Pathology Grand Rounds, January 25, 2024 - Ignacio I. Wistuba, MD

January 31, 2024

Information

Ignacio I. Wistuba, MD, of the University of Texas MD Anderson Cancer Center, presenting on, "Spatial Immune -Profiling Analysis of Lung Cancer" at Yale Pathology Grand Rounds.

ID11238

To CiteDCA Citation Guide

00:06Good afternoon, everyone.
00:07Thank you so much for coming.
00:10I'm very excited about our,
00:12about our speaker today and
00:14it's my great honor to actually
00:17introduce Doctor Ignacio Bisuba.
00:20He told me just to call him Ignacio.
00:22We know each other for some time now.
00:24Yeah, it was really 180 pages long CV.
00:28So I'll try to put it in
00:30like 3 minutes, 4 minutes.
00:32So actually he can speak to us
00:34about his wonderful research.
00:35So Doctor Ristuba is currently Professor
00:37and chair of the Department of Translation,
00:40Molecular Pathology at the
00:43MD Anderson Cancer Center.
00:46He's also has a junk appointments
00:48as a professor at the Department
00:50of Thoracic Head and Neck Medical
00:52Oncology and he's Co Director of
00:54Division of Pathology Lab Medicine.
00:56He started his career in
00:58a medical school in Chile.
01:00And actually I think Kurt and doctor we
01:03still announced each other from back then.
01:05So which is wonderful he did his
01:09pathology training in Chile.
01:11Then he did his post doctoral
01:15pathology research at UT Southwestern
01:18Medical Center and Hammond Center for
01:22Therapeutic Oncology Research Center,
01:23also at UT Southwestern.
01:25He was boarded in Anatomic Pathology in 1989.
01:31He had the numerous academic and
01:33administrative appointments,
01:34and I'll just briefly go through these.
01:37He started actually his career
01:38as assistant professor up to
01:40associate professor in Chile,
01:42Catholic University of Chile, Santiago.
01:44And then in 2003, actually,
01:48he moved to MD Anderson,
01:50where he was associate professor until 2008.
01:53And in 2008 he became a
01:55professor of pathology.
01:59In terms of like his
02:01administrative responsibilities,
02:02there are numerous leadership positions.
02:05So I highlight just a few of those.
02:08He was a director of the Utilonga SPORT,
02:11Tissue Banker,
02:12director of Thoracic Molecular pathology,
02:15also at MD Anderson.
02:17Up till now actually he was Co
02:20director of the Cancer Center,
02:22supportive grant Co director of
02:24the molecular testing development.
02:26And I think I'm going to stop here, Ignacio.
02:28It's really long list.
02:30It's absolutely impressive.
02:33And he also had endowment positions,
02:36numerous 'cause consultations
02:42over 800 publications.
02:44Your CVS from September,
02:45I guess you're probably 1000 right now.
02:47With that pace and numerous awards,
02:51he received Pathology Residency
02:53Scholarship award in Chile
02:56Fogarty Foundation Fellowship.
02:58He also received the Mary Matthews
03:01Pathology Translational Research Award
03:04from the International Association
03:05for the Study of Lung Cancer in 2018.
03:09At the same year he received the the
03:11the award from the Latin American
03:14Federation of Cancer Society's Award
03:17for Supporting Latin American Oncology.
03:20I had the privilege to work with
03:23Ignacio as a member of the ICLC
03:26Pathology Committee when he was a chair.
03:29We were members before them but then
03:31he was a chair and he absolutely
03:33changed the entire committee,
03:35made it actually the most productive
03:37committee of the ICLC.
03:39And he's one of those natural born
03:42leaders with great administrative skills,
03:44great research skills and also
03:46he's still pathologist.
03:48I know that he can read the slides.
03:50So Ignacio, welcome.
03:50We are very happy to have you
03:53here. Thank you very much. Thank you,
03:54Sanjay. Thank you Doctor Basic and Doctor
03:56Liu for the kind invitation to be here again.
04:00I come to institution on a regular basis
04:03because I I have a collaborations and
04:06part of advisory war of the Yale Lancaster
04:10sport program and congratulations
04:11I heard that was submitted finally
04:14today and and now I mean I add to
04:17the list of friends Sandra here.
04:20So it's a pleasure to be speaking
04:22to all to you all. So yeah,
04:25it's a little bit embarrassing hearing about
04:28what you what you have to put in your CV.
04:31Sorry about that.
04:32I'm just being that I'm old, right.
04:35So and and the other thing is
04:38that sorry about the very generic
04:40title that I use for every, every,
04:43every invitation to speak and then I
04:44change it to make it more relevant,
04:46but I forgot to do this time.
04:48What I'm going to talk today is about
04:52using strategies to study immune
04:54response in tumor tissues with emphasis
04:57on Multiplex assays to understand
04:59the immune response with a focus on
05:02a spatial analysis that we have in
05:05exploring our group for the last few years.
05:07So these are my disclosures,
05:09ground support Advisory Board speaker
05:12engagement and also the other you know,
05:15important disclosures that I'm not
05:17an immunologist.
05:18So when I talk about immune markers,
05:20but if if you have a difficult
05:23question immunology,
05:24I'm going to refer them to court.
05:26Thank you for coming.
05:28So
05:31so I mean if you have been in touch with
05:34this happening pathology for lung cancer
05:37particularly in the diagnostic field
05:39including molecular pathology tools.
05:41You have seen this evolution from
05:44Histology based diagnosis to molecular
05:47targeted some classification in
05:49particular the Lenocarcinoma, mystology,
05:51the non sponsor lung cancer area and and
05:54then and then there is a has been a lot,
05:57a lot of hope on more biomarker can
06:01predict response to immunotherapy
06:02particularly the use of immune checkpoints.
06:05But we're kind of stuck there
06:07with a few markers mostly PDL one
06:10in most of chemical expression.
06:12But there is a also a new wave of therapy,
06:15the antibody drugs conjugate that
06:17actually may require the assessment
06:19of protein expression in tumor.
06:20So this is a very kind of brief
06:24description of the evolution of the
06:27field of diagnosis in lung cancer
06:29that makes this disease very exciting.
06:32And I've been there from the beginning
06:35and and I've been able to watch this
06:38by working at the same time this
06:40evolution and this is slide that I put
06:42the paradigms in evolution lung cancer,
06:44molecular pathology.
06:45I'm running out of space now
06:47I'm just reducing the font.
06:48So Histology is the key you know component
06:52of every good diagnosis in this disease.
06:55But we have a targeted therapy
06:57and so we we studied driver by
07:00molecular analysis of tissue cells,
07:02cytology specimens and also have the liquid
07:05biopsy opportunities on immunotherapy.
07:07Unfortunately we don't have a large
07:10number of solid predictive biomarkers.
07:12So there is a lot of effort in this
07:16area and I'm going to show you some
07:17of the work that we are trying.
07:18We're doing that field.
07:20Then many of the new therapy particularly
07:24immunotherapy related approaches
07:27have been moved from stage 4 advanced
07:31disease sometimes refractory disease
07:34to earlier disease patient with tumor
07:37that can be resected surgically with
07:40creative intent stage one and three.
07:42And then therapy has been given before
07:45that recession is called new value of
07:48therapy and that actually put pathology
07:50again in a good position because the
07:52assessment of response after this
07:54therapy when the tumor resect is important.
07:56And Doctor DASI has LED several of
07:59these studies having recently published
08:01on assessing pathological response
08:03and then we have the opportunity to
08:06potentially follow patient with a
08:07liquid biopsy approaches to assess
08:09minimal residual disease to understand
08:11you know patient records.
08:13They already mentioned the biomarker
08:15for the drug conjugate.
08:16But for this kind kind of revolutionizing
08:20a little bit our assays,
08:23assays on biomarker particularly
08:24the immune space is the opportunity
08:28to apply computational pathology
08:30analysis to our immune base,
08:34characterize tissue based characterization
08:36of the immune response and then
08:39I'm going to talk about that.
08:41So these are the two areas that
08:43I'm going to cover.
08:44And as I already mentioned you know
08:46we in pathology particularly in
08:47advanced disease we have a very kind
08:50of defined workflow what's needed for
08:52the diagnosis starting with Histology,
08:54molecular you know assessment of key
08:58genomic and normality,
08:59some PD L wading muscle chemistry for
09:02for all the changes and opportunities
09:04in immunotherapy considering
09:06immune checkpoint inhibition in
09:08combination or in combination with
09:10other drugs like chemotherapy or
09:13a cell therapy or even vaccines.
09:16Approaches are coming up again.
09:18I think they're having just one by a
09:20market for predicting response is very,
09:22very, very dismant and the field
09:25is big and I know that several
09:28of you may have immunology,
09:30immunology training is a is is a
09:32is a lot going on in the immune
09:35response particular to a tumor
09:37without intervention changes.
09:39When you do intervention with immune
09:42modulators or with any other therapy,
09:45chemotherapy, biotherapy,
09:46they change the immune response in tumors.
09:50And there are a series of biomarker
09:52that have been proposed that are part of
09:55the intrinsic characteristic of the tumor,
09:57many of them genome,
09:59genome,
09:59genomic normality is the tumor or
10:02associated to the immune response
10:04that are extensive predictor that
10:06have been considered associated
10:08potential with benefit or with
10:11resistant to our therapies mostly
10:13immune checkpoint therapy.
10:14So we need biomarkers and there's
10:17a crowded space here on different
10:21opportunities and but this in my
10:23bias of pathology I think that I
10:25would have done with genomic that
10:27we started characterizing tumors and
10:30then we know what we need to look for.
10:32We can go to liquid biases and
10:34trying to identify these biomarkers.
10:36I think that then the same paradigm
10:40needs to be replicated in in in
10:43immunotherapy understand what is
10:45the work play of the book play of
10:48immune response with and without
10:50intervention hopefully on log you
10:52doing on basis in the tumor tissue
10:55in the context of immunotherapy
10:56when we learn that we can see we can
10:59identify those cells, those proteins,
11:02those even genomic abnormalities
11:05in surrogate specimen like blood
11:08a sample that we get easily in
11:11every clinical trial.
11:12So all these prompted to pathology
11:15translational research team that we
11:18are actually working very heavily on
11:21the molecular targeted area to try to
11:24understand and learn about immunology,
11:27immunology of cancer.
11:28So we tried to adopt A new technology
11:31to study chemokine cytokine growth
11:34factor in in fluids try to you
11:37know identify the immune cells into
11:40more or peripheral blood or other
11:43fluids with different immunology
11:45immunology techniques for cytometry
11:47site of and try to add to the genomic
11:51characterization of the tissue
11:52immune characterization And then
11:54we can use some genomic approaches
11:57there to get close to that.
12:00We can disaggregate tissues and do
12:02for cytometry site of but use lose
12:05the context of the tissues trauma
12:07malignant cells blood vessels and
12:11other the structure the structure
12:13that could be there by.
12:15So then the analysis of tissue
12:18sections we think is important and
12:21then we can adopt from starting
12:25with a single Plex immunochemistry
12:29with rigor of because that could
12:32be a problematic sometime.
12:35The immunochemistry is it has to be
12:38well done moved from a single to a
12:42uniplex A monochemistry to a Multiplex
12:45approaches to kind of not only quantify
12:49cells that characterize well those
12:52cells and and and actually locate
12:54them in the structure of the tumor.
12:57And then further in the analysis
12:59to see the relationship of
13:01specific suburblation cell to the
13:04malignant cell and to each other.
13:07And that's actually the evolution
13:08from automated from immunos to
13:11chemistry to Multiplex incorporating
13:13image analysis and spatial analysis.
13:16And these are two faculty in our
13:19department of Edwin Para and Luisa Solis
13:21who have instrumental over the years to
13:24develops a similar Multiplex approaches.
13:27We started after testing similar system
13:31adopting the the system called those
13:34days Vectra now is Polaris that have a
13:38very strong chemistry for a multiplexing
13:41different antibodies in tissue.
13:43Now I think that they can do up to
13:459 different antibodies where we save
13:49one spot for the nuclear staining
13:54ADAPI and it's important as I said
13:58to validate these Multiplex very,
14:00very carefully with single Plex
14:03monochemistry followed by single
14:05Plex immuno fluorescent and then put
14:08it in combination in the sequence
14:10that you find best result.
14:12We have done this over the last seven years,
14:16a year.
14:16We have over 30 panels that
14:18will have developed,
14:20but the panel that will have used
14:21the most are the two initial panels.
14:23I'm going to describe briefly because
14:25I'm going to show some data in lung
14:28cancer cohort with this spanning one
14:30that they define it usually speedy
14:331PD1 centric because we look for
14:35the expression of the markers in
14:38malignant cells characterized by
14:40Pancytokeratene and we have some
14:42other T cells and macrophage marker,
14:45very basic markers.
14:46And then the second panel is a panel
14:49that we explore a little bit more the
14:51T cell population of cells and we
14:53keeping you know the pancytokeratine.
14:59So of course like you have done here,
15:02we have explore some other Multiplex
15:06approaches that could be more suitable
15:09for discovery approaches in this field
15:12that can go to up to 4060 markers,
15:15explore the imaging mass
15:18cytometry Iperion site of system.
15:21We have a 35 markers panel that
15:23we have run in some projects.
15:25We have actually working actively
15:27with colleagues and now it's called
15:30Phenocycler fusion that we have a panel
15:32of 33 markers and we are very actively
15:35working with something called Nanoting
15:37GMX that you can do also for the panels.
15:41Some of these are fluorescent based,
15:43some of these are mass spec based.
15:45I'm not going to talk about that
15:47and maybe because we got one,
15:49it's not working.
15:51So then of course everybody's
15:54excited with the opportunity to do
15:56transcriptome analysis in tissues.
15:58I'm talking about formally fixed paraffin
16:01embedded specimens and add the special
16:03transcript official analysis of it.
16:05So this is something that we are doing
16:07and I'm going to show any data about that.
16:09And all these actually effort of
16:13characterizing the immune response
16:15in tissue specimens is has been
16:18very much enhanced for with the use
16:22of computational pathology tools.
16:24And I know that everybody likes
16:25to talk about AI,
16:26people jump to it very easily.
16:28I I think for pathologists,
16:30we have a process here which is
16:32actually digital pathology scan
16:33stuff and scan them well, right.
16:35And there are challenges there that
16:38are not trivial then do image analysis,
16:41right.
16:42That's the next step and then
16:44learn from that,
16:45that's the machine learning and
16:46then you can start talking about
16:48the AI and this concept.
16:49Computational pathology has been very
16:51useful because we as pathologists
16:55are very good and they have been
16:57shown many times to identify
16:59expression of markers and with
17:01chromogenic immunostock energy that
17:02they are locating the nucleus,
17:04they're locating them in the membrane.
17:06The cytoplasm or a combination in
17:08Malina cells have a good eye for that,
17:11especially the large cells like carcinomas.
17:14But it's a challenge to identify
17:16those markers when they express in
17:19immune cells that you see as the
17:21dot and you don't know what type of
17:23immune cell even some macrophages are
17:25challenging sometime the middle of
17:26the tumor is you don't know if it's
17:29the malignant sort of macrophage.
17:30So in that and also quantify that is
17:33very subjective and there are data
17:36that actually with David published
17:38on that right and I just remember
17:40I should have put the slide on it.
17:42How about we are quantifying immune
17:45self expressing the PDL one right
17:48and that good on the malignant side.
17:50So that's why computation I mean
17:53digital pathology and computational
17:54approach are very useful and and and
17:57this is also a way to interrogate
17:59the issue to understand the biology
18:01in this case you know transcript
18:02immunology and and people are using
18:04the same for transcriptomics.
18:06So in this case you can use them
18:08to quantify
18:09to study compartment heterogeneity
18:12and then the locations and the
18:16location compared to targets or
18:18other cells cell interaction.
18:20So now this is the introduction.
18:23So I'm doing OK,
18:27I'm going to give you.
18:29So I work, I started working lung
18:31cancer and I'm getting all the
18:34goals and telling all the stories.
18:36But when working on pathogenesial lung
18:38cancer what is the origin of cancer
18:40is what is the prunoplastic lesion
18:42that actually matter or pre Malinas in
18:45the matter to develop small cell lung
18:47cancer or non small cell lung cancer.
18:49So I have very strong still
18:53association and collaboration in
18:54the early pathogenic lung cancer,
18:57but you know working in lung in in in
18:59the Cancer Center that MD Anderson we
19:02all most of our research try to focus
19:05initially in advanced metastatic disease.
19:07So we see patients with stage 4 and cancer
19:10and we do clinical trials and research,
19:13translational research in that setting
19:16and even worse it was worse before
19:18we did it mostly in their factory
19:21patient that failed chemotherapy.
19:23So and and I'm actually so happy that many
19:25of these therapies I mentioned earlier
19:27are moving to the new adjuvant space.
19:30So you can see you can do more
19:32studies of resected tumor that have
19:34been treated with different therapy.
19:37So, so and and and I I like to show that
19:40the progression of lung cancer is also
19:43an opportunity to to bring discoveries
19:45on molecular pathology or biomarkers
19:48across the spectrum of the disease.
19:51There are people who are doing chemo
19:53prevention with immune checkpoints
19:54in lung cancer to small novels,
19:56right.
19:56So whatever we learn on advanced
19:59metastatic could be also or resected
20:02tumor could be important to.
20:05Study premalignant or low malignancy
20:07lesions that could progress or not.
20:11So the first example is going to be
20:14actually about some immune analysis of
20:17tissues that have these premalignancy
20:21or low grain malignancy characteristics.
20:24And this is study that was published
20:27in Nature Communication 2021 that
20:29they started with Doctor Dejima,
20:31A pathologist from Japan who visited
20:33for three years and was finished
20:35by Doctor Jay Sang,
20:36the faculty interested maker oncology.
20:39And in this one we collected specimen
20:42from Japan that had these atypical
20:44adenomatosaparplasia that is believed
20:46to be the precursor of at least a
20:50substance or lung adenocarcinoma.
20:52We have a few adenocarcinoma inside
20:54that means malignant cells that are
20:57not inviting invading which is but
20:59invasion is a is a controversial
21:01topic in in Langa carcinoma but
21:03we thought that we had inside the
21:06lesions and we have micro invasive
21:08carcinomas and then invasive carcinoma.
21:10This is small number,
21:11but we decided to do this is to do
21:14a kind of a kind of comprehensive
21:16approach based on different technology
21:18that we work in these formally fixed
21:21paraffin and visited small lesions.
21:23We run an RNA expression using a mono
21:27oncology panel of 100 and 77170 genes.
21:30From nanostream we run multiplexing
21:33monoforescence,
21:33the two panel that already mentioned
21:36PD1PD1 and T cells TCR SYNC sequencing
21:39call it some sequencing of global
21:42methylation and basically just to
21:45give you the summary of the result.
21:47So what we found is that when
21:50we increase on
21:51the malignant potential or
21:53malignancy future of these lesions,
21:56we found that RNA level studying
21:59these immune related genes,
22:01we saw an increase in later stages
22:04this progression of increase of
22:07immunosuppressor gene status with
22:09the decrease of immune activation.
22:11The maximum number of Cytodoxy T cell that
22:14we found was in the adjacent normal tissue.
22:17These are big patients that may have
22:19been supposed to tobacco and had some
22:22COPD features and this actually with
22:25timer deconvolution of the RNA data.
22:28We thought that would identify CD40
22:30lymphocyte that were increasing that could
22:33be related to these immunosuppressive
22:35genes state and we thought that our
22:38direct cells we could improve it.
22:40That's why we ran the Multiplex multiple
22:42for Essence show you the next slide.
22:44We found an increase in the density
22:47and diversity by reviews on coronality
22:49of the T cells that were expanding.
22:51And also we saw as expected increased
22:54copy number changes allelic imbalance
22:58which is a reflection of loss of the
23:00residosity and increase on new antigen.
23:03And what we saw in the later stages
23:05in the micro invasive and carcinoma,
23:07a higher frequency HLA loss of the
23:10residosity that could associate with
23:13these can reduce of immune activation
23:16and increase of immune repression.
23:18So this Malina cell could escape
23:21the immune system. We can base it.
23:24The multiplexing Monoporesia actually
23:26confirmed that we saw an increase
23:28of direct cells and a reduction
23:30of cytotoxicity cells and this
23:31is going to go to,
23:33sorry to this slide in which actually
23:37we are not cannot show you the details,
23:39but actually it shows this an example
23:42of the increase of the direct cells and
23:45decrease of Cytotox activated these cells
23:48and and this is the data showing reduce
23:52on clinality and increase in diversity.
23:55So all these show very early on
23:58this stage in the transformation of
24:00the South that is associated with
24:03an immune response from the coast.
24:06But then you lose and some suppressor
24:09mechanisms trigger in and may explain more
24:12other things the progression of this lesion.
24:14And actually this is the work that
24:16have been doing over the years.
24:18And then we actually added to the
24:22genomic findings that we're ready,
24:23we're aware the mechanisms of the
24:28role of immune response in the
24:30evolution of these lesions.
24:32So now I'm going to give you a couple
24:34of examples of projects that we have
24:35done in surgical resected tumor,
24:37non small cell lung cancer stages,
24:39one and two in these cases usually
24:42is they have not been treated with
24:44a therapy before the resection.
24:47It's hard to have a clinical endpoint and
24:49the clinical endpoint is outcome of the page,
24:51right.
24:52So when I refer to recurrent
24:54free survival or overall survival
24:56after resection as a an outcome,
24:59you know comparing the findings
25:02on the immune response that we did
25:04the study that we did.
25:05But before that when we developed
25:08this Multiplex assay and lung cancer,
25:11we're looking for proof of principle
25:13study something that we
25:14can show that we are finding something
25:17that's can correlate with the clinical
25:19status of the patient and people
25:21have been talking and they have been
25:23shown that chemotherapy induced
25:25immune response in tumor by trying
25:28cells you know and the antigens are
25:30are available to the immune system
25:33that's elicit the immune response.
25:35So but nobody has shown that and it's
25:37hard to get biopsies from patient
25:39before and after chemotherapy.
25:41So we did with these two panels again
25:45PD1PD1 centric, T cells centric,
25:50we studied 61 chemo naive cases,
25:53surgical resected non small cell cancer,
25:55they were not exposed to any therapy
25:58versus 51 were controlled by a sex story
26:02and so on 51 treated with chemotherapy,
26:05platinum based therapy and
26:06we found what was suspected.
26:08So we see activated,
26:10we see more T cells,
26:12memory cells,
26:13cells with exposure to antigens
26:16higher in the one the patient
26:19that have received chemotherapy
26:20and also we saw an activation and
26:23increase of PDL one expression.
26:25So it was published in 2018 by Doctor
26:28Parry in journal of in in monology.
26:33So then we haven't been applying
26:35these to actually your new argument
26:37trials in which patients have
26:40received immune checkpoint therapy,
26:41single agent in combination
26:43or weak chemotherapy.
26:44I know when I show enough of the
26:46data and we haven't discovered
26:47anything exciting by doing the
26:49study but we have shown what people
26:51expect and this is an example.
26:52This is a new argument trial running
26:55in in our institution in the Anderson
26:59which they compare in patients use of
27:02anti PD1 ebolumab with the combination
27:04of drug nivolumab and epidumab.
27:06Anti PD one acid DA four and and we
27:10show by multiplexing monoparesin
27:11using the similar patterns that
27:13when they add the second drug
27:15the anti CDA 4 to anti PD one.
27:18Actually they see a more mounting
27:20a more robust immune response
27:23based on T cells activation mostly
27:26in the treated in the patient
27:29that received this treatment.
27:31And this was along with some other tests
27:33done in the peripheral blood of the patient.
27:35So this is something that we have done
27:38in in in at least four or five illegal
27:41trials to contribute to validate
27:43some of the findings that people
27:45have with other immunology tools.
27:47But they haven't been actually
27:49discovered by themselves and it's
27:51very hard to discover in my opinion
27:53with a limited number of panels.
27:54I think that you need to go
27:57with use higher multiplexes.
27:59This is a nice story,
28:01very brief that we published in
28:042018 in John after a psychology
28:08and we're using classic
28:10chromogenic immunothochemic.
28:11We decided to start studying on
28:13top of PDL 1 immune checkpoint
28:15others and we have eight others.
28:18You know you can see the 7384 either
28:21one ICOS Vista like what like 3 of
28:2540 at team three and the reason
28:29was is I was thinking is is is what
28:32is the chances that lung cancer
28:35tumor express more of one of these
28:38immune checkpoints in which level
28:42and is that opening opportunity for
28:45combination and this is and and how
28:47we can also learn about studying
28:49these immune checkpoints some of
28:51them expressed in malignant cells
28:53only most of them expressed in
28:55malignant cell immune cells some of
28:57them expressed only in immune cells.
28:59It's very hard to quantify but
29:00we did it with digital pathology.
29:03I mean analysis in 184 non small
29:05cell and cancer adenocarcinoma
29:07squamous or carcinoma and this
29:13heat map showed the the data that we
29:17found and we don't we don't what is
29:20consider PL-1 positive in lung cancer.
29:23So we put the cases from higher to lower
29:25based on the percent of malignant cell
29:27express in it and then the other markers
29:30in the malignant cells on the tumor
29:32associated immune cell are presented as
29:34higher than the median for the cohort.
29:37I don't know who squam because we don't know
29:39what slack 3 positive and immune cell is.
29:42So and we did that and you can see that they
29:45given two more have many of these markers
29:48higher than the median that cohort positive.
29:51So it's it's a very complex environment
29:53and with there are many of these
29:56immune checkpoint play potential role.
29:58So I think that we're very lucky
30:00we have to PD1 and PD1.
30:02I don't think we haven't been
30:04very lucky with others.
30:05And I think because there is complexity on at
30:08least on the immune checkpoint perspective,
30:10there's other complexity on the cells.
30:13So then we put all these markers in
30:16multiplexing monoforous and it was published
30:18in Nature Communication 2020 through.
30:20It's a paper with a lot of data,
30:22but not a clear story because
30:26we couldn't find anything that
30:28could be called a discovery.
30:30And I know that we're doing a lot of
30:32spatial analysis now and I hope we can
30:34make more contribution with this data,
30:36but we decided to publish anyway
30:39to make it available.
30:40And what we've found is we put
30:43all those T cells,
30:45macrophages,
30:46malignant cells markers together in
30:50five panels including Myelo cells
30:52and and then you put all these immune
30:56checkpoints in the panel and we did this in,
31:00in,
31:00in in 225 non small cell lung cancer cases,
31:06142 I don't know 83 squamous cell carcinoma,
31:08we always separate,
31:09there are two different diseases for
31:11me so for for many and so I will
31:14look at differently and what we found
31:16it's a lot of Co expression and I
31:19think that this also Co expression
31:21based on data that I've done in other
31:23diseases or you're doing all vibes,
31:25it's change over time chain with
31:28tubal progression and chain with
31:30intervention and and here you can
31:33see that marker by marker.
31:34They are associated with other
31:36market very frequently.
31:37I said it's a high level of Co expression
31:40of these immune checkpoint in T cells,
31:42Mylo cells,
31:43even in B cells and this is highly
31:46heterogeneous in tumor.
31:47In A tumor you see areas that their
31:50expression of certain immune checkpoints
31:52combined with areas that are others.
31:54So it's highly heterogeneous.
31:57There is a high level of
31:59these immune checkpoints,
32:01some of them with inhibitory
32:03stimulatory features.
32:04It's complex it's complex so and it's
32:07different between Adam and squam across
32:09you know that's not a big discovery.
32:11So if you are interested in this
32:13data this could be available to
32:14somebody doing more spatial we
32:16did some spatial analysis.
32:17This is the first
32:19time that actually we
32:20did we published on this.
32:22We follow up with another paper I'll
32:24show you in a minute that we we focus
32:27on spatial analysis and we found that
32:30some markers we we define two two
32:33ways to study especially the we have
32:36two approaches to study the the the
32:39facial distribution of immune cells in tumor.
32:41One is by infiltration there
32:44are some index called G index,
32:46what is the level of infiltration of cells
32:50in looking at you know the tumor compartment,
32:52the malignant cell and the other is
32:54the distance of these cells of interest
32:57with malignant cells or with the others.
32:59So that's the two ways and in this
33:02study we found that if one markers
33:05of obscure market for me probably
33:07makes a lot more sense for you.
33:09It's a my love a neutrophil related
33:12marker that when we have more infiltrated
33:15a pattern close to the malignant cells
33:17associated with product with better
33:19overall survival in these patients.
33:21And this is adjusted by proper
33:24characteristic multivariate analysis
33:26and and then so we found that this
33:29more easier for me to understand that
33:32T cells and and say the **** T cell
33:37when now looking at the distance in
33:40microns are closer to malignant cell
33:42those patients have a better outcome
33:45and based on overall survival the
33:47same we found for 64 microphages
33:50that basically CD 68 these are kind
33:53of the first time that we actually
33:55were doing this and learning how to
33:57deal with this infiltration in this
33:59and and and then you know distance
34:03and this is study that was published
34:06in modern pathology the same year
34:08but later this study was designed to
34:11address the intratumor heterogeneity
34:13of immune response very small study
34:16that with our all Multiplex pattern
34:19one and two in which we take took 33
34:23stage 1 lung Adeno garcinum wanted
34:25I mean Adeno and squabous.
34:27So dividing two-story wanted to control
34:30and go to the cases that are part of
34:33one stage of the disease and we divide
34:36in two groups based on recurrence pattern.
34:39They we call recurrence cases when they
34:43recur within 36 months of after surgery
34:4717 patients and the one that didn't
34:50recurs is the one that after five year
34:52follow up in having a recurrence right.
34:54So if I didn't kind of extremes outcome
34:57as much as we can we could and then we
35:01took the tissue the malignant tissue,
35:02the tumor we developed great system of
35:051mm diameter in which we ran in each
35:09of these spots corruption sequence
35:11RNA sick and Multipleximolar forest.
35:14The game the two panels that I showed
35:16before when I show some of the data
35:18on term of the immune infiltration
35:20result but we found that the one that
35:23recurred within this that this month
35:26have and it's hard to see I'm sorry
35:28about that is an increase of immune
35:31cells that had either PD1 or PD1
35:35expression with some we call inhibitory
35:38mechanisms of the immune response.
35:40Those cases tend to recur earlier or recur
35:44because the other after five year they
35:46didn't have any recurrence and this is
35:48actually shown in this more you know graph
35:51here and I cannot even see it myself.
35:53But the the red are are the recurrence
35:57and these are different subpopulation of
36:00macrophages or AT salt express PD1 or PD1.
36:03And also we saw that it was an increase
36:08of macrophages compared to T cells in
36:12this recurrence of the ratio between
36:14T and T cells and macrophages was
36:16lower in the recurrence.
36:17So that's tell us there's immune
36:21suppressive stage in these cases.
36:23And then we decided to explore
36:26these other features which is the
36:28distance of immune cells and we found
36:32two populations of immune cells,
36:34one is cytotoxic T cells that are
36:37activated and also these PO1 positive
36:40macrophage decided to show you this is
36:43that this PDL 1 positive macrophages that
36:46you can assume have an immune suppressive
36:49stage when they're closer to the tumor.
36:52And I think that the the 20 Micron is
36:55kind of the the the the number that
36:58is is can divide these cases in closer
37:01and higher when they're closer to
37:04the tumor this actually this patient
37:07have a worse outcome and this is also
37:11associated with a higher level of
37:13infiltration by the gene by other index.
37:15So,
37:16so both in usually both numbers goes
37:20together and this is actually very
37:23interesting and we're trying to
37:25apply this process of infiltration
37:27pattern with more sophisticated
37:29computational tools now or distance to
37:32other studies and I'll show you a few
37:34minutes and experience in advancement
37:37of starting of Molson and Gas.
37:39The other thing is we we look at is
37:42the heterogeneity in the tumor on,
37:47on, on,
37:48on,
37:48on and the effect on on the outcome
37:52of this patient.
37:53And we have also some data on the genomic
37:56heterogeneity and we have published before.
37:59But this is trying to think on the
38:01genomic and normality in the context of
38:03the immune response shows some data.
38:05What we found is that a very interesting
38:08is that actually the Fox P3T cells,
38:12the direct cells,
38:13they tend to be more heterogeneous
38:18in the recurrence cases.
38:23So they're not diffusely infiltrating
38:26a tumor,
38:27they're in different spots and and
38:29the key is expressed by frequency
38:31and by an index of the regenetic,
38:33I think that's Moriceta index of nine
38:36compared to 3.4 in the northern currents.
38:38So that means that more infiltrating
38:41in in in in there sorry they're
38:44not very much infiltration to do
38:46more that are scarce around and and
38:49this is actually associated with a
38:52higher level of worse outcome And
38:56this is what's independent of the
38:59genomic alteration that we have like
39:02somatic mutation antigen burden or
39:04or or even the TCR repertoire.
39:06So the T Rex when they're I,
39:09I have a higher ITH is associated
39:12with higher risk for last and then
39:14when I think I got it wrong before
39:17when they're actually in clusters far away
39:19in in a few locations of the tumor is,
39:22is is actually is, is, is is is the opposite.
39:27So these are the studies that actually
39:29we continue doing and I'm going to show
39:32you some samples now in the advanced
39:34metallic cases but summarize this,
39:37this part of the lecture basically multiple
39:39small Fraser I think that's a good tool.
39:42We have shown that you know it's
39:45associated with activation of T cells
39:47and another immune cells in chemo treated
39:50cases compared to untreated cases.
39:52We have identified certain pattern
39:56of infiltration at spatial level that
39:58may be associated with the outcome
40:01of patient without intervention have
40:03shown that actually can show you in
40:06tissue the fact of immune checkpoint
40:08in the context of Ionia and therapy
40:11and and there is a complex pattern
40:14of expression of immune checkpoints
40:16in in in in tissues especially when
40:19you use all these multiple system.
40:22And the last story is about
40:25advanced metastatic cases.
40:26And this is a study that actually is part
40:29of a network that in the Anderson is,
40:32is, is is one of the centers
40:34is funded by NCI or the CMAC.
40:36Some people call it SIMAC after seven-year.
40:39We haven't been solving that.
40:40I call it CMAC.
40:41I was the chair of this network and they
40:43have three other centers in Dana Farvez,
40:46Mount Sinai and Stanford.
40:48And the goal of the center is to perform
40:52comprehensive analysis of a genomic and
40:57and immune level of sample collecting
41:00clinical trial funded by NCANCI network.
41:03These serving groups like
41:06SOAG ECO accring early,
41:08early early therapy network pediatric
41:11ILG and Alliance and and and what we
41:14decided to do and this was activated
41:17before COVID is to develop a series of
41:20three set of marker or the assay that we
41:22can apply to sample from these trials.
41:25And the goal is to identify by a
41:27market that could be predictive
41:29or response or even some markets
41:31associated to less secondary effect.
41:33And and we established Tier 1 market
41:35that we need to run in every single
41:38sample from the clinical trial if
41:40the sample is available or suitable.
41:42That's one of the challenge in this
41:44type of recent collection signal
41:47RNA sig nano chain IO panel.
41:49When RNA 6 doesn't work,
41:51we have a lot of formally fixed parasitic
41:53tissue that hadn't been processed well.
41:55Unfortunately Cytof on a panel in
41:58blood Multiplex immunochemistry or
42:01immuno fluorescent tissue and single
42:03Plex immunochemistry that means PL.
42:06one and only which is a way to study
42:09cytokine chemokine growth factor proteins
42:11in serum with a bundle of 92 markers.
42:15And then the Tier 2 assays more focus
42:19analysis in some trial like TCR sequencing
42:23or some other Multiplex system like
42:25maybe and our microbiome this has
42:27changed a little bit over the years.
42:29And then the Tier 3 assay which
42:32you need fresh specimen is a lot
42:35of single cell sequencing activity.
42:37And also we have done recently transcriptome,
42:41special transcriptome we we face
42:45activation of the network COVID hit.
42:47We didn't have too many samples to analyze.
42:49So what we did in the first two
42:52years actually to harmonize these
42:54assays between 3:00 or 4 labs.
42:56So we harmonize RNAC or some sequencing
42:59site of a Multiplex immuno frerescent.
43:02Actually we developed SOP and that
43:05are publicly available for people
43:08to to do this work in the context
43:11of immune profiling and and and
43:13they're they published these three
43:15papers back to acting as a region.
43:17They have been highly cited and nobody
43:20has done this before and actually help
43:22us all the labs to do a better job and
43:26we have a system of quality control
43:28on ongoing assays that we're running.
43:31They try the network.
43:33We have done study more than
43:35sampler for more than 55 clinical
43:37trial and over 2000 patients.
43:38This was from September last
43:40year and many diseases,
43:42solid tumor nouns and malignancies and mostly
43:46immune checkpoint therapy with combination.
43:49So we were lucky to get
43:53assigned a lung cancer trial.
43:54It's a phase three-part of the
43:57lung map associated with salt,
44:00the S 1400 eye cohort.
44:02There was a phase three study
44:04designed to see the benefit,
44:08potential benefit of adding anti
44:11CDLA 4 IPIL luma to anti PD,
44:15one Ebola in patient with squamous
44:19cell carcinoma metastatic.
44:20So see the effect of the combination
44:23versus single single agent.
44:24So the trial after
44:27270, 252 patients was negative
44:29was but we and the investigator
44:34including doctor herbs that he
44:36asked me to mention his name.
44:38So I did it check we're good
44:41friends for many years.
44:42So I can make these jobs
44:45show that here at the end of the day
44:47of the car you see there's a group
44:49of patients that may have benefit of
44:52receiving this but nothing was significant.
44:54So this is we got, we do more and blood
44:58sample 455 patients divided in a group of
45:02responders across 20% stable disease 40%
45:05and then a lot of progressive disease.
45:09And these are the clinical data
45:10that we're not going to go through.
45:12And our team is CMAC actually LED analysis
45:14of year one immunochemistry multiplexing,
45:17mono fluorescent holoxome sequencing,
45:19nanoching, the panel that I mentioned
45:22before 770 genes and the all ink run
45:25by our colleagues and Mount Simon.
45:27So and and this is the 2 panel,
45:29we we identify 17 phenotypes that
45:31we're going to study divided in two
45:34panels I mentioned before and what
45:36we found is that few,
45:37few things that are probably not new.
45:40We found that it's a higher component
45:43of immune cells trauma compared
45:45to the malignant cells area.
45:47We divided the the analysis
45:49by trauma and malignant cells.
45:52And also we have what we call total
45:54when we combine both compartments.
45:57And we didn't find a huge difference between
46:03the single agent or the combination,
46:07but we found in the total population
46:09of patients that we could run
46:12in multiple monoforensis.
46:13You can see 82 samples only.
46:15It's a big attrition EVI and 35 receiving
46:18the combination of 47 single agent
46:21that actually a series of T cells
46:25activated expressing PD one or with
46:29memory features in the tumor compartment,
46:31the total compartment of
46:33both compartment the tumor.
46:35The higher density associated with a
46:37progression of fee survival regardless
46:40of the treatment of the patient in terms
46:43of differences between these two treatments.
46:46So what we found is that actually
46:49when you have you will receive only
46:51nivo treatment NTPD one the series
46:54of T cell with memory features and
46:57memory regulatory features overall
46:59all associated with better outcome,
47:02better operation fee survival for
47:04three marker survival for one in the
47:07nivo EP in the combination actually
47:11the association of this the increase
47:14of activate the cytloxy T cells and
47:16explain T cell associated with better
47:19outcome but the patient regulatory
47:21T cells C3 poses the negative and
47:24C positively positive associated
47:26with worse outcome and this is shown
47:28in here in the capital measure.
47:31So we identify one group of cell
47:33that I think that very interesting
47:35to follow that associated when the
47:38higher density of baseline with worst
47:41outcome impatient to do with the
47:43combination this particular tumor and
47:45it's something that we're following
47:46up because a similar study was run
47:48in Italy by a friend Ferrigo Capuzzo
47:52and he's sending samples asked for
47:55to look at for this particular fine.
47:58And I know that some people doing
48:01work in the laboratory also have
48:03been associated a role.
48:05I've been looking at the role of
48:07Tigre cells in the indeed this
48:09particular combination of therapy
48:11particularly in our institution.
48:13So then we decided to look at
48:16especially what's happening in the
48:18with more digital pathology tools.
48:20And for that in collaboration
48:22with the clinical team,
48:24we divided this patient treated and
48:26you can see here in the swim plot,
48:28you know each patient's online,
48:30we define 11 patients that were
48:32called exceptional responder.
48:34I think that's not a good name,
48:35but it's the name that we gave
48:37it patient that didn't have any
48:39sign of progression and were
48:40alive any sign of progression.
48:4218 months were alive at 24 and then the
48:46early progress of people that actually
48:48were alive after one month treatment
48:51and they have signed a progression
48:55of death or disease at six months.
48:59And and then we'll look at these two,
49:01unfortunately this is the problem with this
49:04perspective analysis of clinical trials.
49:06So I hope you have prospective
49:09collection of samples,
49:10so can do better job here.
49:12Here our 11 exceptional responders 8.
49:17Our 44 LE progressor 21 for the
49:20Multiplex and we found actually
49:23that activated T cells were higher
49:26in the exceptional responders makes
49:28sense right that's that's that's
49:30interesting and it's not and and
49:33and and and also we found that the
49:35density of and and and and and of
49:40other cells cytotoxicity cells,
49:43cytotoxicity cell and cell with
49:46memory features also where higher
49:49exceptional respondents.
49:50So in the way that we were looking
49:53to data that actually makes sense
49:56and then we decided to go a little
49:58bit further on the analysis of the
50:00special data and the animation was not right.
50:03And we look at now the infiltration pattern
50:07with the more sophisticated cell clustering,
50:10base cell clustering is is what
50:13defined like at least 10 cells or
50:15more that are located in a 20 microns
50:19radio from the malignant cells.
50:22And and this was actually show that
50:24in the exception responder here one
50:26case in blue and trying to show that
50:28they have more dots that the early
50:31progress of the word called tumor.
50:32And this is based on the cell
50:35clustering base analysis.
50:36So showing that not only the density
50:41but also the infiltration pattern not
50:44the actual number cells that where they
50:47are infiltrating the two may have a role.
50:49And then we did the other analysis,
50:51we see the distance right,
50:53the distance of immune cells to
50:55malignant cells measure here in the
50:58software of the Polaris system.
51:00And we found that let's focus on the
51:03whole all cohort and this was also seen
51:06in the people received the combination
51:09is that when cytotoxic T cells activated,
51:13toxic T cells are closer to malignant cells.
51:16Actually those patients have better in
51:19this case progression free survival.
51:21And when those malignant cells expressed TL1,
51:25we saw the same and there's one effect
51:28on overall survival was activated
51:30T cells so close to Malina cells.
51:34So and and this was the same for
51:37all cohort and for the new EP we
51:40didn't see that effect on.
51:42So with that,
51:43I think they're going to stop because
51:45the next is, is studies on the
51:47genomic part in these analysis or on
51:50the all link just going to go to my
51:52last slide basically showing that
51:54this in this clinical trial setting,
51:58our exploratory analysis show that the
52:01frequency attribution and cluster of
52:03immune cells relative to malignant cell
52:05may affect the the efficiency of immune
52:08checkpoint and I see a typo there.
52:10And also we had some other interesting
52:13observation on their genomic and the
52:15all link but I didn't have time to show
52:17today because I'm running out of time.
52:19So with that I would like to thank
52:22you again for being here in person
52:24and virtually and for your attention.
52:26Happy to answer any question.
52:28Thank you.
52:39David. I
52:41had a number of spatial information and
52:44the type of cells were all associated
52:47with a better or worse outcome.
52:50The changes weren't that big.
52:52Is there any of those assets
52:54that you envision taking to
52:55the clinic as a diagnostic?
52:59Yeah, no, the the,
53:01the changes are are not big.
53:04I think that especially as you look
53:07at density and and and and and the
53:10fact on on on the patient's operation
53:12fee survival overall survival.
53:14There are some interesting cut
53:16point that you can establish
53:19in this spatial analysis.
53:21And I'm particularly intrigued
53:24by this is done by computational
53:27people in our group about this
53:29cell clustering analysis of the 20
53:32Micron not 20 Micron because that's
53:34kind of a point that you actually
53:37establish that and you divide a
53:39couple of measures significantly
53:41not barely as we did it right.
53:44It was a negative trial,
53:45so hard to to come up with something
53:48very very or or or or or you or
53:53you just have you know significant
53:55increase on people responding
53:57versus not responded by that.
54:00I think that there is an opportunity
54:02there to have at least something
54:03that is with a yes or no kind
54:06of approach that's what
54:10yes the cell the cell clustering
54:13of immune cells that may have an
54:16activation or repressing right in
54:18the in the response that would be
54:20good or bad in the in certain in in
54:23radio of cells from malignant cells.
54:26There is a potential cut point
54:27there because I'm, I'm trying to,
54:29you know, see what could be a
54:31yes or no answer of a Biomark.
54:34So that's one thing I think that there are,
54:35there are some opportunities however,
54:38I think that it's very hard to bring
54:41these to a clear setting, right,
54:44because the challenges of getting stable
54:48work done in pathology laboratories
54:52in in in the Multiplex area,
54:56you have experience on that.
54:57I think that your center and other
55:00center probably could do that,
55:01but there's a lot of variability
55:04and we learned that when we tried
55:06to do the Multiplex.
55:07Similar to chemistry and fluorescent
55:10harmonization among different sites.
55:11We started with more than three.
55:14We published we need to drop others
55:17because we couldn't get to a basic
55:21standard of performance of the Multiplex.
55:24There's several challenges,
55:25technical challenges how to get
55:27the same sample across different
55:29places right and things like that.
55:31But I think that is is is very
55:34challenging in my opinion.
55:36I think that is any of these Multiplex
55:38as I get to the clear setting is to
55:42answer kind of fundamental questions.
55:44So far is this a hot tumor or
55:47is this a cold tumor?
55:49This tumor express the cell that I'm
55:51looking for the market I'm looking for.
55:54I I also have a lot of hope on the
55:58ABC field if that pan out and we
56:00need more proteins to be examined
56:02if they're used as a biomarker
56:03to select patient maybe it's
56:05an opportunity for Multiplex.
56:07But I'm I'm I'm I will be nervous
56:09about the performance of different
56:12laboratory and and if they have the
56:15right skills and the right controls
56:18to actually validate the work.
56:20I just followed up with a quick question.
56:22So they're all related to the member sites.
56:26Have you compared it to
56:28just regular HD tails? No,
56:33I haven't. But it's happening actually.
56:36Why happening? Because we have a
56:39group of computational pathology
56:42that they like our Multiplex images
56:46from these or codecs or the UMX,
56:51but they want to work with H&amp;ES.
56:54And actually I had a couple of slides
56:56on the printer plate lesion that all
56:58these fancy T cell work that we did,
57:01somebody can do it very well
57:03with a metoxilinous aosine and
57:05computational pathology.
57:06So I think that there is hope with that,
57:08but but you know the things that
57:10we need to know what we're looking
57:12for and if we don't do this
57:15smart in depth characterization,
57:16we're not exactly what we're looking for.
57:20And then if that can be given by deal
57:24a simple computational pathology assay,
57:27I'm all for it.
57:27But we need to know if we can do that.
57:30And those can do a spatial analysis,
57:32very simple, very easy,
57:4111 difficulty that I see this type
57:47of field, Is that everything for you?
57:50Yeah, some redundancy.
57:51It's very difficult to identify something.
57:53Everything is correlated.
57:54And the second one is the spatial analysis.
57:57Generally, the distance between the cells
58:00is inversely related with the density.
58:02So essentially, typically the distance
58:04is the inverse of the density.
58:05So it's a survey measurement of this.
58:08How do you think we can
58:10extract or clean data
58:11without those redundancy or those problems?
58:13Do you have any? No, I I think that I
58:19mean to me all this work is a way to
58:21actually start mastering tools, right.
58:23So are you can you do this special,
58:25can you do go the Multiplex, you do go the
58:32Multiplex asset, do they actually give
58:34you the right answer for you expect
58:37in terms of immune response and that
58:39correlate with outcome of patient?
58:41That's the basic question I think.
58:43I think that we're at that stage
58:46is any of these going to be a
58:48biomarker I don't think so right.
58:49We know that but actually it's it's pointed
58:52out in the right direction in terms of
58:55the methodology that you're applying.
58:57So that's why I, I,
58:57I fully agree with your comment
59:00about you know everything correlate
59:02with everything and I I I promise
59:04I'm not hiding anything that didn't
59:05correlate and it makes sense because
59:07that's you find those right a lot
59:09of things that correlate don't
59:10make sense I I didn't hide any.
59:13So we we we're we're we feel good about it.
59:16So the other thing is that in
59:19that sense if you master these
59:23approaches can help you to better
59:27work with biomarker will be complex,
59:32right in terms of immune response when
59:36the real biomarker comes and the real
59:40biomarker will come on prospective
59:43clinical trials in which biopsies
59:45and other samples that you can save
59:48in the freezer and but starting with
59:50tissue analyze tissue on prospective
59:52basis and for immune oncology on
59:55in my opinion on logituinal basis,
59:58right.
59:59So get clinical trials biopsy
01:00:01before on ongoing treatment and
01:00:04you are lucky on time of progress
01:00:06that will give you the answer.
01:00:09I have experience working in rare
01:00:11tumors with logituinal biopsies.
01:00:13And sarcoma they were cold after a
01:00:15couple of cycles of immune checkpoints
01:00:17are not cold anymore and actually
01:00:19some of those patients benefit
01:00:21with very long stable diseases.
01:00:22So I think that when we go to that area,
01:00:25I think that all these tools
01:00:27are going to make more sense.
01:00:29And and and then specific
01:00:31question about yours,
01:00:33you know the challenge of density
01:00:35and and proximity, I I don't have,
01:00:37I don't have an answer on on that.
01:00:40I heard about that.
01:00:41I don't know how exactly to correlate,
01:00:43I haven't seen the data but there's a
01:00:47lot of also there's hope on 3D analysis,
01:00:51right.
01:00:52So we can actually because whatever
01:00:54we're seeing we're seeing one dimension.
01:00:57So maybe going deeper in in 3D assay will
01:01:00be possible with computational pathology,
01:01:03maybe you can solve that
01:01:05because whatever we see now,
01:01:07we're seeing in one place,
01:01:08we see the density,
01:01:09we see the instant in one section,
01:01:11we don't know who's going on
01:01:13low and above and probably it's
01:01:16maybe it's that correlation not
01:01:18as great as you pointed out,
01:01:20but I don't,
01:01:21I don't know how to actually
01:01:23deal with that at this point.
01:01:28Oh, sorry. So in some core Opsis and
01:01:31both of your second vessels you can
01:01:34see both features autoimmune microvirus
01:01:36but you know tumor proliferative B
01:01:39cells and hold it in microvirus And
01:01:41you know how for those patients do
01:01:44you think that they would respond
01:01:46to some of these therapies.
01:01:47And also nicely we should graph a
01:01:50lot of this in essentially categorize
01:01:52patients with these algorithms so
01:01:54that we can make sure that we're not
01:01:59you know that these algorithms are
01:02:01biased against those areas that you know
01:02:04essentially might have heterogeneous
01:02:05spots of hot and cold regions.
01:02:09So you yeah, so the the the
01:02:12two more tetogeneity is, is,
01:02:13is a tissue tetogeneity sometime right.
01:02:16This is very challenging because
01:02:18if you have a small biopsy you
01:02:21are using what you got right.
01:02:24So that's big bias and and you see
01:02:27something that could be interpreted
01:02:29as priming of immune response or some
01:02:32either one expression that means you know
01:02:35that something happened already there.
01:02:37You you can assume that maybe the entire
01:02:40tumor can be followed the same, right.
01:02:42And if you are not lucky and you don't
01:02:45see it, but it's happening somewhere else,
01:02:47it's, it's a problem, right.
01:02:49So that's that's and so my approach
01:02:51has been in clinical research
01:02:53prospective clinical trial,
01:02:55we tried to get at least 5 biopsies, right.
01:02:58So at least to try to
01:03:00overcome the derogeneity.
01:03:01Can we use the five biopsy
01:03:03for everything that no,
01:03:04but at least two have three and we we're,
01:03:07we try to be good on that.
01:03:09So that's one thing I hopefully in
01:03:11the future more molecular imaging
01:03:13on patients and and imaging advances
01:03:16may help to identify the best
01:03:18spot to get the biopsy,
01:03:20but that's that's an issue on a larger
01:03:22space tissue because it's resected.
01:03:25It's also the bias,
01:03:27right.
01:03:28So how to select I hope competition
01:03:31pathology tools in the future will
01:03:33help us to reduce the bias of an
01:03:36observer to select areas that actually
01:03:38can give us a fair representation of
01:03:41it of the two more in term of high,
01:03:44medium, low grade or infiltration.
01:03:48I I believe that because one of the
01:03:51major issues in the field of the
01:03:53Multiplex and I have data showing that
01:03:56is how people select the region of interest.
01:03:59Each of the images of the multi grid
01:04:01for us is about 1mm diameter field and
01:04:04we have a system to select those and
01:04:08we select five per per per per case.
01:04:11We can do whole whole, whole,
01:04:14whole section now and we're doing
01:04:16that in biopsy.
01:04:17But when we started doing this work
01:04:19have a Section 5 region but they did
01:04:21analysis with a greed and similar
01:04:23greed analysis I showed you before
01:04:25we ran Casey with the monochemistry
01:04:26with Multiplex and we're able to
01:04:28ask for certain marker how many of
01:04:31these equivalent to 1mm diameter
01:04:33spots I need to get a picture of the
01:04:36tumor from 5:00 if they gave us the
01:04:39Max one of the highest R value .94.
01:04:41So I think that 5 is good for lung cancer.
01:04:44It may be different Melanoma maybe
01:04:46different sarcoma different for
01:04:47colorectal but for non small cell
01:04:49and cancer warfare.
01:04:49So that's it's another issue right
01:04:52a bias of people analyzing.
01:04:54So I I believe that computational
01:04:57pathology tools could give us us
01:04:59that answer actually you have when
01:05:02we don't have the thing to do
01:05:04whole sections
01:05:04analysis in the Multiplex.
01:05:06My hope is that our computational
01:05:08pathology team can develop this
01:05:10unbiased system that tell us these
01:05:12are the region of interest when I give
01:05:15you based on what they have learned,
01:05:16we need to feed them also with
01:05:18some of the Multiplex data,
01:05:20the area that you could actually
01:05:22analyze and get a good picture of
01:05:24what's going on that tumor and
01:05:26that's not happening these days is
01:05:29somebody that may like lymphocyte
01:05:31go to the lymphocyte.
01:05:32Oh, it's a lot of information here.
01:05:35And and and and focus only there.
01:05:38Yeah.
01:05:43All right.