"Type I Interferon Transcriptional Network Regulates Expression of Co-inhibitory Receptors on Human T cells" and "co-Stimulating Innate and Adaptive Immunity to Treat Melanoma"

December 11, 2020

Yale Cancer Center Grand Rounds | December 8, 2020

David Hafler, MD, FANA and Harriet Kluger, MD

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ID: 5992
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00:00Sure, there's enough time for both of you,
00:03so I see folks here.
00:05The numbers are going up and appreciate
00:08folks logging on welcome everyone once
00:10again to Cancer Center, grand rounds,
00:12and we're really very privileged
00:14today to have two of our exceptional
00:17physician scientists presenting.
00:19You know, really and frankly,
00:21what's exciting is it it
00:23once again highlights the
00:25extraordinary work in immunology.
00:26Immuno biology at Yale and at
00:29the impact on this ultimately.
00:32In our cancer therapy and in our
00:34understanding of cancer biology,
00:36so let me turn to our first
00:38speaker to ensure we have time.
00:41Our first speaker is Doctor David Hafler,
00:43who is, you know,
00:44is the ugly professor and chair of
00:47the Department of the Rolla G and
00:49Professor of Immunology, Immunobiology,
00:51and David's accomplishments
00:52are are really quite Legion.
00:54Renee actually prepared a synopsis,
00:56and I just said that I want to make
00:59sure David has time to present.
01:01I won't.
01:02Go through all of it,
01:04but his accomplishments in
01:06terms of understanding.
01:08Advancing neuroscience and understanding
01:10that human autoimmunity in an understanding
01:14how to leverage our understanding
01:16of immunology to impacting human
01:18disease is really quite impressive.
01:21And among his awards include the
01:23distal Prize for Ms Research,
01:26the University of Miami
01:28Distinguished Alumni Award,
01:30the American Urology Association,
01:32Adams Lectureship.
01:34And most recently,
01:35and I think a year or so ago,
01:38election to the National Academy of
01:40Medicine and and David has really
01:42been an incredibly engaged member
01:44of our Cancer Center faculty.
01:46I think David's leadership,
01:47I think, has advanced the cause
01:50of our brain tumor program,
01:51among other things,
01:53an David thank you for making the time
01:56to share your work with us today.
02:00Thank you Charlie.
02:01It's really a pleasure to be here.
02:04And let me turn this on and.
02:08My cell phone, so I'd like to do today
02:12is to present some new unpublished
02:15work which really epitomizes to me of
02:19physician scientists of learning from
02:21the patient and just in a nutshell,
02:24what I'm going to show you is
02:28very fundamental question,
02:29which is what induces the checkpoint
02:32inhibitors particular PD one Tim three lag,
02:363 digit on human T cells.
02:39And that's gonna be the nature
02:41of the talk that the work has
02:43been submitted for publication.
02:45It was put online,
02:46a bio RX being one's interest in
02:48seeing the paper itself and upfront.
02:50I want to really, now Stamos Amita,
02:53who really really performed
02:54this work in our laboratory tone
02:56was now an assistant professor
02:58and then pursuing this work.
03:00It wanted knowledge.
03:01My long term collaborator, Vijay Kutru.
03:03Yes, you see a Yale,
03:04a sticker that he was here
03:06helping us recruit students.
03:08Don't tell the people in Boston.
03:10Enjoy dulberg in the Softmod
03:12who did the computational work.
03:14So the question is,
03:16what are the regulatory mechanism
03:18for induction of a Co inhibitory
03:20receptors on human T cells?
03:22But I'll show you is surprisingly type one,
03:25interferons induce Cohen Cohen
03:26territory receptors on human T cells,
03:29so that's the bottom line of what I'm
03:31going to show you over 30 minutes.
03:34We worked through the in vitro
03:37transcriptional regulatory network
03:38for this interferon beta response and
03:40then we identified an in vivo model
03:42where abara load strongly correlate's.
03:44With type one interferon signature,
03:46which allowed us to perform an in
03:48vivo validation of the in vitro
03:51interferon transcriptional regulatory
03:52network Co inhibitory receptors.
03:54So that's what my talk will be.
03:58Now it's been known for a number
04:00of years to work.
04:02Button from Vijay Kutru and be ready
04:04given we've had a program Project
04:06Grant 2 program project grants looking
04:09Cohen inventory molecules valene
04:10sharp for well over 25 years that PD one Tim,
04:14three lag three and TIGIT ARCO,
04:16regulated and expressed as a module.
04:18So here we have.
04:20Hopefully you will see the pointer.
04:22I won't advance the slide
04:24while I'm doing this,
04:25but you can see that there.
04:28Expression of PD one Tim,
04:30three lag three and TIGIT on C4 and CD8
04:34cells that their modulated together.
04:37And that this is a new spot.
04:40I'll 27 here.
04:41We have the induction of Tim 3
04:44not so much PD one but lag three
04:47and TIGIT by I'll 27 you knock
04:50down aisle 27 the mouse you lose
04:52the induction by aisle 27.
04:54That's the upregulation and
04:56downregulation by the knock down.
04:58Now it's been known for a long time.
05:01That type one interferon signatures,
05:03or enriching chronic viral infection,
05:05and both mouse and humans,
05:07and that chronic viral infection
05:10induces T cell exhaustion.
05:12Really first identified by Rafi
05:14Ahmed in the HIV system and
05:16in El CMV infection and that's
05:19associated with expression and Co
05:21inhibitory receptors such as PD,
05:23One Tim, three lag.
05:25Three antigen is interferon signature
05:27with the LC MP model suggesting that
05:29there may be an Association with type
05:32one interferons and these cone hitori
05:35molecules so wish to ask do they
05:38induce these receptors again here's
05:40why I showed you in terms of mouse.
05:44An you know first experiments and when
05:47I googled in photograph of human,
05:50I swear this is what showed up and I know
05:54way mean to denigrate mouse immunologist.
05:58By showing this picture,
05:59but one can see is that in CD4 cells,
06:04either with with no cytokine
06:06I'll 27 or interferon beta.
06:09This market induction of Tim three lag
06:12three and PD one. By interference.
06:15So now we go into more depth to show this.
06:20Here's how the experiments were done.
06:22We took CD4 CD 8 cells.
06:24That was CD.
06:26That were CD 45 negative positive.
06:29That is a naive T cells and
06:31stimulate them for non use.
06:33Different different time points
06:35with CD3 plus minus.
06:37I'll 27 and interferon beta and one can see.
06:41The induction of here's a control.
06:43The market induction of lag three
06:45and Tim three with interfere on.
06:48Here's the control and he is
06:50looking at Tim three PD.
06:52One here is a summary of data
06:54with Tim three lag through in PD,
06:57one individually and the summary
06:58of Tim three lag 3P1 positive
07:01cells within this market.
07:02Induction by type one interferons interferon
07:04beta of these Co inhibitory molecules.
07:07But surprisingly unlike in the mouse with
07:09digit is Co regulated part of the module?
07:12These other Co inhibitory molecules in human.
07:17We saw that TIGIT use digit expression
07:20in the presence of interferon is
07:22markedly decreased from 25% down to four,
07:2612% from 28% when look the
07:28RNA expression we saw there.
07:30In fact two modules,
07:32one with interferon with Lag,
07:353 Tim, three PD,
07:37one increase with interferon beta
07:40and the other module with digit.
07:43The Jennifer subtest.
07:44Nine other modules,
07:46a CD 160 being decreased by
07:48type One interferon.
07:49So these data show that in humans
07:51there are two modules regulated
07:53by interferon that in fact
07:55go in opposite directions.
07:57Here's a kinetex.
07:59Overtime the induction of Tim three lag,
08:01three PD,
08:02one with the decrease in digit.
08:07So just take a step back.
08:09Why do we have an interest in Tidjane?
08:12I mention this because under the
08:14leadership of Antonio Mora we're
08:16about to embark upon a phase one
08:18clinical trial in patients with
08:20glioblastoma with anti TIGIT or anti PD.
08:23One or a combination of of the two,
08:26working with Jemal eternal
08:27and lead in my lab.
08:29By Liliana Luca.
08:30So why an interest in tinge of this
08:33work goes back to 2012 work done by
08:35S Duluth Lozano in the laboratory.
08:38We've always been impressed with
08:41the biologic effects of blocking
08:43with anti TIGIT looking at Tibet.
08:45The gamut of fear on Gata,
08:483RF-9 and and RRC expression.
08:51And one can see that with anti TIGIT
08:54antibody there's a market loss of
08:57these cytokines in culture and if you
09:00knock down ticket here within SHR Now
09:03you have market increases engagement
09:06affair on and decreases dial 10.
09:09So comparing PD one antigen,
09:11our hands in human systems been very
09:13impressed with the effects of ticket and
09:16also just comparing Ms two glioblastoma,
09:18there really isn't a big difference between
09:21PDL one or PD1 between Ms and brain tumors,
09:25but there is a virtual absolute
09:27difference between TIGIT expression,
09:28typically on the CD 8 cells in patients
09:31with GBM virtually absent in Ms,
09:34he was looking at teacher by
09:36flow and tills versus blood,
09:38suggesting the potential importance of digit.
09:41In the central nervous
09:42system for glioblastoma.
09:44So first one to work through.
09:46After that identification of the
09:48effect of type One interferons
09:51wanted to work through the in vitro
09:54transcriptional regulatory network.
09:56So we use the same model
09:58that would be regift.
10:00Near Youssef used in terms of setting up
10:03identifying the TH17A regulatory network,
10:05and this is work done by a soft in BJ's lab,
10:09so we needed to have higher
10:11resolution transcriptomic data to
10:13construct the regulatory network.
10:15For those of you who aren't engaging
10:17in terms of looking at RNA now,
10:20what we used to do is to
10:22take a T cell stimulate,
10:25measure the RNA 4 hours later
10:27and say this is what it is.
10:30We've learned that their complex regulatory
10:33networks and one needs to really do this.
10:36The kinetics overtime to construct
10:39a dynamic regulatory network.
10:41Such a performance.
10:42This network we took dive CD4 CD 8 cells,
10:45stimulate them,
10:46measure them in different time
10:47points with control versus type.
10:49One interferon did bulk RNA sequencing.
10:51We did 34 samples time three
10:53replicates with the same healthy
10:55donor and we decided that rather
10:57than looking at human variation,
10:59which is significant mediated by the
11:01by the genetics of the individuals,
11:04we do what mouse immunologists do,
11:06which is pick one strain of
11:08mice and study it in detail.
11:11And we measured are we did RNA seek RT
11:13PCR protein for flow so that this is a
11:17transcriptomic analysis of interferon
11:19beta high temporal resolution.
11:21We so differential expression of
11:23gene levels for eight different time
11:25points with interferon stimulation.
11:27Here's a log 2 expression so we have
11:30differential expression patterns.
11:31We have an early expression
11:33pattern here and here.
11:35We have an intermediate expression pattern.
11:38A late expression pattern over here and
11:40finally a bimodal expression pattern goes up,
11:43down and back up.
11:46So in performing it just
11:49transcriptomic analysis,
11:50we looked divided into transcription factors.
11:52Here CD four cells with
11:54different kinetics and these are
11:56different transcription factors.
11:58Again, we can see early
12:00transcription factors immediately,
12:01transcription factors induced
12:03and we identified different Co
12:06inhibitory receptors and different
12:08T cell related genes for both the
12:10CD four and for the CDA population.
12:13Again, in looking at the effect
12:16of interferon.
12:17And what it does in terms of
12:19the transcriptional networks is
12:21critical to look over time 'cause
12:23there's a dynamic change in these
12:26transcription factors and Co
12:28inhibitory receptors overtime.
12:29So we identified the most differentially
12:32expressed transcription factors and
12:34about 20 of them here and these
12:37are transcription factors that
12:38were differentially regulated and
12:40decreased in both CD4 and CD8T cells,
12:43and we as a reality check we
12:46asked of these word known.
12:48Interferon responsive gene.
12:50So here's the IFN responsive responsive
12:53gene score overtime and then the
12:56green represents regulators for Co
12:58inhibitory receptors until the yellow
13:01HIV signatures in progressive patients.
13:04And then I'll 27 regulators.
13:07So we we want to examine these
13:10transcriptional for these
13:12transcriptional factors in detail.
13:15So in order to do this and presented dilemma,
13:19we had to develop new technology
13:20because I called the Heisenberg
13:22uncertainty principle of immunology.
13:24The process of examining the cell
13:26with activation perturb the system.
13:28Some of looking for an electron
13:30after hitting it with HV.
13:32So we had to develop a gene
13:35knockdown the early time points and
13:37primary T cell without activating
13:39T cells and again this is all work
13:42developed by Tomo by Thomas Anita.
13:44We used an efficient lentiviral vectors
13:47that developed by wearing a green.
13:49And basically one takes a
13:51viral like particles V LP's
13:53which is incorporated with TPX,
13:55which degrades Sam Sam HD one,
13:58removes restrictions,
13:59you can transfect primary human
14:01T cells with this Sam S1,
14:04which now allows transfection with SH RNA,
14:07HIV, HIV, lentivirus and all.
14:09This can be done in an activated T cells.
14:13Could knock down the gene and
14:15then do the the incubation.
14:17So here we have night CD.
14:20Or cells incubated without
14:22CD3 CD 28 with this procedure,
14:24knocking down the different genes
14:26and then there is stimulated with
14:29and without interferon beta and then
14:31measured five days later and then
14:34we perform fax GFP of we sort of
14:36the GFP positive cells were knocked
14:38down and did bulk RNA sequencing
14:41and you can see very efficient
14:43knockdown in the GFP positive cells.
14:46With these different transcription factors.
14:47This is a monumental amount to work.
14:51Performed by tomo.
14:52So we perform principal component
14:54analysis to changes in the total
14:56RNA expression after the interferon
14:58signature associated with each knockdown.
15:01So let me just say that again,
15:03so these are PCA plots.
15:05We knock down each transcription
15:07factor and then looked at all
15:10the RNA expression and then put
15:12that into a principle component.
15:14One in principle component,
15:16to what that revealed is that the interferon
15:19one stimulated genes are positive.
15:21Regulated by we call interferon
15:25regulated module one, this modulator
15:29increased the downstream interferon.
15:32Stimulated genes with module 2 represented
15:36transcription factors that negatively
15:40regulated the interferon interferon genes.
15:44So to go into more detail,
15:46we first have the interferon
15:49regulated module one,
15:50so a something that a transcription
15:53factor that knocks down the gene
15:55will lead to decreased expression,
15:58which means as positive regulating.
16:00So the interferon regular module one
16:03regulates the conical interferon
16:05stimulated genes over here.
16:07Where is interferon regulated module two over
16:10here regulates these non Canonical jeans?
16:13Interferon stimulated genes perhaps
16:15a greater interest was looking at the
16:19Co inhibitory receptors so we have.
16:22Interferon regulated module one
16:24over here which is bath map.
16:28ETS2 SP 140 which differentially
16:32regulate lag 3.
16:34PD1 PD L1 slam F6 and other
16:40transcription factors.
16:41And then we have stat one and stat
16:44three which positively regulate
16:46Tim three but not lag 3.
16:48So we see that these different
16:50transcription factors differentially
16:51regulate different Co inhibitory receptors.
16:54And here's a summary.
16:55The data just showed you,
16:57which is the effect of these
17:00transcription factors.
17:01Interferon stimulated stimulation,
17:02so again there are two modules of
17:05transcription factors based on
17:07the global effects on interferon
17:10stimulated genes,
17:11thereby directly regulated by
17:12different modules,
17:13transcription factors and then
17:15Co inhibitory receptors are also
17:17regulated by interferon associate
17:19transcription factors and which up
17:21regulate and down regulate these receptors.
17:24So we have for example,
17:27a MoD in module one, the which is a bath.
17:31ETS2 math one which positively
17:34regulate lag 3 Tim three and PD one
17:38but negatively regulate a TIGIT.
17:40BTL BTL A and CD 160 again.
17:43Going along with the flow cytometry data.
17:46And again this I showed you step
17:49one and three here.
17:51Positively regulate Tim three
17:53but negatively regulate PD one.
17:56So then we performed a hierarchical
17:58backbone network analysis transcription
18:00factors.
18:00I'll just go over this very briefly,
18:03but basically looked at gene expression,
18:05overtime, differential expression,
18:06protein, DNA bonding,
18:08a transcription factor database
18:09is integrated.
18:10Those data looked at a rank list
18:13of transcription factors which we
18:14perturbed and knocked down as I
18:17showed you integrated those data
18:19into refine network model and what
18:21we found was at the early and
18:24intermediate network contain more
18:25up regulated transcription factors.
18:27And downregulated in contrast late
18:30network had more downregulated in up,
18:33regulated transcription factors and
18:35interferon induced differentiation.
18:36Involves dominance of the up
18:39regulated transcription factors.
18:40The first 16 hours over here which
18:43then the dominance of down regulated
18:47transcription factors over here.
18:49And just a summary.
18:51So there were dominant transcription
18:53factors that bridge each wave to the next.
18:56So the green circles represent
18:58a transcription factors that
19:00are differentially expressed in
19:02one transcriptional wave.
19:04Where is the purple circles represent
19:07transcription factors that differential
19:09expressed in all transcriptional waves.
19:12So Cal offense tattoo are early
19:15intermediate transcription factors.
19:16Math blimp one?
19:17An MIP are intermediate transcription
19:20factors and stat one hit 1A and T bet or
19:24bimodal transcription factors apart show
19:26this it just to get the bigger picture
19:29of the what nature does in terms of the
19:33biologic complexity of these systems.
19:36So a dear friend of mine,
19:38somebody may know of one of the great.
19:42Textbook authors of immunology.
19:44Abul Abbas would say to me,
19:47in Vivo Baratas and then in vitro maybe.
19:51So the challenge for us was to find
19:54an envy both system which replicate
19:57all this lovely in vitro data.
20:00So.
20:00Like to show you it in Beeville.
20:02Model that we did not develop a nature
20:05developed for us with the viral load.
20:07Strongly correlate with interferon
20:09T cell signature which is COVID-19.
20:12So this is work that is presently
20:14under revision.
20:15That nature communication,
20:16led by a team of individual or for two
20:20at the end where we perform single cell.
20:23Now sis of patients with healthy
20:25controls and various COVID-19
20:27samples of individuals with mild,
20:29severe or moderate severe disease and
20:31basically for the purpose of this talk.
20:34But we found this out as a very
20:36strong correlation between the
20:38interferon score and the viral load,
20:41as measured by PCR.
20:42Nasal swabs,
20:43in fact,
20:44if you look at the correlation time
20:47difference between here and the
20:49respective change interferon score,
20:51we had a remarkable R ^2 .9 seven.
20:54So nature had accidentally given
20:56us a in vivo model of type one
21:00interferons in their effect on T cells.
21:03So if you look at the interferon signature,
21:06it's higher in progressive Covid patients,
21:09his controlled,
21:10stable progressive CD4 CD 8 cells.
21:12One can see that the type one interferon
21:15score went up with more progressive disease,
21:18so then we wish to ask.
21:21Looking at these,
21:22the interferon stimulated T cells
21:24in ex vivo with a similar to what
21:27we saw in vitro with our interferon
21:30transcriptional signature and
21:31the answer is yes.
21:33So here is CD4 cells CD 8
21:36cells this this column.
21:38Here are the controls,
21:39stable and progressive patients.
21:41So we see this module too.
21:43Upregulated these are highly upregulated.
21:46PD one Tim, three CTO for lag three.
21:50Precisely what we saw in vitro in
21:53CD4 and CD8 cells, whereas module 1.
21:58Which led to downregulation again
22:01of TIGIT BTL ACD 160 and such.
22:06So we had a extremely.
22:11Could the recapitulation what
22:13we saw on in vitro.
22:15Here's expression of Co inhibitory receptors
22:17for the controls and COVID-19 patients.
22:20Just to summarize,
22:21here's like 3 going up to three going up,
22:25whereas TIGIT Slam 6 and
22:27layer one all went down.
22:29Similar to what we saw in vitro.
22:34So we looked at the T cells induced in vitro,
22:38which led to with an interferon
22:40score and asked that really
22:41mirrored the transcriptional wave
22:43score aren't dividing covid CD4
22:46and CD8T cells and basically one can see
22:48then dividing CD four and eight cells
22:51that the in vitro interference core very
22:54much recapitulate if we saw in vitro.
22:56And finally we looked at the relation
22:59between regulators that we saw in vivo and
23:02in vitro in this intermediate wave network.
23:05The positive regulated
23:06transcription factors in red,
23:08negative and blue, and we saw that SP.
23:11140 is a bidirectional regulator,
23:13so this is the regulator which
23:16induces lag three and other Co
23:19inhibitory molecules while inhibiting.
23:22Going the opposite direction for ticket.
23:26And then we looked at the relationship
23:28between late faith covid for lag free,
23:30Tim three and PD one and found
23:32that BSL three instaff 3A positive
23:34regulated flag 3 and 10 three.
23:37And finally,
23:38looking directly in patients to the
23:40SP140B cell three and stat three
23:43while elevated in COVID-19 cells,
23:45so we're able to recapitulate what
23:48we saw in terms of induction wisco
23:51inhibitory molecules in vivo in
23:53terms of what we thought on Pedro.
23:56So in summary,
23:57interferon is a major driver of cone
24:00hitori receptor regulation and human T cells.
24:04The computational and biologic
24:06approaches identifies.
24:07Regulatory networks under interferon one.
24:09Responses in human T cells.
24:11There are modules of transcription factors
24:14that control interferon stimulated genes.
24:16Colon,
24:17hip to receptors and interferon
24:19which really highlights the novel
24:21noncanonical transcription factors
24:23beyond the conventional Jack stat
24:26pathways that we previously knew about.
24:28We then demonstrate the relevance of
24:31our in vitro T cell type one interferon
24:34responses by integrating single cell RNA.
24:37See data from COVID-19.
24:39Patients were strong T cell into fair.
24:42One response was observed and
24:45finally we identify SP 140 as a key
24:49regulator that differentiates Lag 3
24:51digit expression during acute viral
24:54infection as well as Aaron Vivo systems.
24:57So let me just acknowledge the individuals.
25:00Again, this truly represents the
25:02work of Thomas Amita.
25:04Here, members of the laboratory
25:06contributed various parts of this.
25:08My long,
25:09long term collaborator,
25:10collaborator PJ Kutru Shadow Bergen is
25:12off Marty and also wondering knowledge.
25:15The covered work led by audio
25:17Untermann with Tomo Jonas Scoop
25:20and enough Tally Kaminski.
25:21So I'll stop there and take any questions.
25:25Thank you.
25:26David, thank you.
25:27What an incredible body of work and
25:30congratulations on sorting through
25:32what is clearly a very complex.
25:35Regulatory system, let me ask,
25:37and this is sort of my concrete question,
25:41which is you know.
25:43Obviously you're sorting through
25:44what's driving expression of Tim.
25:46Three lag, three TIGIT an realizing
25:49that almost the Holy Grail
25:51today is what's the next PD one?
25:54So does this work?
25:56Help us understand the relative
25:58merits of these targets and in
26:00the future of immuno oncology
26:03or give us some insight there.
26:06Great question. I think the short
26:08answer is probably not at one level.
26:10It gives us insight,
26:12so I guess one could ask what
26:14what induces type one interferons
26:17in different tissues and.
26:19And how are tumors so presumably in
26:22tumors are secreting type one interferons.
26:24We know they are and that that may be
26:28influencing the local team environment.
26:30But the reason why I say no is my
26:33suspicion is that each organ has
26:36his own set of regulatory module
26:38for controlling LG cells work.
26:41We just completed an extensive
26:43analysis paper published in Science
26:46Immunology doing a single cell RNA seek.
26:49In T cells from normal spinal fluid
26:51is normal yell graduate students and
26:54see that over 50% of the T cells.
26:57In this DSL or PD,
26:59one positive high expression
27:01digit in three with spontaneous
27:03production of gamma interferon.
27:04So I think each organ and that's
27:07why I showed the Ms GBM data.
27:10I think looking at what is expressed in
27:13tumors compared to autoimmune disease,
27:15which goes the opposite direction may
27:17give us insight as to what is the next
27:21Holy Grail coding inventory molecule.
27:23I think that would be perhaps
27:25the best way of addressing it.
27:28And this is more mechanistic,
27:30and it was surprising because it's
27:32a Vijay kept saying well Style 27.
27:34Can't you find it kept saying?
27:35Well we keep looking and kept saying
27:38what you're doing the experiment
27:39wrong and I didn't show them
27:41picture of Donald but you know,
27:43we just couldn't get it to work
27:44and then we explore different
27:46like going hit or molecules.
27:48And then it's very simple observation
27:50and actually predicted based on
27:51all the viral immunology work.
27:53Yeah, thank you, Ann Habermann has a
27:56question which is how long does the
27:58T cell response to interferon persist
28:01and why would this be a desirable
28:03response during a viral infection?
28:06Well, I I think in terms of
28:09covid there cleared two phases.
28:11The initial phase of the
28:13high interferon response.
28:15We thought the intermediate phase
28:17and then with time disappears.
28:19If one can generate so there really are
28:22these biphasic interferon response?
28:24Is this what nature does to try to
28:27clear clear viruses and we suspect that
28:30one reason why patients do badly and
28:33we're positive that the loss of TIGIT.
28:36Which is induced by interference.
28:38We have persistent high interference
28:40signature leads to a loss
28:42of the mean regulation.
28:44We actually wrote a grant
28:47that supplemental grant.
28:49Hypothesising that Tim three
28:50PD one go up and teacher will
28:53go down in covid patients.
28:55I don't like hypothesis driven science.
28:57It seemed like a long shot and were
29:00shocked to see that was going on.
29:03So so in terms of why be desire response
29:05because indifference help clear viruses.
29:08But then I think it becomes a
29:10less desirable response with time.
29:12And we suspect that will raise the
29:14issue that loss of digit which is
29:17really quite remarkable in these individuals.
29:20May late relate to the hyper mean
29:22response that we see in patients.
29:26Well, David, thank you for a really a
29:29terrific talk and and thank you for
29:31sharing that the work in progress.
29:34It's really impressive.
29:35Let me now turn to our next speaker,
29:38Doctor Hairy Cougar,
29:39who as you all know is is a professor
29:42of medicine and along with Marcus
29:44Bosenberg leads or yell Sporen
29:46skin cancer which were so pleased,
29:48got renewed about a year ago and
29:50continues to be extremely productive.
29:52Harriet's work in the Cancer
29:55Center has been really.
29:56Sort of the triple threat.
29:58Obviously she is a highly.
30:01Respected and highly sought after physician,
30:03but at the same time leader in
30:05research and immunology in Melanoma
30:07and also a leader of our education
30:10program and not many people can
30:12can do all that and do it so well.
30:15Harriet's work I think has really
30:16been instrumental in understanding
30:18the biology of Melanoma.
30:20How do we leverage Immunobiology
30:22towards novel therapies?
30:23And Anne frankly I suspect
30:24willingness to hear about it today,
30:27but her work on metastases
30:28as well has really, I think.
30:31Very insightful,
30:31but Harriet thank you for taking the
30:34time and sharing your work with us.
30:38Thank you Charlie and thanks
30:39for that wonderful introduction.
30:41I'm just going to share my screen here.
30:45So it's always humbling to
30:47talk after David Heffler,
30:48but that was the assignment I received,
30:51so I will do my best here.
30:54So I'm going to be talking to you about
30:57one of the sport projects which focuses
31:00on Co stimulating the the innate immune
31:03adaptive immunity to treat Melanoma.
31:06So just a few fast facts about Melanoma,
31:08so it's a disease of the relatively young
31:11most patients present between age 45 and 55.
31:13The incidence has been going up
31:15actually for decades already,
31:17so just by way of example,
31:19in 2003 there were around 54,000
31:20new cases in the United States,
31:22and just a decade and a half
31:24later it was already up to 87,000.
31:27It's now the fifth most common malignancy
31:29among men and the seventh among women,
31:31but Fortunately most of our patients
31:33present with stage one disease,
31:35so stage one refers to diseases
31:37confined to the skin and is.
31:39Then stage two is confined to the skin
31:41and thicker stage three is disease.
31:44It's spread to the lymph nodes and
31:46stage four is distant dissemination.
31:48And that's essentially what kills patients.
31:50So we're really going to be talking
31:53about stage four disease today.
31:55So for mortality, Interestingly,
31:57it was going up as well.
32:00So for 2000 three 7600 deaths,
32:022017 ninety 700 deaths.
32:04But if you start tracking later on 2019,
32:08the death rate started to go down
32:10for the very first time 7230 deaths,
32:14and the projected number for
32:16this year is 6850.
32:18And this is because of
32:21our improved meta static.
32:23Approved therapies for metastatic disease,
32:25particularly immunotherapy.
32:26And that's what I'm going
32:28to be talking about today.
32:30So we've known for years that some Melanoma
32:33patients are cured by old-fashioned therapy.
32:35If you do a medister tech,
32:37to me,
32:38this is an old series published in 2011.
32:40You can see that eight or ten
32:43years at approximately 5 or 7%
32:45of patients are still alive.
32:47Chemotherapy you actually see
32:48a similar kind of a figure,
32:50and we don't think chemotherapy
32:51really prolongs survival.
32:52Maybe it's just Natural History
32:54of disease that some people live
32:56for a long time.
32:57Now over here on the right you see
32:59the the five year survival data from
33:02our flagship phase three study of
33:04epilim abalon versus nivolumab alone
33:06versus the combination thereof at
33:09where at five years you see 26% of
33:12patients are alive with EPI alone
33:1444% with anti PD one alone and 52%
33:17or maybe even higher than that.
33:20With the combination of the two drugs.
33:23So what we're really trying to
33:25do in the Melanoma field,
33:26especially the drug development field,
33:28is to raise the tennis tail
33:29at the end of the curve.
33:31So this is a figure that I borrowed
33:33from one in Microsoft students, Irina,
33:35who I'll mention as we go along,
33:37just showing that targeted
33:38therapy and chemotherapy.
33:39You're very low down here with
33:41people in Malibu starting
33:42to push up. We're pushing up
33:44further with Anti PD one even
33:46further with the combination.
33:47But really, what we need to do is to
33:49get new drugs and drug combinations,
33:51so hopefully in the next five years
33:53will have a five year survival of 80%.
33:56And eventually we'll reach 100%,
33:58and until then we still have employment.
34:02So what are the limitations
34:04of immunotherapy's,
34:05the Society of Immunotherapy or City?
34:07Which is the big society that Mario
34:11presides over recently formed a
34:13task force to define to provide
34:16some clinical definitions of.
34:18Limitations so firstly,
34:19not all patients respond upfront.
34:20We call that primary resistance.
34:22Then there's some patients that will
34:24respond and subsequently progress.
34:25So we call that secondary
34:27resistance or required resistance.
34:28The third problem that we have is
34:30that we sometimes give combinations.
34:32So for example,
34:33when we give a pill and an urban Nevada map,
34:36we give the two together for
34:38four cycles and then we continue
34:40with Nevada map monotherapy.
34:42So if somebody has a nice response in
34:44the beginning and then 18 months later
34:47when they're on monotherapy maintenance,
34:49they then progress.
34:50Is that resistance to the combination or
34:53is that resistance to the monotherapy and
34:56all of these things need to be defined?
34:59And then how do we define regrowth
35:01after patient stops therapy?
35:02So we normally treat for a
35:04limited period of time being at
35:06one years one year or two years.
35:08However long we treat for specific disease,
35:11if a patient is in off therapy
35:13and then has regrowth,
35:14does that mean they're actually
35:16resistant to the original code?
35:17Because in theory the tumor
35:19should have been gone.
35:20Or are they just dependent on it
35:22and we need to continue so the task
35:25force is starting to define all of
35:28these categories and to come up with?
35:30Specific definitions that can be
35:32used for clinical track for drug
35:34development so that all trials
35:36are designed the same way.
35:37We've started on that,
35:38but we're chipping away at
35:40all of these questions,
35:41and I think many valuable faculty
35:43are actually participating in
35:44this endeavour with concurrent
35:46with the clinical definitions,
35:47we really need to work on the science.
35:50So really,
35:51what I'm going to talk about mostly today
35:53is is translation going back and forth.
35:56So what?
35:56Why do patients develop resistance?
35:58Or many many potential mechanisms
36:00of resistance have been described,
36:01and I think.
36:02You know half of the cancer immunology world
36:04is now working on one or other of these.
36:07So some of the some of these
36:09tumors are just desert rumors,
36:11lack of till of tumor infiltrating
36:13lymphocytes within the tumors you can have,
36:15in effect of priming of your T cells.
36:18We know that defective antigen presentation,
36:19such as bile acid,
36:21beta,
36:21two microglobulin in the tumor
36:23cells will cause resistance.
36:24Sometimes T cells get exhausted
36:26as David just mentioned.
36:27Of course lack of PDL one in the tumor
36:29or in the tumor microenvironment
36:31suggests that we don't live PD
36:34one. Inhibition isn't going
36:35to do very much over there.
36:37And then the other costimulatory
36:39or Co inhibitory molecules
36:40that David just mentioned,
36:42particularly teachers and
36:43Lag 3 might also be present,
36:45and maybe it's just not sufficient in
36:48all cases to inhibit PD one or PDL 1.
36:51And finally there there are many other
36:54immune inhibitory cells that we need to
36:56focus on in the tumor microenvironment,
36:58and sometimes those might just be
37:01overpowering the role of the T cells.
37:03So examples are MD's season T regs
37:06which might need inhibition as well.
37:09So when we started putting
37:11together the renewal of the spore,
37:13one of the projects that we
37:15worked on is specifically looking
37:16at the innate immune system.
37:18So Sucic, when she was here,
37:20provided all of the preliminary
37:22data which I'll be reviewing very
37:24quickly and some sewers left,
37:25Marcus has become a key collaborator,
37:27and actually it's now become a whole
37:29village in the whole party because
37:31all of the investigators and trainees
37:34listed over here on the right are
37:36quite involved in this project,
37:37and I'll mention some of their.
37:39Contribuciones as we go along.
37:42So Sue started off looking at
37:44Marcus is young 1.7 models,
37:46so I'm sure everybody knows that
37:48this is a cell line that was
37:51generated from the from a gym model.
37:54It's byref mutant and P tenancy.
37:56DK into a null and when you take
37:59this young 1.7 and you treated with
38:01anti PD one you see over here there's
38:04absolutely no tumor regression.
38:06If you irradiate the cells
38:08and generated the second.
38:10This tortoise airline called Yammer 1.7.
38:12ER stands for exposed to radiation.
38:14You get some sensitivity to anti PD one,
38:17but ultimately with time these
38:19tumors to grow out as well.
38:22So the first question next to asked was
38:24what was actually in these in these tumors.
38:27So all of this work was done by Kurt Perry,
38:30who's over here on the right.
38:32We can see his picture and he's actually
38:34one of the new fellows that match to.
38:36Our program will be very thrilled
38:38to have him as part of our
38:41medical oncology fellowship.
38:43So first question that they asked
38:45was what was the infiltrating
38:46tumor content in these mass?
38:49In these mass melanomas?
38:50And it turns out that the predominant
38:53cell type was actually terms or
38:55tumor associated macrophages.
38:57The next question that they asked was
38:59what kind of macrophages are these?
39:02Are there more inflammatory or inhibitory?
39:05Classic definition of M1 and M2
39:07and over here on the right you
39:09see a contour plot where on the
39:11X axis you've got F 480 and the
39:13Y axis you've got like 6 E.
39:16It turns out that there at
39:18least three populations,
39:19and they're probably more than that,
39:21and just in a nutshell,
39:22the terms that have highlights 6,
39:24three like 6 E and low EF 480,
39:26or those that are more inflammatory
39:28in the ones on the right over here
39:30are those that are presumed to
39:32be more inhibitory.
39:36So at that point they said, OK, we've got.
39:39We've got these terms.
39:40We need to try to modulate them,
39:42and there are many, many mechanisms
39:43out there for modulating terms.
39:45But the ones that they chose to
39:47work on with CD, 40, agonism,
39:48and CSF, one R inhibition,
39:50and in the beginning they used
39:52a small molecule inhibitor.
39:53So if you take these memory
39:55cells and implant them in mice,
39:57and you treat either with control vehicle or.
40:00The CD 40 agonist.
40:02You'll see some some decrease in
40:03the size of the tumors with the
40:05CD 40 agonist if you give the CSF
40:08one receptor inhibitor you get a
40:09similar amount of tumor reduction.
40:11If you give the two together,
40:12you get synergism.
40:13As you can see by the red line over here.
40:17So to look back into the similar
40:19contour plots,
40:20what is the content of these different
40:22tumors within the mice treated in the
40:24graph over here on the left you can
40:27see that when you give doublet therapy,
40:29the CD 40 agonist in the CSF
40:31one receptor inhibitory,
40:32the main difference is that you get
40:34an increase in this little group over
40:36here on the right in the upper corner,
40:39which are like 60 high and in 480 low and are
40:42presumed to be more inflammatory macrophages,
40:44and that's essentially
40:45verified on the bar graph.
40:47Over here on the left.
40:49On the right,
40:49at the bottom over here you can
40:52see this to the changes in the in
40:54the immune infiltrating content,
40:56and I think what's most interesting
40:58over here is that when you give
41:00the CD 40 agonist along with
41:02the CSF one receptor inhibitor,
41:04you do get an increase of
41:06infiltration of T cells.
41:07So possibly we might be able to make
41:09desert those desert tumors more
41:11inflamed by using a regimen such as this.
41:14And in addition you get more
41:16PD one high T cells.
41:20So Catherine Miller Jensen on the main
41:22campus is developed a technology for
41:24single cell site eccentric creation,
41:26and she looked at what the difference of
41:29was between these different treatment
41:31arms and what you can see here on
41:34the principle component analysis.
41:36On the left is that if you only treat with
41:38assistive one receptor inhibitor versus
41:40the city for The Agonist inhibitor alone,
41:44versus the combination,
41:45you get quite a different pattern
41:47of cytokine secretion on the right.
41:49Oh, I'm sorry in the middle over here,
41:52you've got a heat map which we
41:54essentially depicts the differences,
41:55and some of them are highlighted over here
41:58on the right for cytokines and chemo kinds.
42:00Pretty much as as one would expect
42:02when you give the combination therapy,
42:05you get an increase in TNF Alpha.
42:07I'll 12 BIL 6 etc and the same
42:09for the panel of the side of kinds
42:12of the chemo kinds at the bottom.
42:14So essentially the doublet therapy
42:16over here is inducing quite quite
42:18vast changes in the animals.
42:19What does it do to the T cells?
42:22What else is important over here?
42:24What you see on this figure here is
42:26that when you give the doublet therapy,
42:29you can actually abrogate the
42:30effect if you give anti TNF Alpha
42:32or anti interferon gamma,
42:34again highlighting the the importance
42:35of the T cells in this process as well.
42:38So with that at the time we concluded
42:40that CSF one receptor inhibitors in city
42:43for The Agonist treatment can induce
42:45an inflammatory term population in
42:46the two in the tumor microenvironment.
42:48It also induces a functional T cell response.
42:52And this is dependent on TNF Alpha
42:54and interferon gamma,
42:55and these were the preliminary data
42:56that we had to start our project.
42:59So when we received funding,
43:00we by then Curtis Perry had gone
43:03off for residency.
43:04So Bill Dembski came in to help
43:06us and you'll see a whole cast of
43:08trainees along the way over here.
43:10So Bill Bill did a heroic job over here
43:13with bringing us closer to the clinic.
43:15So we decided at that point not to
43:17use a series of 1 receptor inhibitor,
43:20the small molecule inhibitor,
43:21but rather to move towards and.
43:23Antibody because of precision
43:25of drugging our target.
43:26Also in the clinical arena,
43:28it would be very difficult to take
43:30a patient who progressed on a PD one
43:32inhibitor and not to continue the PD
43:34one inhibitor with the next regiment.
43:37That's essentially how most regimens
43:38are now being developed for Melanoma
43:40and renal cell as well.
43:42So the question is what can we add onto a PD?
43:45One inhibitor to get us there so
43:47he these are large groups of mice
43:49treated either with control vehicle,
43:51either one of the three drugs alone
43:54so anti PD one.
43:55CD40 agonist or CSF one receptor.
43:57Any doublet of the from among
43:59those three and the triplet,
44:00and you can see by the Brown line
44:03over here that by far the triplet
44:05therapy was superior on the
44:07right you see the spider plots
44:09for the size of these tumors,
44:11which in the beginning
44:13they'll grow and then shrink.
44:15Irina clickbait ever.
44:16Who's MD PhD student who is in Marcus's
44:19lab at the time or selection Marcus is
44:21lab did similar experiments on aranka
44:23model because we wanted to go into
44:26the clinic in kidney cancer as well.
44:28Again, showing their triple therapy
44:30was superior to double therapy.
44:31Not quite as pretty as
44:33in the Melanoma models,
44:34but that's then that's consistent
44:36with what we see in the clinic,
44:38whereby renal cell patients respond less well
44:41to these therapies then Melanoma patients.
44:43So because it's a sport project,
44:45you have to have a clinical Pi and
44:46a basic science Pi and everything
44:48has to have a clinical trial so
44:50to go back to the bedside.
44:51What are we going to do with these data?
44:54So we formed collaborations with Bristol
44:55Myers Squibb and a company called a passage
44:57and that makes a CD 40 agonist and we
44:59were able to get them to work together.
45:03The problem was that there was no
45:05phase one data for the triplet.
45:06Now could be oralism AB which is the
45:08CSF one receptor antibody and the
45:10volume Abbott being given to hundreds
45:13of patients in BM S LED studies in
45:15the activity in Melanoma was modest,
45:16but there was a little bit of
45:18activity at that point.
45:20We knew that a CD 40 agonist can have
45:22significant activity in Melanoma
45:24based on studies by done by the
45:26group at Penn already years ago.
45:28But we didn't know very much
45:30about the other combinations,
45:31so at the time sterilize,
45:33brought in a Phase 1 two study of APX.
45:36005 AM.
45:37In other words,
45:37the CD 40 agonist plus nivo in
45:39Melanoma and lung cancer started at
45:41around that time and we rolled a
45:43good number of patients there and
45:46actually saw phenomenal responses.
45:47So this is an example of a patient
45:49treated by doctors know who had
45:52a mucosal Melanoma,
45:53which tends to be very resistant
45:54to implement map in the volume.
45:56Evan the patient indeed had
45:59progressed on there.
46:00So we put the patient on the CD
46:0240 agonist plus nevala mehrban.
46:04The patients had a complete response
46:06and remains of therapy couple of years
46:08later we have four of these patients
46:09and others and implement Melbourne Nivolumab.
46:12We don't actually see this,
46:13so maybe this is the answer to Charlie's
46:16question is what's the next anti PD?
46:18Why?
46:20So we're very excited about this
46:22molecule and with that Sarah Weiss.
46:24This picture over his over here and I
46:27wrote a Phase one slash 1B or phase
46:30two for the combination of the triplet.
46:32We partnered with the yellow Spore
46:34in lung cancer and we were able to
46:37get support both from the Cancer
46:39Center Bristol Myers and Apixaban.
46:42So the phase one trial design is
46:44depicted on this picture over here.
46:45In the beginning we were very
46:47anxious because nobody had
46:48ever given two macrophage modulating
46:50agents together and we were worried
46:52that we were going to get like
46:53diffuse macro activate macrophage
46:54activating syndrome and kill patients.
46:56So we had to go very very gingerly.
46:58We will also working with
46:59two pharmaceutical companies,
47:00each with its own opinion so it
47:02could be oralism AB which was being
47:04developed by Bristol Myers Squibb
47:05dead already did it already defined
47:07the recommended phase two dose and
47:09we had to stick with the dose that
47:11they gave us which was for me.
47:13Ramza, kilogram.
47:14We escalated the CD 40 agonist very gently,
47:17so cohort one only had the doublet therapy
47:20at a tenth of the recommended phase.
47:23Two dose for the CD 40 agonist within
47:26escalated by a half a log into cohort
47:29three in Cohort 5 and concurrently
47:31added the nevala map on with the goal
47:34of ultimately reaching cohort six,
47:36which would be 4 doses at the
47:38record for of Cabrera.
47:40Lismer,
47:41the pic surgeon drug and nivolumab at the.
47:44Same recommended phase.
47:45Two dose of each one of these individually.
47:49Once we get to the Cohort 6 or to
47:51the recommended phase two regimen,
47:53the plan is to go into.
47:57The Phase 1B component,
47:58which is which is essentially
48:00three phase two studies,
48:01each one with its Simon phase.
48:03Two design, one per disease.
48:06At this,
48:07this trial has lots of embedded correlates,
48:10both blood based and tumor,
48:12based with pretreatment biopsies
48:13mandatory on treatment,
48:14biopsies etc.
48:15So to update you on what's going on
48:18with the Phase one trial which is an
48:21integral part of the sport project.
48:24We have completed the Phase 126
48:26patients in total have been
48:28enrolled sarahs busy preparing the
48:30publication for this and that should
48:32be going out over the coming weeks.
48:35Overall it was reasonably well tolerated.
48:38It certainly wasn't candy,
48:39though we saw a lot of periorbital
48:41edema as well as diffuse edema
48:43elevations in CPK AST and a Lt,
48:45but those didn't appear to be
48:47particularly clinically significant.
48:48Fevers Insider Kind release,
48:49but a lot of fatigue.
48:51I think that was the biggest problem.
48:53The other big problem that
48:55we saw was skipped.
48:56While there was some activity
48:58in some of the patients,
48:59it was mostly stable disease in
49:01progression of disease and not
49:03quiet what we've seen in the mice.
49:05The trial has preceded to the Phase 1B
49:07component in Melanoma and lung cancer.
49:09Both are in the first stage,
49:11but we've we've completed the phase one.
49:14I'm going to show you some examples
49:16of correlative studies that we've
49:18done and this is still a bit
49:20of a work in progress,
49:21so we looked at cytokine panels before
49:24and on treatments at 24 hours later,
49:26and you can see nice increasing interferon
49:28gamma as well as in in TNF Alpha.
49:31The different cohorts are listed over here,
49:33but Code 5 and six are when we hit
49:35them at the recommended phase,
49:37two dose of deep excision drugs,
49:39so that's where you see most of the activity.
49:44There are other changes in circulating
49:45cytokines and I could spend an
49:47hour just talking about this,
49:48but I selected a few just just
49:50to show you what we're seeing,
49:52so we've got the CL 2,
49:54which is a side kind that's primarily
49:56secreted by dendritic cells and macrophages.
49:57Very high levels of the higher dose levels,
50:00same with. P.
50:0110 and then the macrophage
50:03colony stimulating factor,
50:04also highest levels in Cohort
50:076 but clear increases.
50:09Across the board,
50:10we do have the pretreatment and on
50:12treatment specimens show me jessel
50:13who supposed dark in my lab is
50:16busy analyzing these what you see
50:17over here is the basic analysis,
50:19so these are just this is just a
50:21munificent staining a CD4 and CD8
50:24before treatment and on treatments
50:25on the left is pre and on the right
50:27is post and you can see an increase
50:30in the infiltration of the CD 8
50:32cells which are colored in green.
50:34There's also an increase of
50:35the CD Force which are in red.
50:37CD 68 also actually.
50:39Increase in the amount of CD
50:4168 on this particular patient,
50:42but in some patients we actually
50:44see the opposite,
50:45so over here you can see that the
50:47C8 cells pretreatment were much
50:49more dense than post treatment.
50:51Although you do see some post treatment,
50:53I don't know how well this projects.
50:56There's an increase in the CD 68 though.
51:00Just to highlight one of the challenges
51:02that we have with doing this.
51:04Pre Anon treatments studies in
51:05that it may not come from this
51:07come from the same site,
51:09so the pretreatment was a a containers
51:11tissue metastasis on the back and
51:13the post treatment in this particular
51:14patient came from the Gallbladder,
51:16so it's possible that the tumor
51:18micro environment in the different
51:19organs is playing a part over here.
51:21But because we didn't see much
51:23activity in the Phase one trial,
51:25we're going back to the bench
51:27to try to determine what can we
51:29do to improve our trial.
51:31So Irina clickbait ever,
51:32who was the postdoc working?
51:34I'm sorry there's the doctoral
51:35student in Marcus's lab,
51:37partnered with Deanna,
51:37who's working in my lab to ask the
51:40question of whether we're actually just
51:42giving too much CSF one receptor antibody.
51:45So more isn't always better,
51:46particularly when we're trying
51:48to polarize macrophages and not
51:50necessarily knock them off completely.
51:52So when we do these experiments in the mice,
51:55we were seeing much better
51:56activity than the humans,
51:57and the question is why?
51:59So the dose is selected for the Marin
52:01experiments with somewhat random we go
52:03based on what is done by other researchers,
52:05what's done by format and the amount that
52:07we were giving them was 200MG kilogram.
52:09So we asked the question.
52:11Well,
52:11what happens if we give them more CSF?
52:13One receptor antibody and keep
52:15the other two drug steady?
52:16And as you can see in this figure over here,
52:19if you give more CSF,
52:21one receptor antibody basically
52:22doubling the dose.
52:23The mice actually do less well
52:25die sooner or sacrificed sooner,
52:27and as you can see here on the left,
52:30the tumor volume is actually bigger
52:32when you give the higher dose of
52:34the CSF one receptor antibody.
52:36So we're still debating what to do
52:38about that as we go into the clinic.
52:41But then the Meanwhile,
52:42because it's a small project,
52:44we still need to have an ongoing
52:46clinical trial, and the question was,
52:48well, is the CSF one receptor
52:50the optimal second target,
52:52in addition to CD 40 agonist
52:54and PD one inhibitors?
52:56So it's possible,
52:57at least theoretically,
52:58that CTA for is a better target because
53:00CTA for new mission is is really
53:03key for dendritic cell activation.
53:05So Kelly Alina,
53:06who's one of our wonderful
53:08surgeons in the Melanoma group
53:10and also surgeon scientists,
53:11is doing work in the lab.
53:14It, primarily Marcus is lab where she
53:16is taking a very aggressive model
53:18marine model whereby she injects
53:20these cells into the left ventricle.
53:22So they developed vast mistake
53:24metastases all over,
53:25including in the brain.
53:27And this model is particularly resistant
53:29to anti PD one in Antici TLA 4.
53:31So the question is whether the addition
53:33of the CD 40 agonist adds something.
53:35And as you can see over
53:37here with the red bar,
53:39the addition of the CD 40 agonist
53:41does appear to improve the survival
53:43of these nice that typically
53:44will be dead within 20 days.
53:46This is some subq injection
53:48data over here on the left,
53:50which we don't have time to go through,
53:52but with those data we again
53:54approached the passage and we said,
53:56well, maybe we should do a
53:58different trial now in parallel,
53:59and this is our second trial which Kelly
54:02and Sarah worked with me to to write.
54:04So it's a phase one study of the CD 40
54:07agonist in combination with epilim urban,
54:09the volume app in Melanoma.
54:11So just to highlight some of the
54:13challenges of a study like this,
54:15we know that a polymer mabona volume
54:17app toxicity rate of at least 6570%.
54:20We're talking about these immune
54:22related adverse events all the time.
54:25And we also know that sometimes
54:26these events occur late,
54:27so you can have a patient who
54:29is treated comes off therapy,
54:31and six months later develops
54:33a horrendous toxicity.
54:34So how long?
54:35How do we design a study like that?
54:37How long can we follow the patients?
54:39For how long do we go from one
54:41cohort to the other?
54:42So it took a lot of negotiation
54:44back and forth with the FDA,
54:46but we put a lot of thought into this
54:48really slow trial design where we
54:50actually have only two dose levels,
54:51so dose level one is a.
54:54Third of the recommended phase.
54:56Two dose of the seat of the CD 40
54:58agonist which is the drug that we're adding,
55:01and we give people a map in the volume AB.
55:04We only treat three patients.
55:06Monitor them for 28 days and
55:07then and then enroll
55:09another 46 and at that and
55:10all of these six patients.
55:12They need to be monitored for six
55:14weeks so this is going to take
55:16us a long time to get through.
55:18But what we're hoping is that we'll have
55:21a regimen that may not be more toxic,
55:23but that will be significantly
55:25more effective.
55:25Then the PD one and see TLA for that.
55:28We have right now to finally bring
55:30that tail of the curve up to 80%.
55:33We have started.
55:34We've enrolled three Melanoma patients
55:36or have completed their 28 day DLT
55:38period and they did OK with there,
55:40but they have not all completed
55:42their nine week observation.
55:43Before Christmas, we going to enroll.
55:45Two more patients have consented and
55:47we're looking for the six patient,
55:49but they all have to be monitored
55:51for 9 weeks before we can proceed.
55:54So I'm going to conclude there that
55:56Co targeting the innate and adaptive
55:59immune system with the CSF one
56:01receptor inhibitor or antibody plus
56:02CD 40 agonist results in better anti
56:05tumor activity than either alone.
56:07It also increases the CD 8 tumor
56:09content in animals if we treat
56:11mice bearing PD one resistant
56:13tumors with all with these drugs
56:15in combination with anti PD one,
56:17it does look better than the doublet.
56:19The findings were confirmed in a renal
56:22cell carcinoma model where we are
56:24in the clinic already testing this.
56:26We're having some difficulty with.
56:29With insufficient activities,
56:30so we're back in the lab right
56:32now trying to modify the doses
56:33in the regimen before we go back
56:35again into the clinic,
56:36and this kind of back and forth between
56:38the lab in the clinic is something that
56:41can only be done at a place like this.
56:43We are also at the same time evaluating
56:46the combination with the CTL A4 inhibitor
56:48and hopefully this will be as exciting,
56:50more exciting and just to
56:51say the final conclusion,
56:53that is that it really takes a
56:55village to do a project like this.
56:57So all of the the folks have been
57:01involved acknowledged on this slide.
57:02The scientific collaborators at Yale,
57:04colleagues in other labs have
57:06helped a lot through this process.
57:09Members of my lab members
57:10of the Collaborating lab,
57:12clinical collaborators,
57:13pharmaceutical collaborators,
57:14patients and their family,
57:15and then finally the funding.
57:17So I did mention the sporting skin cancer
57:20which which is funded the core project.
57:24But the K12 is funded a couple
57:26of the investigators here,
57:27Kelly Alina and Sarah Weiss,
57:29and Cancer Center has supported it,
57:31and some of our folks of which
57:34have received career development
57:35awards as well related to this.
57:38So with that I'll stop.
57:39I'm happy to take any questions.
57:41Thank you for listening.
57:43Hurry, thank you.
57:44What a great example of translating
57:46science into the clinic and folks can
57:49certainly submit questions online.
57:51So let me I have a question watching
57:53'cause I you sort of anticipated my
57:56question by adding the CTA four antagonist.
57:59But to what extent do you think that
58:02triplet might have had greater benefit if
58:05they weren't previously exposed to a PD?
58:08One antibody?
58:08And that's really good
58:10question. So the masks were
58:12not exposed to PD one antibody,
58:14whereas the humans would.
58:16And it's possible that you know,
58:18we've we've just used that
58:19app and developed it yet,
58:21and you're of mechanism of resistance,
58:23so we haven't done that
58:24experiment in the mouse.
58:25But that's actually a
58:26really good next step to do.
58:28It's a great thought.
58:29We should expose the mice to
58:31PD one inhibitors and then
58:32add on the other ones instead
58:34of giving all three up front.
58:36And this may be impossible,
58:38but is there any consideration of
58:40combining all four agents in previously?
58:43I mean that is a CSF one R CD40 anti CD L4,
58:47GTA 4 and PD one and I realized
58:50that's a smorgasbord of agents,
58:52but is that a conceivable approach?
58:54We could, we just got it.
58:57We can get through the 1st 3 first,
58:59so the CTA for CD for D and P1.
59:03So far we're doing OK with toxicity.
59:07But we are only on the 1st dose level.
59:09It's it's very intimidating
59:10to do all of this sure,
59:12and then the other question
59:13is in what line do you do it?
59:15So what we're trying to do now is to
59:17actually move it forward to the first line,
59:19that that very last trial that I
59:21showed with the CTA for antibody.
59:23We decided to go in first line.
59:26Mostly because of of memory.
59:28So if you if you take patients
59:30with her previous settling for,
59:32you can get additive toxicity over there.
59:37But that's a really good idea to do that in
59:40the mouse. Thank you.
59:41Yeah, well, I know where I
59:43know we're just we're out of.
59:45We're a little past the hour and I want
59:47to be sensitive to everyone's time.
59:50So Harriet and David.
59:51Thank you both for really exceptional talks.
59:53Congratulations on all your
59:54work and everyone in attendance.
59:56Thank you for joining us and enjoy your day.
60:00Thanks. Bye bye.