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The Determinants of Effective Anti-Tumor Immunity in Kidney Cancer

December 14, 2022

Yale Cancer Center Grand Rounds | December 13, 2022

Presented by: Dr. David Braun

ID
9280

Transcript

  • 00:00And so it's my great pleasure.
  • 00:02I'm Marcus Bosenberg,
  • 00:03I'm one of the Co leaders of the cancer
  • 00:06immunology program and we're actually
  • 00:08have our full House of a program
  • 00:11Co leaders in person as we speak.
  • 00:15So today's grand rounds speaker will
  • 00:17be David Braun who's an assistant
  • 00:19professor in the Department of Medicine
  • 00:21and also has appointments in pathology
  • 00:23and urology and is a Lewis Goodman
  • 00:25and Alfred Gilman, Yale scholar.
  • 00:27So he is also a member of.
  • 00:31The Center for Molecular and Cellular
  • 00:33Oncology that Marcus Musham leads at
  • 00:36the Yellow Cancer Center and received
  • 00:38his PhD in computational biology at
  • 00:41NYU and his MD at Mount Sinai and was
  • 00:44a resident in Boston and a fellow at
  • 00:47Dana Farber prior to coming here.
  • 00:50And we're very glad he's come to Yale.
  • 00:52So David, as you will see,
  • 00:54is covering a remarkably broad set of
  • 00:57things related to cancer immunology,
  • 00:59really focused initially on renal
  • 01:01cell carcinoma.
  • 01:02But you know,
  • 01:03we'll see where that goes and
  • 01:04we're super happy to have him here
  • 01:06and have them associated with the
  • 01:08cancer analogy program.
  • 01:09And David, without further ado,
  • 01:11we'll have to start.
  • 01:13That's perfect.
  • 01:17Thank you so much for the incredibly
  • 01:19kind introduction and for that the
  • 01:20chance to speak to you all here today
  • 01:22and all the people on zoom as well.
  • 01:24And So what I'm going to talk about today
  • 01:26is some of the determinants of effective
  • 01:28antitumor immunity in kidney cancer.
  • 01:29And as Marcus mentioned,
  • 01:30a lot of this is relevant specifically
  • 01:32for kidney cancer and I see patients
  • 01:34with kidney cancer at smilow.
  • 01:35And so it's relevant for these,
  • 01:37it's relevant for these
  • 01:39patients specifically.
  • 01:40But I hope as well that this could be used
  • 01:42as a as a broader model of human tumor
  • 01:44immunology that we might be able to learn.
  • 01:46Uh, learn particularly neurologic
  • 01:47mechanisms that might be applicable
  • 01:50to other cancer types as well.
  • 01:52Here my disclosure is not relevant
  • 01:54for today's talk and so I usually
  • 01:56like to start off with just a couple
  • 01:57of patients just to highlight the
  • 01:59challenges within kidney cancer.
  • 02:00This is going to be true for a lot
  • 02:02of solid tumors and particularly in
  • 02:04the immune therapy era.
  • 02:05So the first patient,
  • 02:06these are two of my patients
  • 02:07from the last couple of years.
  • 02:08The first patient was a
  • 02:0968 year old gentleman,
  • 02:10had a fairly common age for kidney cancer,
  • 02:12had metastatic clear cell kidney
  • 02:15cancer that's the most common.
  • 02:17Thank you.
  • 02:18The most common histologic diagnosis of
  • 02:21kidney cancer and had pretty widespread
  • 02:23metastatic disease throughout the lungs.
  • 02:25Lungs actually had a brain metastasis
  • 02:27and so received a standard first
  • 02:29line combination of checkpoint
  • 02:30inhibitors and the volume animac.
  • 02:32And for those that don't
  • 02:33look at CC's every day,
  • 02:34the primary tumor is outlined in red and
  • 02:36we can see compared to prior to therapy,
  • 02:39there's a tremendous shrinkage
  • 02:40of that primary.
  • 02:41There's basically resolution
  • 02:42of all metastatic disease.
  • 02:43The the primary,
  • 02:44the residual primary was resected.
  • 02:46And so this patient is free of disease,
  • 02:48potentially cured of their disease has
  • 02:51now been off therapy for over a year.
  • 02:53That's in sharp contrast
  • 02:54to a very similar patient,
  • 02:56very similar demographic received
  • 02:58the exact same therapy for the
  • 03:00exact histologic diagnosis.
  • 03:01But unfortunately the tumors
  • 03:02did not respond to therapy.
  • 03:04They all grew and despite this
  • 03:05and subsequent lines of therapy,
  • 03:07the patient passed away within
  • 03:09eight months of diagnosis.
  • 03:10And so it's it's cases like these,
  • 03:12these extreme response phenotypes,
  • 03:13the sort of promise of long standing
  • 03:16durable responses and resistance
  • 03:17that sort of drives the questions
  • 03:19in in my lab and there's some
  • 03:22peculiarities to kidney cancer.
  • 03:23That on the scientific front has also
  • 03:24been really interesting and fascinating.
  • 03:26So historically we think of CDA T cell
  • 03:29infiltration as being a positive thing,
  • 03:32positive prognostic thing.
  • 03:33They're main factor self
  • 03:34or anti tumor immunity.
  • 03:36So having a lot of them in the
  • 03:37tumor is positive though historical
  • 03:39exception is really kidney cancer
  • 03:41where over time of the past 20
  • 03:43years or so having a high degree
  • 03:45of CD T cell infiltration has been
  • 03:48associated with a worse prognosis
  • 03:49really in contrast to just about every
  • 03:52other solid tumor type.
  • 03:54Further when we think of what our
  • 03:55tumor types that typically responsive
  • 03:57to new checkpoint inhibitors,
  • 03:58we think of tumor types
  • 03:59that are on the far end,
  • 04:01the right end of this mutation
  • 04:02spectrum that makes a lot of sense.
  • 04:04We have tumors that have high
  • 04:07mutation burdens, lots of neoantigens,
  • 04:08so lots of potential antigenic
  • 04:10targets for the immune system.
  • 04:12They're potentially more likely to respond.
  • 04:14So Melanoma, non small cell lung cancer,
  • 04:16urothelial cancer,
  • 04:17MSI colon cancer and then we see right
  • 04:19in the middle is clear cell kidney
  • 04:21cancer with a modest mutation burden.
  • 04:23Pretty similar to glioblastoma or
  • 04:25pancreatic cancer, ovarian cancer.
  • 04:27Yet both historically and contemporarily,
  • 04:29it's responsive to immune therapy.
  • 04:31And so my hope is we might be able
  • 04:32to learn a little bit about why,
  • 04:34if we can figure out why this
  • 04:35is responsive to immunotherapy,
  • 04:37maybe we can apply those lessons elsewhere.
  • 04:38And then also for those patients who
  • 04:40aren't lucky to benefit from immune therapy,
  • 04:43the current forms of immune therapy,
  • 04:44can we understand mechanisms of
  • 04:46resistance that can guide rational
  • 04:48combinations of future therapies and so
  • 04:51the framework that our lab really uses to.
  • 04:54Answer these questions,
  • 04:55what are the infiltrating immune cells
  • 04:56and what are the antigenic targets
  • 04:58in kidney cancer is a pretty simple
  • 05:00framework and I've sort of outlined
  • 05:02it here where we have a tumor cell,
  • 05:04the kidney cancer cell,
  • 05:05yeah,
  • 05:05interacting with an infiltrating T cell
  • 05:07and that takes place in the context
  • 05:10of a heterogeneous microenvironment.
  • 05:11And so with this really basic
  • 05:14sort of worldview,
  • 05:15we can begin to ask focus questions
  • 05:17and these are the really the
  • 05:19questions that end up guiding a
  • 05:20lot of the projects in our lab.
  • 05:22So the first is what are the genetic
  • 05:24alterations in kidney cancer,
  • 05:25how do they potentially impact immune
  • 05:27infiltration into the tumor and
  • 05:29ultimately how do those intersect or
  • 05:31interplay to impact therapeutic response.
  • 05:33What are those other immune cells
  • 05:35with immune cells within the tumor
  • 05:37microenvironment? How do they?
  • 05:40Interact with T cells and impact
  • 05:43cell phenotype.
  • 05:44And finally when everything goes
  • 05:45right and T cells are capable CD,
  • 05:47T cells are capable of recognizing
  • 05:49the tumor and eliminating it.
  • 05:51What is it that it's recognizing.
  • 05:52And so we know that at the heart of
  • 05:54this interaction of antigen specific
  • 05:56immunity is the tumor cells presenting
  • 05:59antigenic peptides and MHC Class 1
  • 06:01molecules being recognized by the
  • 06:03cognate T cell receptor and for for
  • 06:05this for kidney cancer and for most
  • 06:07of their I would say solid tumors,
  • 06:09we don't actually know what
  • 06:11those antigens are.
  • 06:12We know sometimes for high
  • 06:13mutation burden tumors that.
  • 06:14Can be classic in the antigens,
  • 06:16but for things that are
  • 06:17modest mutation burdens,
  • 06:18tumors like kidney cancer,
  • 06:20it's much less clear.
  • 06:21And so these are the sort of three
  • 06:23fundamental areas that the lab is
  • 06:25currently working on and we'll
  • 06:26kind of step through each one,
  • 06:27maybe talking a little bit about
  • 06:29some prior work over
  • 06:30the last couple of years when I was
  • 06:32back in in Boston and then some
  • 06:33ongoing efforts now in the lab.
  • 06:35So the first is really what are the
  • 06:37mutations in kidney cancer that
  • 06:39might impact immune infiltration
  • 06:41and ultimately response to therapy.
  • 06:43And I would say broadly,
  • 06:44we use a lot of classic genomic techniques,
  • 06:46whole exome sequencing,
  • 06:47RNA sequencing to really approach
  • 06:49these questions.
  • 06:50And so a lot of the motivation
  • 06:51for this came from an early study
  • 06:53just a few years ago from Ellie
  • 06:54van Allen's group at Dana Farber,
  • 06:56where he looked at a small phase one
  • 06:58trial of nivolumab in kidney cancer,
  • 07:00the checkmate O 9 trial and it was
  • 07:02only about 35 patients that had genomic
  • 07:04data available, but for those 35.
  • 07:06Patients asked a pretty simple question,
  • 07:08what are the the mutations that are
  • 07:11recurrent in kidney cancer among those
  • 07:1335 patients and out of those recurrent
  • 07:15mutations and that's on the X axis.
  • 07:17And now those recurrent mutations
  • 07:19which are actually significantly
  • 07:20impacting response to therapy
  • 07:22and that's on the Y axis.
  • 07:23And we can see there's only one loss
  • 07:26of function mutations in the Pfaff
  • 07:28complex member PBR one was associated
  • 07:30with improved response and we see
  • 07:32the bottom also improve survival in
  • 07:34this small cohort of 35 patients.
  • 07:37So really building off of this
  • 07:39initial funding that we were through
  • 07:41partnership with Bristol-Myers able
  • 07:43to sequence not only the phase
  • 07:45one data but the tumors from the
  • 07:47phase two and phase three trials
  • 07:49of nivolumab in kidney cancer.
  • 07:51And looking specifically at the phase
  • 07:53three trial which is the Checkmate O25 trial,
  • 07:55we were able to confirm that yes,
  • 07:57loss of function mutations and BM
  • 07:59one were associated with improved
  • 08:00response and in this case progression
  • 08:02free and overall survival with
  • 08:04nivolumab with immune therapy though
  • 08:05we can see the effect size is fairly.
  • 08:07Modest.
  • 08:08So it's really, you know,
  • 08:09it's something that's there,
  • 08:10but it's certainly not the whole picture.
  • 08:13And so this was a very focused
  • 08:15sort of validation question,
  • 08:16but we really want to look at
  • 08:17this much more comprehensively.
  • 08:19And So what we did was again in
  • 08:21partnership with Bristol-Myers,
  • 08:22we're fortunate to have access
  • 08:23to tumors from the phase one,
  • 08:25phase two and phase three trials
  • 08:27of nivolumab in kidney cancer.
  • 08:29The phase three trial is really
  • 08:31the pivotal trial that led to the
  • 08:33first checkpoint inhibitor approval
  • 08:34within kidney cancer.
  • 08:35And we're lucky to benefit as
  • 08:36well from that phase three trial
  • 08:38also having a control arm and mtor
  • 08:40inhibitor of your elymus.
  • 08:41And so we could see if there's
  • 08:42anything that we find that might be
  • 08:44associated with response or resistance.
  • 08:45Is that something that's specific for
  • 08:47immune therapy or is that something
  • 08:49that's perhaps more prognostic
  • 08:50that we see with old therapies,
  • 08:51including one with an independent
  • 08:53mechanism of action like an mtor inhibitor.
  • 08:55So we performed whole exome sequencing,
  • 08:57RNA sequencing and CD8 immunofluorescence
  • 08:59to really look at the immune
  • 09:01infiltration of the broad immune
  • 09:03phenotype of these tumors.
  • 09:04At the time this was the largest
  • 09:06study of advanced kidney genetic
  • 09:08study of advanced kidney cancer.
  • 09:10The TCG which was really a foundational work
  • 09:12really skews towards earlier stage tumors.
  • 09:14Only around 10 to 15% of the kidney
  • 09:17cancer tumors in TCG are advanced stage.
  • 09:18And so by sequencing this we can really
  • 09:20get an understanding of first the genetic
  • 09:22landscape of advanced kidney cancer and
  • 09:24there's some interesting findings in here,
  • 09:26some enrichment and clinically
  • 09:28unfavorable aggressive mutations
  • 09:29and copy number variants,
  • 09:31things like NF2 mutations, a loss of.
  • 09:34Nine P 21.3,
  • 09:35but really our our primary question
  • 09:37was how does this ultimately
  • 09:39impact response to immune therapy?
  • 09:41And so the first thing we did was look
  • 09:43at some of the classic markers of somatic
  • 09:46alteration burden that is present in
  • 09:48that we associate other tumor types
  • 09:50in other tumor types response with.
  • 09:52So we know that there's a Histology
  • 09:54agnostic approval for the immunotherapy
  • 09:56drug pembrolizumab based purely on
  • 09:58mutation burden and we know that in many
  • 10:00tumor types there's an association with
  • 10:02high mutation burden response to therapy.
  • 10:04And so for kidney cancer,
  • 10:05we looked at total mutation burden,
  • 10:07we inferred,
  • 10:09neoantigen load we inferred.
  • 10:11You want to drive from frameshift insertion,
  • 10:13insertion deletions which create new
  • 10:15open reading frames and no metric
  • 10:17of somatic alteration burden was
  • 10:18at all associated with response.
  • 10:20So again this is in sharp contrast
  • 10:22to Melanoma and non small cell
  • 10:24lung cancer and bladder cancer.
  • 10:25Here mutation burden really does not
  • 10:28associate or predict response to therapy.
  • 10:30When we then looked across each
  • 10:32individual recurrent mutation
  • 10:33and tried to see which one might
  • 10:35be associated with resistance or
  • 10:37response in this much expanded cohort,
  • 10:39again we found only one in
  • 10:41this pooled analysis.
  • 10:42So again it was only PBR on one which
  • 10:44is a very common mutation president
  • 10:45perhaps up to 30 to 40% of of kidney
  • 10:48cancer tumors that was associated with
  • 10:50improved response and overall survival.
  • 10:52And again here we really could
  • 10:53benefit and see that this response,
  • 10:55this impact on response and survival
  • 10:56was unique to the patients tree
  • 10:58with immune therapy and was not
  • 11:00seen in patients treated with the
  • 11:01control arm with an M Tor inhibitor.
  • 11:04So that's uh mutations.
  • 11:06What about the immune landscape and
  • 11:08how that might impact kidney cancer?
  • 11:10We know that prognostically having a lot
  • 11:12of CDT cells might be a negative thing,
  • 11:14but how does that impact response
  • 11:16to immunotherapy?
  • 11:17And so the first thing we did
  • 11:19was characterize these tumors
  • 11:20broadly into 3 immune phenotypes.
  • 11:21And these are types you might be familiar
  • 11:23with the classic immune infiltrated
  • 11:25where there's lots of CDT cells,
  • 11:26immune deserts,
  • 11:27where there's a positive CDT cells
  • 11:29and then in Munich excluded tumors
  • 11:31where there's made perhaps lots of
  • 11:33CDT cells lining up to the tumor.
  • 11:34Urgent but really unable to infiltrate
  • 11:36the tumor center and have a factor
  • 11:38function that's a potential mechanism
  • 11:40resistance and you know other solid
  • 11:41tumor types including a common mechanism
  • 11:43of resistance in bladder cancer.
  • 11:45And so when we looked at
  • 11:47our kidney cancer tumors,
  • 11:48our advanced kidney cancer tumors,
  • 11:49the first thing we observe
  • 11:50was that immune occlusion is
  • 11:52really not a common phenotype.
  • 11:53It's not looking to be a predominant
  • 11:56mechanism of resistance in in kidney cancer.
  • 11:58We see here only about 5% of
  • 12:00these tumors are Munich excluded.
  • 12:02This is in contrast to some like
  • 12:03bladder cancer where up to 50%
  • 12:05of metastatic bladder cancers.
  • 12:06Would have this immune exclusion phenotype.
  • 12:09The other thing we can observe
  • 12:10is by and large these are heavily
  • 12:12CD8 infiltrated tumors.
  • 12:13About 3/4 of these tumors are
  • 12:15highly infiltrated by CDT cells.
  • 12:17But again in contrast to a lot
  • 12:19of other solid tumors,
  • 12:20we're having a lot of CD T cells
  • 12:23might positively impact response.
  • 12:25Here it had no impact on response
  • 12:26and survival and we can see that for
  • 12:29patients I'm showing here treated
  • 12:30with immune therapy that regardless
  • 12:32of whether you had an immune excluded
  • 12:34tumor and infiltrated tumor or desert tumor,
  • 12:36all of those had roughly the same
  • 12:38response to therapy and survival.
  • 12:40And so looking at genetics alone
  • 12:42you know didn't yield much.
  • 12:44Looking at the immune phenotype
  • 12:46alone really doesn't tell us which
  • 12:48tumors are responsive to therapy.
  • 12:50Is there perhaps some interaction
  • 12:52or interplay between them and
  • 12:53so the first thing we did was.
  • 12:55Really,
  • 12:55just look at among the infiltrated
  • 12:57and non infiltrated tumors.
  • 12:59Are there different mutational landscapes?
  • 13:00Are there different driver mutations that
  • 13:02might be present in one or the other?
  • 13:04And the answer was yes.
  • 13:05And here again it was only one that it
  • 13:07was actually the immune desert tumors,
  • 13:09the ones that lack CD infiltration
  • 13:11that were really enriched for these
  • 13:13clinically favorable CDA T cells that
  • 13:15nearly half of those immune desert
  • 13:17tumors had mutations in people on one,
  • 13:19whereas less than 1/4 of the
  • 13:21immune infiltrated ones did.
  • 13:22So my mutations,
  • 13:23we have enrichment of clinically favorable
  • 13:25PR1 mutations within desert tumors.
  • 13:27How about within infiltrated tumors,
  • 13:31particularly looking at copy
  • 13:32number alterations?
  • 13:33And here there was a a different picture.
  • 13:35There was actually a lot more copy
  • 13:37number alterations within these
  • 13:39infiltrated tumors potentially
  • 13:40indicating these might be perhaps
  • 13:42more chromosomally unstable having
  • 13:43a higher copy number burden in
  • 13:45general than non infiltrated tumors.
  • 13:47And so we took a similar approach.
  • 13:50We looked systematically which copy
  • 13:52number alteration was associated
  • 13:53with was increased and infiltrated
  • 13:55tumors and that's on the X axis.
  • 13:58And then out of those infiltrated tumors
  • 13:59which copy number alteration might be
  • 14:01associated with altered response or
  • 14:03survival either positively or negatively.
  • 14:05And that's on the Y axis.
  • 14:06And again only one came out
  • 14:08deletions of nine P 21.3 which
  • 14:11contain genes like CDKN 2A,
  • 14:13CDKN 2B M tap loss of function.
  • 14:15The loss single copy loss of nine
  • 14:18P 21.3 was associated with worse
  • 14:21survival and worse response.
  • 14:23And looking at whether this effect
  • 14:25would really specific to NTP one
  • 14:27treatment or a broad prognostic effect,
  • 14:29we could see that really loss of nine
  • 14:31P 21.3 within these infiltrated tumors
  • 14:33was associated with worse progression.
  • 14:35Green overall survival
  • 14:36really only with anti PD,
  • 14:38one treatment with immune therapy on
  • 14:40the left and not with mtor inhibition
  • 14:42of control arm shown on the right.
  • 14:46So what is it that's impacting this response?
  • 14:48Well, how is 9 P 21.3 actually acting to,
  • 14:52to lessen response to immune therapy?
  • 14:55That remains an open question.
  • 14:56We we took a a first look at
  • 14:58least some activated pathways by
  • 15:00integrating the RAC data and really
  • 15:03looking at which pathways might be
  • 15:05enriched in these nine P 21.3 tumors.
  • 15:07And there's some potentially initial hits.
  • 15:09There's certainly more angiogenesis
  • 15:12and hypoxia on those tumors.
  • 15:14There's definitely more increased
  • 15:16mtor signaling.
  • 15:17And so at least some initial hints
  • 15:18as to how these might be associated.
  • 15:20But there's a lot of mechanistic
  • 15:22work that still needs to be done.
  • 15:23And so the initial model we put
  • 15:26forward for this is that yes,
  • 15:27in theory CD and infiltration should be
  • 15:29associated with better response to PD,
  • 15:31one therapy just like in other tumors.
  • 15:34But what we have here is overlying
  • 15:35the genetics of the tumor,
  • 15:36these non infiltrators.
  • 15:38Infiltrate tumors enriched for loss
  • 15:39of function P brown one mutations,
  • 15:41these clinically favorable mutations
  • 15:43that bring the response rates up and
  • 15:45the infiltrated tumors are enriched
  • 15:47for these clinically unfavorable
  • 15:49deletions of NI P 21.3 and again
  • 15:51dragging the response rates down.
  • 15:53And so this is the work that was done
  • 15:55now published a couple of years ago.
  • 15:57And while at the time this was
  • 15:59a large sequencing effort,
  • 16:00it was about 454 tumors that
  • 16:02underwent whole exome sequencing.
  • 16:03It turns out that that is only enough to
  • 16:06capture you essentially fairly common.
  • 16:08Mutations and kidney cancer that might
  • 16:10be associated with immune infiltration
  • 16:11or response that you actually need much,
  • 16:13much larger numbers to really saturate
  • 16:14and really get a better sense of
  • 16:16the full landscape of of genetic
  • 16:18alterations within kidney cancer.
  • 16:20And so in efforts that we're leading
  • 16:22together with Allie Van Allen's lab,
  • 16:24we've put together in our cohort
  • 16:25of just about 2000 patients that
  • 16:28were treated with immune therapy.
  • 16:30This is from a series of phase three
  • 16:32trials including that checkmate O25 trial,
  • 16:34but other more modern combination
  • 16:36therapies phase three trials of.
  • 16:38Pure immune checkpoint inhibitors like
  • 16:40the volume Applebaum lab or immune
  • 16:43therapy plus antiangiogenic inhibitors.
  • 16:45And the reason we're doing this is sort
  • 16:47of demonstrated by this simulated power
  • 16:49calculation which I've shown here,
  • 16:50which is basically for the number
  • 16:52of patients we have in our cohort,
  • 16:54which is shown on the X axis,
  • 16:56what frequency of mutation are
  • 16:57we actually powered to detect.
  • 16:59So if we look at our original paper
  • 17:01from a couple of years ago now,
  • 17:03we were actually powered to detect
  • 17:04exactly what we found things that are
  • 17:07really quite common in in this case.
  • 17:08Number one,
  • 17:09mutations was present in nearly
  • 17:11half of responsive
  • 17:12patients and a little bit less than
  • 17:131/4 of non responsive patients,
  • 17:15very, very common mutations.
  • 17:16That's all that we were power to detect.
  • 17:19Now that we have a much more substantial
  • 17:21cohort of over 2000 out of which about
  • 17:231500 were treated with immune therapy,
  • 17:25we're now powered to detect a much
  • 17:27broader range of mutations that
  • 17:29might impact response or resistance,
  • 17:31things that might be present
  • 17:32in as low as 5% of responders.
  • 17:34And so really getting a much broader
  • 17:36land idea of the genetic alterations
  • 17:37and how they might be associated with.
  • 17:39Resistance and so our our sort of
  • 17:41questions driving this project are
  • 17:43one just about kidney cancer genetics.
  • 17:45What are the long tail of mutations,
  • 17:47we know the the most common
  • 17:49mutations from TGA,
  • 17:50but what are the long tail of
  • 17:51driver mutations and kidney cancer?
  • 17:53Do those fall within common pathways
  • 17:55that might actually lead us to better
  • 17:57understand kidney cancer biology?
  • 17:58What is the connection between the somatic
  • 18:00alterations and immune infiltration?
  • 18:02We saw some interactions between PT
  • 18:04Barnum one or deletions of nine P 21.3,
  • 18:07but again are there others?
  • 18:08And then finally how do these intersect?
  • 18:10Or interplay to ultimately impact response.
  • 18:14So that's a large ongoing project,
  • 18:16but I think the use of whole exome
  • 18:19sequencing and an RNA sequencing
  • 18:21is really applicable to answer a
  • 18:23number of other focus questions.
  • 18:25And I think as we think about these,
  • 18:27it's also important.
  • 18:28We're really obviously tumor focused,
  • 18:30but also to integrate what's happening
  • 18:32in the host immunity as well,
  • 18:33for instance, soluble circulating,
  • 18:35soluble factors in the plasma or
  • 18:37circulating immune cells as well.
  • 18:39And so just a little bit of a hint
  • 18:41of some of the things that we've
  • 18:43been working on over the past year.
  • 18:45One is a partnership with Random McKay
  • 18:47at sorry that should say UCSD who ran
  • 18:49a phase two trial of a another immune
  • 18:52therapy drug atezolizumab plus bevacizumab.
  • 18:55This was actually non clear cell
  • 18:56kidney cancer less common variants.
  • 18:58And what we could we could see by
  • 19:00looking at circulating factors by plasma
  • 19:02cytokines that is actually a highly
  • 19:05correlated module of inflammatory
  • 19:06cytokines that are present in a
  • 19:09variety of these patients with non
  • 19:11clear cell disease and what we call
  • 19:13the systemic inflammatory module.
  • 19:15This was actually associated with
  • 19:16worse response and worse survival
  • 19:18within these patients.
  • 19:22We've talked mostly about the genetics,
  • 19:23but we know that the RNA sequencing can also
  • 19:25be leveraged to really understand some of
  • 19:27the molecular subtypes of kidney cancer.
  • 19:29There was really nice work done from
  • 19:32the Genentech group and work that was
  • 19:34initially led by Bob Motzer and and
  • 19:36Brian Reaney where they broke down kidney
  • 19:39tumors from a phase three trial and kidney
  • 19:41cancer into different molecular subtypes.
  • 19:43And actually we're able to see
  • 19:45differential response to therapies was
  • 19:47actually predictive of whether a patient
  • 19:49would respond to therapy A or therapy.
  • 19:51Which is a really exciting sort of idea,
  • 19:53biomarker driven selection of of
  • 19:55therapy for patients with kidney cancer.
  • 19:58However that was from patients treated
  • 20:00with drugs that are not FDA approved.
  • 20:02It was overall a negative phase three trial.
  • 20:04And so in work led by Renee Maria
  • 20:06Saliby in my lab we've actually used
  • 20:09a random forest model to now classify
  • 20:11tumors in a FDA approved regiment,
  • 20:14a value map plus axitinib and actually
  • 20:16look at whether these are associated
  • 20:18with response or resistance in a really
  • 20:20FDA approved. Measurement really.
  • 20:21Can you use this for for treatment selection?
  • 20:23The answer is probably not.
  • 20:25And finally,
  • 20:26we know that some patients as I showed
  • 20:28not just respond to immune therapy
  • 20:30but really have exceptional response.
  • 20:32They really have long term durable
  • 20:34response that goes on for years
  • 20:36or tremendous tumor shrinkage.
  • 20:37And so how can we learn what might
  • 20:39drive not just responsive theory
  • 20:40but exceptional response.
  • 20:42And this is together with Suchat
  • 20:43Shukla's lab at MD Anderson.
  • 20:45We've partnered to look at a handful
  • 20:47of these exceptional responders both
  • 20:48from courts we have but also again
  • 20:50in partnership with industry and are
  • 20:52able to identify certain features,
  • 20:54the presence of high clonal
  • 20:55neoantigens and actually.
  • 20:56A higher proportion of tertiary lymphoid
  • 20:58structures they release seem to be associated
  • 21:00with these exceptional responders,
  • 21:01ones that really have response
  • 21:03that lasts for years.
  • 21:04And so overall,
  • 21:05our lab is really focused on using
  • 21:07a lot of these classic genomic and
  • 21:10transcriptomic tools to understand
  • 21:12response resistance to therapy.
  • 21:14But we know these are sort of broad tools,
  • 21:17classic genomic tools that to
  • 21:18understand really what's happening
  • 21:19in the tumor microenvironment and
  • 21:21the tremendous heterogeneity both
  • 21:22in T cell phenotypes but also in
  • 21:24other cells with immune system,
  • 21:26we need finer tools and that
  • 21:27we've heavily relied on single
  • 21:29cell RNA sequencing for this.
  • 21:31And so our past work really asked a
  • 21:33pretty basic question independent
  • 21:34of therapy which was as you advance
  • 21:37along disease stage as you go from
  • 21:39a relatively normal kidney or at
  • 21:41least non malignant kidney to
  • 21:42early stage kidney cancer.
  • 21:43To locally advanced kidney cancer,
  • 21:45to metastatic kidney cancer,
  • 21:47how does the immune microenvironment change?
  • 21:49How do the T cells change?
  • 21:50How do the myeloid cells change and are
  • 21:52there any interactions between them?
  • 21:54And to do this,
  • 21:55we prospectively collected fresh tumor
  • 21:57specimens from different patients with
  • 21:59either early stage locally advanced or
  • 22:01metastatic disease and perform single
  • 22:02cell RNA in T cell TCR sequencing.
  • 22:04Overall, we had a pretty good balance.
  • 22:07We sequence about 165,000 cells from
  • 22:09a little over a dozen patients heavily
  • 22:11skewed towards sequencing the immune.
  • 22:13Uh, the immune compartment
  • 22:15of the microenvironment.
  • 22:16And so now armed with this data set,
  • 22:18we can begin to ask questions what are
  • 22:20the T cell compartment look like and how
  • 22:22does that evolve with progressive with
  • 22:24advancing disease stage and ask similar
  • 22:26questions of the myeloid compartment.
  • 22:29For the T cell compartment,
  • 22:31we can see heavy infiltration by CDT cells.
  • 22:33Largely there's a huge component of
  • 22:35terminally exhausted CD8T cells,
  • 22:37but we see a variety of T cell
  • 22:39phenotypes ranging from resident
  • 22:40memory like cells to classic T regs.
  • 22:42And so when we classify these
  • 22:44different cell populations,
  • 22:46these T cell clusters and organize
  • 22:49them about organize them in a way
  • 22:51to see which might be increase
  • 22:53in advanced disease stage,
  • 22:55we begin to see highlighted in red
  • 22:57that there are few T cell clusters,
  • 22:58few T cell populations.
  • 23:00There really seems to be enriched
  • 23:02in more advanced disease.
  • 23:04Now, at least for me,
  • 23:05it's a little bit unwieldy to look at
  • 23:06so many different cell populations.
  • 23:08And So what we did was brought more broadly,
  • 23:10classify them just using standard
  • 23:12hierarchical clustering.
  • 23:13We can construct this dendrogram
  • 23:15where transcriptionally related
  • 23:16groups of cells are near each other
  • 23:19on this dendrogram and once that
  • 23:20transcriptionally different are far apart.
  • 23:22And what we can see is now instead of,
  • 23:24you know, 18 or 19 different clusters,
  • 23:27we can see really 2 broad groups
  • 23:29in red T cell CD.
  • 23:30She sells that broadly have
  • 23:32markers of T cell exhaustion,
  • 23:33expression of talks, high expression,
  • 23:36multiple inhibitory receptors
  • 23:38and then everything else.
  • 23:40All of the other T cells
  • 23:41which are shown in blue,
  • 23:42the non exhausted T cells.
  • 23:43And now with this much more
  • 23:45simplified definition,
  • 23:46we can see a pretty striking pattern
  • 23:49that terminally exhausted or exhausted
  • 23:51CD8T cells progressively increase
  • 23:53with advancing disease stage.
  • 23:54They're essentially absent
  • 23:55a normal kidney president,
  • 23:57very low levels in early stage disease
  • 23:59and progressively increasing more.
  • 24:00Against disease stages and that
  • 24:03we by contrast see relatively few
  • 24:05non exhausted CD T cells with
  • 24:08an advanced disease stage.
  • 24:10So that's the T cell compartment.
  • 24:11We have this progressive exhaustion
  • 24:14with advancing disease stage.
  • 24:15What about the myeloid compartment?
  • 24:18And for the myeloid compartment,
  • 24:19it's often harder to put these
  • 24:21cells into discrete buckets.
  • 24:22You know,
  • 24:22for T cells were labeled them as either
  • 24:25AT RAG or a CDA T cell that's exhausted.
  • 24:27You put them in some of these
  • 24:29discrete buckets or clusters.
  • 24:30Myeloid cells as we know can exist
  • 24:31much more along a phenotypic spectrum
  • 24:33and so for this sort of analysis,
  • 24:35for for this sort of continuous phenotype
  • 24:37using a trajectory inference analysis.
  • 24:40Is a really nice approach.
  • 24:41It doesn't force you to put
  • 24:42things into discrete buckets.
  • 24:43It allows cells to exist on
  • 24:46a phenotypic spectrum.
  • 24:47And so when we do that for our myeloid
  • 24:49cells we see a actually this interesting,
  • 24:51this nice interesting branching pattern,
  • 24:53which I think recapitulates a
  • 24:54lot of sort of known biology.
  • 24:55We have classic monocytes at the
  • 24:57root and then branching either into
  • 24:59non classical monocytes on the left
  • 25:02or into macrophages on the right.
  • 25:04And if we look at where these
  • 25:06individual cells are coming from,
  • 25:07those myeloid cells that are
  • 25:09present in normal kidney.
  • 25:10I should say normal with a caveat.
  • 25:13They're adjacent non malignant kidney.
  • 25:14So it's from a cancer patient
  • 25:16might not be totally normal,
  • 25:17but we see that they're largely classical
  • 25:19monocytes and non classical monocytes,
  • 25:20very few macrophages in
  • 25:22these non malignant kidneys.
  • 25:24Now if we look at myeloid cells
  • 25:26from different tumor types,
  • 25:27we see a very different pattern.
  • 25:28The first thing that might catch your eyes,
  • 25:30there's many more macrophages
  • 25:31that's the right-hand branch and
  • 25:33across different disease stages
  • 25:34there's just a lot more macrophages
  • 25:36than there are normal kidney.
  • 25:38But if we actually hone in on on.
  • 25:40That right branch,
  • 25:41we see again a different pattern
  • 25:44between early stage disease,
  • 25:45locally advanced and metastatic
  • 25:46and early stage disease.
  • 25:48Those myeloid cells,
  • 25:49those macrophages are heavily clustering.
  • 25:52Relatively early along that branch,
  • 25:54relatively early in that bifurcation.
  • 25:56By contrast,
  • 25:57locally advanced tumors are kind of
  • 25:59spread throughout that branch or spread
  • 26:01throughout what we call pseudo time.
  • 26:02And if you look at the
  • 26:03metastatic tumors at the bottom,
  • 26:05those macrophages are all the
  • 26:06way at the end of that branch,
  • 26:08all the way at the end of pseudo time.
  • 26:10And So what are the genes and
  • 26:12gene programs that are really
  • 26:14driving these trajectories?
  • 26:15It looks like a a switch from a
  • 26:17more pro inflammatory state to
  • 26:18a more ah as they use the term.
  • 26:20But an M2 like state,
  • 26:21an imperfect term but a a more immune
  • 26:24suppressive or pro tumorigenic state
  • 26:25that we see if we look at signatures
  • 26:27of a pro inflammatory signature
  • 26:29those are really those peak and are
  • 26:32really high early on in pseudo time
  • 26:34at that right hand branch where
  • 26:36those early stage macrophages are.
  • 26:37And by contrast we look in an
  • 26:39anti-inflammatory signature that
  • 26:40really peaks later corresponding to
  • 26:42where those metastatic macrophages
  • 26:44are if we look at individual.
  • 26:45Means prone flammatory genes,
  • 26:46aisle 1, beta TNF, aisle 6.
  • 26:49Those are all relatively absent in those
  • 26:52metastatic macrophages outlined in pink.
  • 26:54Whereas if we look at genes that
  • 26:57are typically associated with
  • 26:58this more M2 like phenotype,
  • 27:00things like C163 of the folate receptor,
  • 27:03those are significantly enriched expression
  • 27:05in those metastatic macrophages.
  • 27:07I should say not shown here those
  • 27:09really do express high levels of
  • 27:11complement genes as well and and
  • 27:12trimmed 2 which has been described
  • 27:14by other groups,
  • 27:14these trimmed 2 positive macrophages.
  • 27:18And so we've looked independently at T
  • 27:20cells independently and myeloid cells.
  • 27:22The natural question is,
  • 27:23are those independent events or are
  • 27:25they actually talking to one another?
  • 27:26And to begin to look at this,
  • 27:28we inferred cell cell interactions
  • 27:30using the transcriptomic data.
  • 27:32And the idea is fairly simple.
  • 27:33We use a tool called cell phone DB,
  • 27:35and the idea is if one group of cells
  • 27:36is expressing a ligand and another
  • 27:38group of cells is expressing the known
  • 27:41receptor complex for that ligand,
  • 27:42you might infer that they're
  • 27:44interacting or talking to one another.
  • 27:45And by randomly permuting the labels,
  • 27:47you can actually get some statistics.
  • 27:48And say, is this something that
  • 27:50we expect more than by chance?
  • 27:51And what's shown here is a heat map
  • 27:53of the number of interactions between
  • 27:55different cell populations and this
  • 27:56couple different patterns you might see.
  • 27:58There's the darker blue area that's an
  • 28:01area of relatively low number of cell
  • 28:03cell interactions between different
  • 28:04cell types and that's between usually
  • 28:07between different cell populations.
  • 28:08In red in the upper left corner,
  • 28:10you see a lot of interactions mostly
  • 28:12between different myeloid cell populations.
  • 28:14And then outlined in Black was a
  • 28:16particular area that caught our eye,
  • 28:18which are.
  • 28:19High number of interactions between T
  • 28:20cells and myeloid cells and when we zoom
  • 28:23in and look on exactly what populations,
  • 28:25it's actually these M2 like these trim
  • 28:272 positive macrophages interacting
  • 28:29with these terminally exhausted
  • 28:30CD T cells at a high degree.
  • 28:32So these are inferred interactions.
  • 28:34We obviously have to make sure
  • 28:36they're actually present in
  • 28:37the same in the same sample.
  • 28:38They have to be in the same
  • 28:39tumor to physically interact.
  • 28:40And so we'll look at the relative
  • 28:42proportion of these different populations.
  • 28:43We can again see they're
  • 28:45really highly correlated.
  • 28:46That's a strong correlation
  • 28:47between the presence of these CDT.
  • 28:49Cells and these tumor associated
  • 28:51macrophage populations.
  • 28:52And So what are these interactions?
  • 28:55They appear to be interactions
  • 28:56that are bidirectional and really
  • 28:58support these different phenotypes.
  • 28:59So these tumor associated
  • 29:01macrophages are producing ligands
  • 29:02for inhibitory receptors on T cells,
  • 29:04things we already know about and
  • 29:06target like PD1 and PDL 2 for PD one,
  • 29:09but things we don't yet target
  • 29:10and kidney cancer,
  • 29:11things like PVR and actin 2 for
  • 29:13TIGIT ligands for Tim three and
  • 29:15other inhibitory checkpoints.
  • 29:17But it's not all myeloid
  • 29:18cells inhibiting T cells,
  • 29:20those T cells terminally exhausted
  • 29:21CD T cells are also producing
  • 29:23factors like myth or.
  • 29:25Uh,
  • 29:25CS,
  • 29:25CSF one that support this more
  • 29:27M2 like polarization and so it's
  • 29:30really a bidirectional sort of
  • 29:32immune circuit that seems to be
  • 29:34present in advanced disease stages.
  • 29:36This is of course all inferred
  • 29:38from gene expression.
  • 29:39So can we gain a little bit more
  • 29:41confidence that this is this is true,
  • 29:42this is real and we have a couple
  • 29:44of different validation exercise
  • 29:45I'll briefly go through.
  • 29:46One is are these actually expressed
  • 29:47at the surface of the protein level
  • 29:49and these different populations and
  • 29:50we can use flow cytometry to look at
  • 29:52that are these actually present in the
  • 29:54same tumors and physical proximity?
  • 29:55In situ within the tumor itself
  • 29:57and we can use
  • 29:58Multiplex immunofluorescence and
  • 29:59then ultimately is is actually true
  • 30:01not just in this small discovery
  • 30:03cohort but also more broadly in in
  • 30:06other other larger patient cohorts.
  • 30:08And so briefly to step through this is
  • 30:10in partnership with Arlene Sharps lab.
  • 30:11We looked at these different terminal
  • 30:14exhaust C8T cell populations and CD
  • 30:16163 positive macrophage populations
  • 30:18and ask the question do the T cells
  • 30:20express the ligands we think they
  • 30:22should or the receptors we think they
  • 30:24should and do the macrophages express?
  • 30:26Well, I guess we think they should
  • 30:27and the answer was yes,
  • 30:28they do and they're higher
  • 30:30in advanced disease stages.
  • 30:31Partnering with Sabina Signer Ideas Lab
  • 30:34performing Multiplex immunofluorescence,
  • 30:35we looked at whether these myeloid cells,
  • 30:37these CD 163 positive macrophages
  • 30:39were actually physically interacting
  • 30:41in advanced tumors.
  • 30:42With these terminal exhausted
  • 30:43T cells and again in multiple
  • 30:45multiple metastatic tumors,
  • 30:47we can see evidence for
  • 30:49insight 2 interactions.
  • 30:50And then finally looking
  • 30:51at other external cohorts,
  • 30:53we first looked at a previously
  • 30:55published saitov cohort from Burn
  • 30:57Bodenmiller's Group and re analyze
  • 30:59that data to specifically look for
  • 31:01our T cell and myeloid populations,
  • 31:03our CD163 myeloid populations.
  • 31:04And again could see this pattern
  • 31:06where the proportion of these cells
  • 31:08increase with advancing disease stage.
  • 31:09And finally we derived a gene
  • 31:12expression signature representing that
  • 31:13interaction between those myeloid
  • 31:14cells and T cells and use that gene
  • 31:17signature to interrogate the TCA
  • 31:19and again found this pattern of.
  • 31:21Increasing signature of interaction
  • 31:23with advancing disease stage.
  • 31:24Now that we have this RNA seek signature,
  • 31:27we actually ask the question is
  • 31:29this interaction signature actually
  • 31:30associated with worse outcomes with
  • 31:33worse survival. And the answer was yes.
  • 31:35If we look at the TCG a data having
  • 31:37a high expression of this Tam to
  • 31:39T cell interaction signature was
  • 31:41really associated with worse
  • 31:43overall survival overall.
  • 31:45But again we have to be cautious
  • 31:46that might just be because it's
  • 31:47enriched in stage four disease.
  • 31:49And so if we look specifically
  • 31:50at those patients with stage
  • 31:52four disease in the TCG a,
  • 31:53again we see the same.
  • 31:54Effect having a high number,
  • 31:55higher number of those interactions
  • 31:57associated with worse overall survival.
  • 31:59And if we look at again our initial cohort,
  • 32:01our checkmate cohort that we previously
  • 32:03reported on again having a high expression
  • 32:06of those that interaction signatures
  • 32:08associated with a horse survival.
  • 32:10And so the model we would put forward would
  • 32:11be that with advancing disease stage we
  • 32:13have this progressive T cell exhaustion,
  • 32:15this switch to more M2 like this
  • 32:18anti-inflammatory macrophage
  • 32:18population and that critically in
  • 32:20advanced disease states that they're
  • 32:22really talking to one another,
  • 32:23they're interacting in a way
  • 32:24that we hope is therapeutically.
  • 32:25Marketable.
  • 32:27So up until now I've talked broadly about
  • 32:30kidney cancer as if it's one entity,
  • 32:32but I've been sort of misleading you.
  • 32:35It's actually many,
  • 32:35many different diseases.
  • 32:36And So what we've been talking about is
  • 32:38really clear cell kidney cancer which
  • 32:40is shown here histologically which is
  • 32:42looks clear under the microscope
  • 32:43where it gets its name.
  • 32:45But actually this is a host of over 20
  • 32:47different disease with more entities
  • 32:49being described each WHO update
  • 32:51and there's a huge proportion of,
  • 32:53I apologize, should be about
  • 32:5525% non clear cell which is.
  • 32:57So it was bad to be labeled by
  • 32:58what you're not, but these are
  • 33:00variant histologies of clear cell.
  • 33:01The more common ones are papillary
  • 33:04chromophobe accounts for about 5%
  • 33:06rarer types including translocation
  • 33:08and then hereditary forms including
  • 33:10FH deficient really aggressive
  • 33:12disease biology that often affects
  • 33:14people in their 30s.
  • 33:15And so while all of our efforts has
  • 33:17been have been really focused on clear
  • 33:19cell kidney cancer and that's where a
  • 33:21lot of the clinical data is as well.
  • 33:22We know that these non clear solver
  • 33:25variants really aren't unmet
  • 33:26clinical and scientific need.
  • 33:28We really need to understand their biology.
  • 33:29And how to treat them because most of
  • 33:31their treatment is really extrapolated
  • 33:33from our experience in clear cell.
  • 33:34And so our first sort of approach to
  • 33:36this is really in the chromophobe space.
  • 33:39And so chromophobe is really are
  • 33:40along a spectrum of these renal
  • 33:42oncocytic neoplasms that range
  • 33:44from pretty benign tumors,
  • 33:45renal oncocytoma which never metastasize,
  • 33:49I should say never virtually
  • 33:51never metastasize.
  • 33:52They really have limited
  • 33:55genetic genomic alterations.
  • 33:56Chromophobe is on the other end, are they?
  • 33:58They are true malignant disease.
  • 34:00They have multiple whole chromosome
  • 34:02losses and then there's also these
  • 34:04emerging entities in between low
  • 34:06and high grade unconscious tumors
  • 34:08which have variable potential to
  • 34:10actually invade and metastasize.
  • 34:13And so one of the key things
  • 34:15about these chromophobe tumors is
  • 34:17unlike clear cell kidney cancer,
  • 34:19these don't do well with immune therapy.
  • 34:21And so there's always going to be exceptions.
  • 34:23But both in in clinical trial data
  • 34:25of immune therapies where they've
  • 34:27included chromophore patients,
  • 34:29the response rate is typically less than 10%.
  • 34:31And if we look at these chromophobe tumors,
  • 34:33this is our own data partnership with the
  • 34:36international Metastatic Database consortium,
  • 34:38Danny hangs group at University
  • 34:40of Alberta and we looked at now
  • 34:42hundreds of patients treated
  • 34:43with new checkpoint inhibitors.
  • 34:44Real world data that are the clear cell
  • 34:46or non or chromophobe and the chromophobe
  • 34:48patients really don't do well here.
  • 34:50This is in sharp contrast to in other
  • 34:54treatment varieties chromophores typically
  • 34:55shape a better disease prognosis and so
  • 34:57really this is an area of unmet need.
  • 34:59Why aren't chromophobe tumors
  • 35:01responding to immune therapy.
  • 35:03And so to begin to look at this and
  • 35:05again these are rare tumor types we we
  • 35:07were able to identify a handful of patients,
  • 35:10about five patients that really
  • 35:12represent this disease spectrum
  • 35:13and again perform single cell.
  • 35:15Kinda sequencing to look at the
  • 35:17immune microenvironment and also the
  • 35:18tumor and stromal components as well.
  • 35:20And really our focus questions
  • 35:22were why aren't these responding
  • 35:23to immune therapies and going in.
  • 35:25We had a couple of hypotheses.
  • 35:27So one is maybe it's just a lack
  • 35:29of immune
  • 35:29infiltration. If you don't
  • 35:31have cells there to begin with,
  • 35:32then it's hard to get any immune response.
  • 35:34The second is perhaps they're exhausted
  • 35:37in ways that don't rely on PD1.
  • 35:39So perhaps these have some severely
  • 35:41exhausted or dysfunctional program that are
  • 35:44really incapable of being reinvigorated
  • 35:45by our current immune therapies.
  • 35:48And the last possibility is maybe
  • 35:50those are just bystander T cells
  • 35:52where they're not actually infiltrated
  • 35:54by tumor specific T cells that are
  • 35:56required for true anti tumor efficacy.
  • 35:58And so with these sort of focused
  • 36:00hypotheses we can begin to look
  • 36:02at these different areas and see
  • 36:03which of them actually are.
  • 36:05Able to chromophobe tumors.
  • 36:06So the first is immuno filtration.
  • 36:08Here we did very basic CD 45
  • 36:11immunohistochemistry just to
  • 36:12look at the immune infiltration,
  • 36:14broad immune infiltration of these
  • 36:16tumors and what we can see is on
  • 36:18the right you see clear cell and the
  • 36:20immunohistochemical stain for CD45.
  • 36:22These are really heavily immune
  • 36:24infiltrated T cells,
  • 36:25immune infiltrated tumors as we
  • 36:26saw in our previous study.
  • 36:28By contrast these oncocytic neoplasms,
  • 36:31oncocytoma is low grade oncocytic tumors,
  • 36:33chromophobe tumors,
  • 36:33these have really low degree of immunity.
  • 36:36Centration and we can see that sort of
  • 36:39characterized statistically on the right.
  • 36:41And so that seems to be one problem.
  • 36:43They just aren't a lot of immune cells.
  • 36:44So we're going to improve immune
  • 36:46responsiveness of these tumors.
  • 36:47One will be driving,
  • 36:49driving immune cells actually
  • 36:51into the tumor itself.
  • 36:53The second is,
  • 36:53are these are these cells that are
  • 36:56there just too exhausted to respond.
  • 36:58And to begin to look at this weekend,
  • 37:00look,
  • 37:00turn to our single cell data and look
  • 37:02to the CDA T cell populations and saw
  • 37:04that they express markers of immune
  • 37:06exhaustion and the transfer is no,
  • 37:07those were not exhausted T cells.
  • 37:09So if we look at clear cell which on the
  • 37:11left versus chromophobe on the right,
  • 37:12we see as we know the the ones in the
  • 37:14clear cell tumors are often exhausted
  • 37:17terminally exhausted CDT cells.
  • 37:18That was not the case for chromophobe
  • 37:20tumors and if we look now at the TCGA.
  • 37:22Data,
  • 37:23we see a very similar pattern that
  • 37:25chromophobe tumors have relatively
  • 37:27low levels of exhaustion markers,
  • 37:29expression of exhaustion markers
  • 37:30that's compared to clear cell disease.
  • 37:32And so these are not exhausted T cells.
  • 37:35They seem actually like they're cytotoxic.
  • 37:36They they seem like they should
  • 37:38be functional.
  • 37:38The ones that are there,
  • 37:39why aren't they actually doing the job.
  • 37:41The last part is maybe they're not
  • 37:43tumor specific and so there's ways
  • 37:45to formally prove this by actually
  • 37:47TCR sequencing these,
  • 37:48reconstructing those TCR's and and
  • 37:50testing for antitumor specificity.
  • 37:51That's what needs to ultimately be done.
  • 37:53Our first take out is is to use
  • 37:55the single cell TCR data that we
  • 37:57have to try to infer specifics and
  • 37:58we did this in two different ways.
  • 38:00One is by taking those TCR's and
  • 38:03mapping them to known to TCR's.
  • 38:06I've known viral specificity.
  • 38:07Those are usually specific for
  • 38:09things like CMB or EB or flu,
  • 38:12common viruses that have
  • 38:13nothing to do with these tumors.
  • 38:14And we can see that the the T cells
  • 38:16and chromophobe kidney cancer
  • 38:18mapped out a much more significant
  • 38:20degree to these viral specific.
  • 38:22They have a viral specificity so much
  • 38:25more likely to be bystander T cells.
  • 38:29The other approach we used
  • 38:30was to take signatures,
  • 38:31gene expression signatures defined by
  • 38:33both Kathy who's group but also Steve
  • 38:35Rosenberg's group at the NCI that
  • 38:37are signatures of tumor specificity
  • 38:39including neoantigen specificity
  • 38:41and see what that expression looks
  • 38:44like in these different tumor types.
  • 38:46And we can see for clear cell kidney
  • 38:48cancer in red that those have a
  • 38:50high degree of tumor specificity
  • 38:52signature and for chromophobe
  • 38:54that's substantially less.
  • 38:55And so overall this is our first
  • 38:57sort of attempt to really.
  • 38:59Characterize what is an uncommon
  • 39:00and rare tumor type and really try
  • 39:02to understand it's immune biology.
  • 39:04It looks like it has a lack of
  • 39:06immune infiltration.
  • 39:06It looks like the T cells that are
  • 39:09there are probably fully functional
  • 39:11but lack of tumor specificity.
  • 39:13So that's the work we've we've
  • 39:15largely done the work that was
  • 39:16published last year and the work
  • 39:18on on chromophobe tumors that's
  • 39:20been really over the past year.
  • 39:22But now what we want to do is
  • 39:23move from just characterizing
  • 39:24the disease biology to really
  • 39:26understanding how these different
  • 39:28tumor microenvironments might impact
  • 39:29response or resistance if therapy.
  • 39:31And ultimately how can we
  • 39:33functionally evaluate these,
  • 39:34how can we actually go from a
  • 39:36laundry list of potential sell
  • 39:37sell interactions to ones that
  • 39:38actually might be therapeutically
  • 39:40targetable in the clinic and
  • 39:41that's really what the focus is.
  • 39:43We've started this process,
  • 39:44but over the next year.
  • 39:46The sort of idea behind this
  • 39:48is is shown here.
  • 39:49This is a perspective we publish a
  • 39:51couple of years ago now which is
  • 39:53to really try to integrate these
  • 39:54tumor biopsies for fresh tissue
  • 39:56collection for single cell and
  • 39:58sequencing into clinical trials.
  • 39:59And obviously this is expensive,
  • 40:01this is technically difficult
  • 40:03this feasibility issues.
  • 40:05But if you could do this even
  • 40:06for a handful of patients,
  • 40:08a small discovery cohort and really go
  • 40:09into a lot of depth for small number
  • 40:11of patients then you can actually
  • 40:12learn some lessons like we did in
  • 40:14our our prior work and try to then use more.
  • 40:17Conventional tools,
  • 40:18standard exome sequencing,
  • 40:20RNA sequencing,
  • 40:21immunofluorescence,
  • 40:21amnestic chemistry,
  • 40:23then try to apply that to a larger
  • 40:26validation cohort.
  • 40:27And so this is our our attempt,
  • 40:30this is our basic schema that we try
  • 40:31to take patients who are responsive
  • 40:33and non responsive to immune therapy.
  • 40:34We try to get biopsies before treatment
  • 40:36as much as possible is often challenging.
  • 40:38We try to get biopsies on treatment
  • 40:41or at least that progression,
  • 40:43very variable success on that.
  • 40:46And then to perform single song
  • 40:47RNA sequencing to really uncover
  • 40:48what are the cell type differences,
  • 40:50cellular composition differences,
  • 40:51phenotypic differences and ultimately
  • 40:53what are the differences in cell
  • 40:55cell interactions and so we've.
  • 40:57Uh Bin lucky again.
  • 40:58This is through partnership with a
  • 40:59number of academic collaborators,
  • 41:01but also industry collaborators
  • 41:02preparing to a number of phase two
  • 41:04and phase three trials that we've
  • 41:06been able to collect fresh tissue
  • 41:07from a total of 96 tumors that
  • 41:09were treated with immune therapy.
  • 41:11And we've performed enzymatic
  • 41:13association single cell RNA sequencing
  • 41:14on these on these tumors really to
  • 41:17understand what is the difference
  • 41:18in the immune landscape between
  • 41:20responsive and nonresponsive tumors.
  • 41:22And we've put through a partnership
  • 41:24with AKOYA began to look at
  • 41:25what is the orientation of the.
  • 41:27The physical location of these tumor types,
  • 41:29these different immune populations in space
  • 41:31using these high dimensional platforms.
  • 41:33This is an example of of one of
  • 41:35our tumors from a responsive
  • 41:37patient showing actually a high,
  • 41:39high number of traditional
  • 41:41lymphoid structures.
  • 41:42And so is this actually feasible,
  • 41:44are we actually able to collect
  • 41:45these cryopreserved specimens
  • 41:46from different clinical trials
  • 41:48and get viable cells out of this?
  • 41:50So our first attempt at this was
  • 41:51on a small number of patients
  • 41:53was just on 13 patients.
  • 41:55This is a collaboration with with
  • 41:57Kathy still with MM Atkins at
  • 41:59Georgetown and with Kelly St who
  • 42:01runs a computational group at USC
  • 42:03where we looked at the small number
  • 42:06of cryopreserved tumors and said,
  • 42:08are we able to get viable cells out of this?
  • 42:10And the short answer was yes,
  • 42:12that we're able to get actually really good.
  • 42:13Representation of both tumor cells,
  • 42:15immune cells and also stromal components
  • 42:18and actually even with this small
  • 42:20cord of end up being about 13 samples
  • 42:22that were suitable for analysis.
  • 42:24After passing quality control,
  • 42:25we can actually begin to see are the
  • 42:28differences in immune microenvironment
  • 42:30between responsive and resistant tumors.
  • 42:32And so again this is a
  • 42:34trajectory inference analysis,
  • 42:34this time it's for T cells.
  • 42:36And again we see a branching structure,
  • 42:38but here are fairly interesting one,
  • 42:40one that starts with a root of naive
  • 42:42T cells and branches either into
  • 42:44terminally exhausted CD8T cells.
  • 42:45Those are the same ones we saw in
  • 42:48our our prior work across disease
  • 42:50stages or to these still having
  • 42:52an exhaustion phenotype but these
  • 42:53slam F7 positive CD8T cells.
  • 42:57And we look,
  • 42:57when we look specifically at which
  • 42:59immune populations are associated with
  • 43:01resistance or with altered survival,
  • 43:03it turns out that the slam of seven
  • 43:05positive CDT cells again in this
  • 43:07very small cohort only 13 patients,
  • 43:09but associated with progressive
  • 43:11disease and with worse progression
  • 43:13free and overall survival.
  • 43:15And so this is our sort of initial
  • 43:1813 patients.
  • 43:18We're now parsing through the sequencing
  • 43:20data from our our 96 patients to
  • 43:23really get a better handle on what are
  • 43:25the different human populations that
  • 43:27might exhibit this sort of behavior.
  • 43:29But we also have to move
  • 43:30beyond immune profiling.
  • 43:31We might get a sense of what are the immune
  • 43:33populations that are are relevant
  • 43:34and what are the immune interactions,
  • 43:35which could play a role.
  • 43:37But we actually have to to nominate
  • 43:38individual targets for the clinic.
  • 43:40We actually have to be able to test this.
  • 43:41And then kidney cancer is a
  • 43:43unique opportunity to do this.
  • 43:45Part of it is just purely practical.
  • 43:46These are enormous tumors.
  • 43:47You can have a 6 1/2 centimeter
  • 43:49tumor that's a stage one tumor and
  • 43:51it's not uncommon for these tumors
  • 43:53to extend to exceed 10 centimeters.
  • 43:55And so there's just lots of
  • 43:56material to be able to extract
  • 43:58individual immune populations.
  • 43:59Individual tumor populations and
  • 44:02actually functionally test which
  • 44:03interactions might actually play a
  • 44:05role and we've begun to do this.
  • 44:07We've been able to take these primary
  • 44:10tumors and I should mention Cat sudakin
  • 44:12in the lab is really spearheaded this
  • 44:15process of identifying patients as
  • 44:17in collaboration with Mike Hurwitz
  • 44:19who runs the Gu tumor bank with Debo
  • 44:21Adeniran and Peter Humphrey and
  • 44:23Pathology and Pat Kenny and Urology
  • 44:25where we're able to routinely on
  • 44:27just about every nephrectomy that's
  • 44:28done at the at the hospital.
  • 44:29Here,
  • 44:30collect fresh tumor for this sort of work.
  • 44:34And so we're able to extract both
  • 44:35immune cells and tumor cells and for
  • 44:37a subset of patients we're actually
  • 44:39able to to grow out tumor cell lines.
  • 44:41And so this is really a valuable
  • 44:43resource for thinking about autologous
  • 44:45coculture experiments with T cells.
  • 44:47And so our sort of overall plan for
  • 44:49functional validation is sort of two phases.
  • 44:51One is a more reductionist approach
  • 44:53and one is one that maybe perhaps
  • 44:56recapitulates the contacts of the
  • 44:573D micro environment a bit better.
  • 45:00The reductions to approach.
  • 45:01Again this is led by a few people
  • 45:03in the lab soaky.
  • 45:03Uh Katrina and Hannah is to basically
  • 45:06break this down into individual
  • 45:08cell populations.
  • 45:09So to associate those tumors
  • 45:11into single cell suspension,
  • 45:12isolate individual cell populations
  • 45:14of interest,
  • 45:15coculture just those populations of
  • 45:17interest with a therapeutic drug with
  • 45:19an inhibitor of a particular interaction.
  • 45:21And then be able to measure
  • 45:23changes in T cell function,
  • 45:25basic flow cytometry for intracellular
  • 45:28cytokine production collaboration assays,
  • 45:30expression of cytotoxicity markers like
  • 45:32granzyme and then we begun to implement.
  • 45:35These model antigen systems
  • 45:36where we have now T cells that
  • 45:38we engineer with a specific TCR,
  • 45:40TCR against NY,
  • 45:41so one or against WT1 and we have
  • 45:43tumor cells that express those
  • 45:45antigens and express luciferase
  • 45:47and we can actually now test have
  • 45:48a a model antigen system for for
  • 45:51testing these impact on cytotoxicity.
  • 45:54Again,
  • 45:54there's limitations of a reductionist model.
  • 45:57And so in work that's done in
  • 45:59collaboration actually now with
  • 46:00AstraZeneca and and a lot of
  • 46:02mentorship from from Marcus here,
  • 46:03we also have begun to implement these
  • 46:05tumor fragment models where we try
  • 46:07to recapitulate the 3D microenvironment.
  • 46:09We actually cut the tumor
  • 46:10into these various fragments.
  • 46:12We embed them in a collagen matrix.
  • 46:14We float that in media where we
  • 46:16can add various
  • 46:16perturbations and we can see that over
  • 46:18the course of its short-term culture,
  • 46:21three to five days,
  • 46:22we really can recapitulate the Histology,
  • 46:24the architecture of these clear cell.
  • 46:25Consumers and preserve a lot of
  • 46:27the immune microenvironment both
  • 46:29T cell and myeloid components.
  • 46:30And so with this we can actually
  • 46:31use this to to actually function
  • 46:33and validate some of these things.
  • 46:34We've done some toy experiments,
  • 46:35I'm showing one where we've added
  • 46:37low dose or higher dose IL two and
  • 46:39can show that we can impact the
  • 46:40T cells that are there just as a
  • 46:42initial sort of proof of concept.
  • 46:43But now together with AstraZeneca
  • 46:45really looking at some of these
  • 46:47interactions that we found in our
  • 46:49original cancer cell paper TIGIT
  • 46:51and others and seeing whether
  • 46:52inhibition of those inhibitory
  • 46:54interactions might actually impact.
  • 46:56Tumor killing and cell function.
  • 46:59And so that second piece is really the
  • 47:01tumor microenvironment and how that
  • 47:02changes with advancing disease stage and
  • 47:04now how we can use that to understand
  • 47:06response and resistance to therapy.
  • 47:07The final aspect of our lab focuses
  • 47:09on is really trying to identify
  • 47:11what are the relevant antigens in
  • 47:12kidney cancer and how we might
  • 47:14be able to therapeutically target
  • 47:15them with antigen directed therapy.
  • 47:17And in order to do this,
  • 47:18in my mind we need sort of two pieces.
  • 47:20We need a toolkit of experimental
  • 47:21toolkit to do this and the other
  • 47:23is we actually need the samples.
  • 47:24And so the experimental tool toolkit
  • 47:26is I would say both computational
  • 47:28and physical tools.
  • 47:29Computational tools includes
  • 47:31better antigen prediction,
  • 47:32the ability to infer not just
  • 47:35neoantigens but things like
  • 47:36expression endogenous retroviruses.
  • 47:39Physical tools for antigen detection.
  • 47:41These are immuno epidemics where we
  • 47:43can actually immunoprecipitate off
  • 47:45Class 1 molecules from tumor cells,
  • 47:47elute peptide and and actually physically
  • 47:49detect the presence of individual peptides.
  • 47:52This was done in collaboration with
  • 47:54Steve Carr's group at the Broad.
  • 47:56And then TCR tools and these are
  • 47:57now pretty established tools,
  • 47:59tools for single cell TCR sequencing
  • 48:00where we know the full alpha,
  • 48:01beta paired TT cell sequence,
  • 48:04but also the ability to then reconstruct
  • 48:07them in primary healthy T cells and
  • 48:09actually probe their specificity.
  • 48:11So we do have a good toolkit for
  • 48:13antigen discovery now we need the
  • 48:15samples and I think clinical trials,
  • 48:17particularly early phase clinical trials
  • 48:19are really wonderful platform to be
  • 48:21able to do this really in-depth analysis.
  • 48:23And so I'm going to give a little
  • 48:24bit of a vignette of work.
  • 48:26That's wrapping up from my time
  • 48:27at Dana Farber.
  • 48:28This is a clinical trial,
  • 48:29a phase one trial that led together
  • 48:31with Tony Sherry and Patrick OTT of
  • 48:33neoantigen vaccination in kidney
  • 48:34cancer that I think has some
  • 48:36interesting findings in of itself,
  • 48:37but really also serves I think,
  • 48:39as a platform for answering some
  • 48:41of these questions.
  • 48:42So this was a a trial that took
  • 48:44stage three or stage four patients
  • 48:46with kidney cancer.
  • 48:47They had to be fully resected
  • 48:49at the time of
  • 48:50surgery. So they had no evidence of disease
  • 48:52and these were clear cell only and we
  • 48:54treated 5 patients, vaccine or local.
  • 48:56Pipeline maps or ctla 4 inhibitor
  • 48:58given at the vaccine site or vaccine
  • 49:00alone and the basic process we
  • 49:02would take their tumor and then
  • 49:04normal cells would take their blood,
  • 49:06perform whole exome sequencing
  • 49:07and RNA sequencing on the tumor.
  • 49:09We'd identify tumor specific mutations,
  • 49:11we ensure that they're actually
  • 49:13expressed that the RNA level
  • 49:15we'd use some of our tools that I
  • 49:17just described to infer what are
  • 49:19likely to be HLA binding peptide,
  • 49:21so likely to be antigens and then we'd
  • 49:24actually get together in a spin a room.
  • 49:26And turn it over zoom,
  • 49:28but we get together as an epitope
  • 49:30selection board to actually pick
  • 49:31which of the mutations we want
  • 49:33to target and design synthetic
  • 49:34long peptides anywhere between 20
  • 49:36to 25 more typically in order to
  • 49:39actually physically synthesize them.
  • 49:41We then partnered with a GMP
  • 49:44peptide manufacturer to actually
  • 49:45synthesize these peptides up to 20
  • 49:48representing different neoantigens,
  • 49:50pulled that together with an immune
  • 49:52adjuvant Poly ICLC and then that
  • 49:54delivered that to the patient.
  • 49:56We treated 9 patients overall.
  • 49:59It's the sort of standard demographics
  • 50:01you'd expect for kidney cancer.
  • 50:03Majority were stage three.
  • 50:04There were a couple of stage
  • 50:06four patients as well.
  • 50:07And in every patient we were able to to
  • 50:09identify enough mutations to actually target.
  • 50:11So median of 13 unique mutations
  • 50:13were targeted per patient
  • 50:14with 15 different peptides.
  • 50:16Kidney tumors have a lot more frameshift
  • 50:17mutations than a lot of other tumor types.
  • 50:19So we're able to target a
  • 50:20lot of frame shifts.
  • 50:21I think interestingly,
  • 50:22we're actually able to target in the
  • 50:24majority of patients actually driver
  • 50:26mutations within kidney cancer.
  • 50:27And it turns out those when we
  • 50:29look back ends up being the most
  • 50:31immunogenic immunogenic peptides,
  • 50:33the ones that represent driver mutations.
  • 50:37And I hesitate to like talk about
  • 50:39any sort of clinical data here just
  • 50:42because it's it's nine patients,
  • 50:43but they're,
  • 50:44yeah,
  • 50:44at least encouraging that was certainly safe.
  • 50:47No one,
  • 50:47everyone did well on the trial and
  • 50:49there have been no clinical relapses,
  • 50:51I would say in this population probably
  • 50:53at this point somewhere around 1/3
  • 50:55third to half might have relapsed.
  • 50:57And so the fact that there have been no
  • 50:59disease recurrences is at least encouraging.
  • 51:02But again,
  • 51:02a big part of this was really the
  • 51:04Biospecimen collection and these
  • 51:05were really generous patients that
  • 51:07went through a lot for this trial.
  • 51:08And so for each of these patients and
  • 51:10they went through the vaccination itself,
  • 51:12which were five priming doses of
  • 51:14the course of three weeks and two
  • 51:16booster shots at week 12 and week 20.
  • 51:17They had multiple skin biopsies
  • 51:19prior to and after vaccination to
  • 51:21look at whether they infiltrated
  • 51:22immune populations within the skin.
  • 51:24We obviously have the tumor tissue itself,
  • 51:26but we need lots of circulating
  • 51:27blood cells as well to perform immune
  • 51:29monitoring and so we perform leukapheresis.
  • 51:32Often required a central line placement
  • 51:34before and after treatment and weeks here and
  • 51:36week 16 and pretty regular 200ML blood draws,
  • 51:39regular 200ML blood draws,
  • 51:41really look at what are the circulating
  • 51:45immune populations and our questions,
  • 51:47we're really trying to look kind of end
  • 51:49to end what's happening at the skin as
  • 51:50we move to the circulating immune system.
  • 51:52Ultimately are we getting tumor reactivity,
  • 51:54it's nice to get reactivity
  • 51:55against the vaccine itself,
  • 51:56but it's actually not impacting
  • 51:58tumor reactivity.
  • 51:59We haven't done too much.
  • 52:00We haven't actually been helpful.
  • 52:02And so just to briefly walk through this,
  • 52:03this is what a typical this
  • 52:05actually patient one.
  • 52:06So this is the prior vaccine scars and
  • 52:08what what the vaccine site looks like
  • 52:11two to three days after vaccination
  • 52:13we can perform enzymatic dissociation,
  • 52:16CD45 isolation and single cell RNA
  • 52:19sequencing identifying really high
  • 52:21populations of myeloid and lymphoid cells.
  • 52:24And this is work in progress,
  • 52:26but we actually see some fairly
  • 52:28interesting changes in both the
  • 52:29myeloid cell and T cell population.
  • 52:31I would say predominantly it happens.
  • 52:32With vaccination,
  • 52:33we're not seeing huge differences
  • 52:35with the addition of epilimnion map.
  • 52:37Moving on to the circulating immune system,
  • 52:39really the workhorse for this
  • 52:41was interferon gamma Ellie spots.
  • 52:42These are taking peripheral T cells out
  • 52:44out of a patient peripheral blood cells,
  • 52:46putting them into a dish and stimulating
  • 52:48them with the same vaccine peptides
  • 52:49and seeing whether those T cells
  • 52:51release interferon gamma as a marker
  • 52:53of antigen reactivity and would see
  • 52:54it week one prior to vaccination.
  • 52:56Basically none of the neoantigen pools,
  • 52:58the 1st 4 rows had any reactivity,
  • 53:01but we get pretty strong reactivity
  • 53:04with vaccination and that when we do.
  • 53:07Close cytometry and it's Cellular said
  • 53:08to kind of standing we actually see that
  • 53:10these are not largely polyfunctional,
  • 53:12that they T cells not only produce interferon
  • 53:16gamma but things like aisle 2 and TNF.
  • 53:19And finally,
  • 53:20moving beyond just vaccine reactivity,
  • 53:22are we getting actually tumor reactivity.
  • 53:23And So what we can do is again take
  • 53:25some of these post vaccine T cells,
  • 53:27stimulate them with one of our vaccine
  • 53:29peptides in this case against a driver
  • 53:32mutation PR one and then coculture with
  • 53:34that same patients autologous tumor
  • 53:35and see whether those PBR one specific
  • 53:38T cells actually recognize tumor.
  • 53:41And the answer is, is yes,
  • 53:42that we are able to actually
  • 53:44get tumor reactivity.
  • 53:44It's not for all patients,
  • 53:45but for the majority of patients
  • 53:47we're able to get evidence of tumor.
  • 53:49The activity with vaccination and
  • 53:50so really this is our our first
  • 53:52sort of attempt at an antigen
  • 53:54directed therapy and kidney cancer,
  • 53:55but I think neoantigens are
  • 53:57a good place to start.
  • 53:58But I think those are clearly
  • 53:59not going to be the whole story.
  • 54:01And kidney cancer we know that
  • 54:02there's not an association between
  • 54:04as I showed high neoantigens and
  • 54:05and response to therapy.
  • 54:06So we have to look beyond this
  • 54:09initial neoantigen focused
  • 54:11world and really look at other
  • 54:12sources of antigens as well.
  • 54:14And very briefly this is work large
  • 54:16in collaboration with Bill Kaylan's
  • 54:17Group and Steve Carr's group.
  • 54:19We're using the same cohort of patients,
  • 54:21the same tumors to actually look
  • 54:23at endogenous retroviruses as
  • 54:24potential antigenic targets.
  • 54:26These are ones that are aberrantly
  • 54:27expressed in a few different tumor types,
  • 54:29but specifically kidney cancer has a high
  • 54:31expression of these endogenous retroviruses.
  • 54:33So we can again use our computational
  • 54:36tools to predict antigens,
  • 54:37potential ER derived antigens and use
  • 54:39mass spec based approach to actually
  • 54:41physically detect those antigens.
  • 54:43And in this first patient,
  • 54:45this patient 110 from our original trial,
  • 54:47we see that there were seven ERV.
  • 54:49Derived peptides that were present
  • 54:51on tumor but on a normal normal
  • 54:53tissue and when we take one of those,
  • 54:55the one highlighted in pink and
  • 54:57actually test those for reactivity in
  • 54:59peripheral blood cells from that patient,
  • 55:01we can see that those that patients
  • 55:03actually capable of mounting a low
  • 55:05level but a response to that peptide.
  • 55:07So just a initial proof of concept
  • 55:09that these RV's can actually be
  • 55:10antigenic and now we can actually do
  • 55:12this much more systematically look
  • 55:14across all patients and all of their
  • 55:16endogenous retroviruses that are
  • 55:18presented and look for antigenicity.
  • 55:20Again, these are very focused approaches.
  • 55:22These are specific hypothesis,
  • 55:25neoantigen.
  • 55:26Uh or endogenous retroviruses,
  • 55:27the last thing we want to do is ultimately
  • 55:29build systems and collaborate with
  • 55:31with groups that are interested in
  • 55:33more broad antigen discovery efforts
  • 55:34for things that we're not thinking of.
  • 55:36And so we recently entered a
  • 55:38partnership with Remedy Bio,
  • 55:39a biotech company based in Ireland,
  • 55:41which has a a novel platform,
  • 55:43a nano reactor platform that actually
  • 55:45allows you to coculture individual
  • 55:47T cells and tumor cells within each
  • 55:49of these wells,
  • 55:50but actually measure which of those
  • 55:52wells are reactive to tumors,
  • 55:54use a pneumatic system to extract viable.
  • 55:56These cells tumor reactive T cells
  • 55:57and be able to sequence their TCR,
  • 56:00so really be able to understand
  • 56:02much more systematically what is
  • 56:04the repertoire of tumor reactive
  • 56:05T cells in kidney cancer.
  • 56:07And so overall our kind of hope with
  • 56:09this branch of the lab is really to
  • 56:11move beyond our classic tools for
  • 56:13immunomodulation to add the steering
  • 56:15wheels rather than only looking at
  • 56:17the inhibitory checkpoints or the
  • 56:19the sort of go signals for your
  • 56:21immune cells to actually be able
  • 56:22to add a component of an antigen
  • 56:24directed therapy really focus on
  • 56:26HLA restricted antigens.
  • 56:27And that's where the model for the lab,
  • 56:29it's been a busy but a great year.
  • 56:31That's been a wonderful, a wonderful time.
  • 56:33I felt incredibly welcome here
  • 56:35at Yale and been lucky to have
  • 56:37remarkably energetic and and and kind
  • 56:38group of people joined the lab and
  • 56:40really focus on sort of this model
  • 56:42that we start with the patient.
  • 56:43We try to learn things from their tumor,
  • 56:46from their immune system.
  • 56:47We have a lot to go
  • 56:49and a lot of open questions about sell sell
  • 56:52interactions and about antigenic targets,
  • 56:53but always with an eye to try to
  • 56:55bring that into improved diagnostics,
  • 56:57actually improve therapeutics.
  • 56:58And try to bring that back into early
  • 57:00phase trials like I showed with our
  • 57:02neoantigen trial and then to continually
  • 57:04iterate to try to get a little bit
  • 57:06better each time that we do this.
  • 57:07And so with that,
  • 57:08thank you again for the opportunity
  • 57:10to speak and a lot of people,
  • 57:11both my lab and collaborators,
  • 57:13but most importantly the patients
  • 57:14and their families.
  • 57:15And this time I'm happy
  • 57:17to take some questions.
  • 57:25Alright, just one question.
  • 57:33In general, most of them are.
  • 57:39There must be some difference. Responded.
  • 57:45Yeah.
  • 57:48The CDA looks great based on
  • 57:51responding to non response.
  • 57:55Within infiltrated tumors,
  • 57:56it's a good question.
  • 57:58I think that's where our sort
  • 58:00of larger collection of this 90
  • 58:02single cell sequence of 96 tumors
  • 58:04will I think be very helpful.
  • 58:05If I were to answer this six months ago,
  • 58:08I would have said it's it's
  • 58:09going to be impacted largely by
  • 58:10the myeloid component as well.
  • 58:12And I think that's still is
  • 58:13probably true that we kind of
  • 58:15showed in our original study that
  • 58:16even though we're we're thinking
  • 58:17that we're measuring CDT cells,
  • 58:19likely what we're actually capturing is
  • 58:21interactions between those terminally
  • 58:22exhausted CDT cells and the myeloid.
  • 58:24Component and that.
  • 58:25Historically,
  • 58:25we've only targeted 11 branch of that.
  • 58:28We've only targeted the T cell compartment
  • 58:29and not the myeloid compartment.
  • 58:31I think that's going to be
  • 58:32one big piece of it.
  • 58:33The second piece which was we
  • 58:35weren't expecting to find is this
  • 58:36particular phenotype of slam of
  • 58:38seven positive CDT cells that
  • 58:39requires a lot of validation
  • 58:41both that they're actually real,
  • 58:42but then that they have a functional role.
  • 58:44I think that's going to be the
  • 58:45other sort of component.
  • 58:46Are there different actually the cell
  • 58:48phone even though they're broadly
  • 58:49infiltrated by similar numbers of CDT cells,
  • 58:51are those CDT cells of a different
  • 58:53phenotype that actually might be perturbed?
  • 58:55In some way and one nice thing is
  • 58:57there are even you know FDA approved
  • 59:00antibodies like elotuzumab for slim F7.
  • 59:01So one can see actually a pathway those
  • 59:04end up being true to to the clinic.
  • 59:06So that's that's kind of ongoing work.
  • 59:08Now actually a rotation student in
  • 59:09the lab is putting some of seven
  • 59:11into some of these T cells and we're
  • 59:13actually seeing whether this impacts
  • 59:15cytokine production proliferation,
  • 59:16tumor killing.
  • 59:20Yeah. Ohh. And. 27.
  • 59:33Thank you.
  • 59:39Yeah, it's a great question.
  • 59:40So the question was just about the
  • 59:43stromal component fibroblasts and
  • 59:44other stromal and kidney can't
  • 59:46strictly anthelion cells as well which
  • 59:48these are heavily vascular tumors.
  • 59:50I would say our first study
  • 59:52really didn't we weren't we,
  • 59:53we really didn't look at it at
  • 59:54all because our protocol really
  • 59:56enriched for immune cells.
  • 59:57I think now with not only the chromophobe,
  • 59:59the Chromophobe project,
  • 01:00:00but also this these you know larger 96
  • 01:00:04samples we actually much more broadly.
  • 01:00:06Capture cancer,
  • 01:00:07associated fibroblasts and epithelial cells.
  • 01:00:09I will say that first 13 patients we
  • 01:00:11didn't see any that were specifically
  • 01:00:12associated with response or
  • 01:00:14resistance in this very broad look.
  • 01:00:15But that obviously doesn't mean
  • 01:00:16they're not important actually driving
  • 01:00:18either T cell or myeloid biology.
  • 01:00:20And so that's something that I think
  • 01:00:21we need to look into in more depth,
  • 01:00:22but we don't have,
  • 01:00:23we don't know quite yet but
  • 01:00:25actually now I think.
  • 01:00:26Yeah.
  • 01:00:26Now we actually have the tools that
  • 01:00:27I would be able to look at it.
  • 01:00:33You know.
  • 01:00:36Really the only type that sold?
  • 01:00:39Obesity and commonly treated
  • 01:00:41with cycling inhibitors.
  • 01:00:42I'm wondering if you have any hints
  • 01:00:44in your data as to the role of
  • 01:00:47metabolism in the micro environment.
  • 01:00:48Yeah, it's a great question.
  • 01:00:49The shortening I'll give is,
  • 01:00:50is not yet, but I'd love to be
  • 01:00:52able to support it and look at
  • 01:00:53it because it's some fascinating
  • 01:00:55parts about kidney cancer as well.
  • 01:00:56So there's something where even
  • 01:00:58though it's you're more likely
  • 01:01:00to get it the the incidence
  • 01:01:01is higher in obese patients.
  • 01:01:03Those patients who are obese who
  • 01:01:05have metastatic disease do better.
  • 01:01:06Something called the obesity
  • 01:01:07paradox within kidney cancer.
  • 01:01:09And we know that there's some
  • 01:01:10hints that these are in general
  • 01:01:12are really metabolically active.
  • 01:01:13There's really excellent work from
  • 01:01:14Jeff Rathmell and Jim Rathman's
  • 01:01:16group that looked at what are the the
  • 01:01:17primary consumers of for instance
  • 01:01:19glucose and the micro environment.
  • 01:01:20And since that's not the tumor cells,
  • 01:01:22it turns out it's mostly the
  • 01:01:23myeloid compartment that's a primary
  • 01:01:25drive the primary consumer.
  • 01:01:26But T cells are are still consuming a lot.
  • 01:01:29How those actually ultimately I
  • 01:01:30think impact the function of T cells,
  • 01:01:32I think we haven't looked at all,
  • 01:01:34but it would be great to be able to
  • 01:01:36explore especially with some of these
  • 01:01:37models where we're you know have them in.
  • 01:01:39Really nutrient rich,
  • 01:01:40metabolically favorable conditions and xvo
  • 01:01:43actually would be nice to recapitulate
  • 01:01:45some of the nutrient limitations
  • 01:01:47that are present in the tumor itself.
  • 01:01:49Yes.
  • 01:01:51Well, thank you,
  • 01:01:52David.
  • 01:01:53I thank you all for also coming
  • 01:01:55here in person and we'll look
  • 01:01:57forward to grand rounds next week.
  • 01:01:59Thanks so much.