Skip to Main Content

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

March 15, 2024

Yale Cancer Center Grand Rounds | March 15, 2024

ID
11479

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.