Skip to Main Content

Immune-Based Cancer Treatment Biomarkers – The Pathologist’s Critical Role

April 01, 2022

March 31, 2022

Yale Pathology Grand Rounds

Robert A. Anders, MD, PhD

ID
7647

Transcript

  • 00:06OK, well it's 1232. Let's get started.
  • 00:10Welcome everyone to pathology grand rounds.
  • 00:12It is my distinct pleasure to introduce
  • 00:16my good friend Bob Anders to the group.
  • 00:20Today. Very excited about his lecture.
  • 00:24Doctor Anders earned his medical and
  • 00:28molecular biology graduate degrees at the
  • 00:31Mayo Clinic Medical and Graduate School.
  • 00:34He then completed his anatomic and clinical
  • 00:37pathology residency and a fellowship in
  • 00:40gastrointestinal and liver pathology.
  • 00:42And postdoctoral training in immunology
  • 00:45at the University of Chicago.
  • 00:47With our good friend John Hart.
  • 00:50He joined the faculty at Johns Hopkins
  • 00:52School of Medicine in 2005 as an
  • 00:55assistant professor in the Division of
  • 00:58Gastrointestinal and Liver Pathology and
  • 01:00is soon to be full professor in that lab.
  • 01:04He also serves as the Co director of
  • 01:07the tumor microenvironment laboratory
  • 01:09in the Bloomberg Kimmel Institute
  • 01:11for Immune Cancer Therapy.
  • 01:13But in addition to his duties as a
  • 01:15practicing GI liver surgical pathologist,
  • 01:18a Bob is one of those rare breeds of
  • 01:21diagnostic pathologists who runs an
  • 01:23independently funded research lab
  • 01:25that focuses on tumor immunology.
  • 01:27His interest in immunology developed while
  • 01:30he was studying liver immunology and
  • 01:32regeneration at the University of Chicago.
  • 01:35And in his early years at Johns Hopkins,
  • 01:37his current interests are
  • 01:39in tumor immunology,
  • 01:40and specifically interrogating the
  • 01:42immune microenvironment in tumor tissue.
  • 01:44Obviously a very hot topic.
  • 01:47He first began examining human tissue for
  • 01:50the expression of our good friend PD1,
  • 01:53and PDL one in 2006.
  • 01:55At the encouragement of his K
  • 01:578 mentor doctor Liping Chen,
  • 01:59who's currently at Yale.
  • 02:01And he continues this research on
  • 02:03human in inhuman and marine cancer.
  • 02:05I mean analogy using single and
  • 02:08multi color immunostaining couple
  • 02:10to digital image analysis.
  • 02:12In addition to all these accompanying
  • 02:14this area of interest,
  • 02:16his CV boast hundreds of publications
  • 02:18and he is invited to something
  • 02:21like 50 lectures a year,
  • 02:23a giving courses and and interacting is very,
  • 02:27very active and sought after collaborator.
  • 02:30So really, the model of physician,
  • 02:33scientist in pathology and I'm
  • 02:35delighted to welcome you to his
  • 02:38lecture entitled Immune based
  • 02:41cancer treatment biomarkers.
  • 02:42The pathologist's critical role.
  • 02:45Bob,
  • 02:45thank you.
  • 02:46Yeah, thanks Marie,
  • 02:48that's very generous.
  • 02:50Introduction that would
  • 02:51make my parents proud.
  • 02:53Thanks. So this is a great.
  • 02:57Time to be a pathologist,
  • 02:59and that's sort of my my message here.
  • 03:03So I receive funding and and
  • 03:06consult for these in these.
  • 03:13Institutions, but it won't affect what I say.
  • 03:18So the plan here is to talk about
  • 03:20biomarkers in cancer or talk
  • 03:23about immune based biomarkers,
  • 03:25and then you know kind of the.
  • 03:26So the story of how DNA
  • 03:29based immune biomarkers.
  • 03:33Developed and then something
  • 03:35that shouldn't be, you know,
  • 03:38foreign to to most of you with the efforts
  • 03:41of Doctor Shelper and and and Doctor Rim.
  • 03:44And that's you know how do
  • 03:46we find new biomarkers?
  • 03:48So biomarkers it.
  • 03:49Turns out from the beginning.
  • 03:52There's confusion.
  • 03:54So prognostic biomarkers that's
  • 03:57about how patients will do,
  • 04:01essentially untreated,
  • 04:02or with definitive treatment like surgery.
  • 04:06But most of what we talk about these days
  • 04:09is is really about predictive biomarkers,
  • 04:12and that's and that's personalized medicine.
  • 04:15It's what it's what we've
  • 04:17wanted for the longest time.
  • 04:18It's how to select the the patients
  • 04:21that will benefit from a given therapy.
  • 04:25And this is great.
  • 04:28This is great for pathologists
  • 04:30because you know human human tissue
  • 04:33is a prognostic biomarker, right?
  • 04:38This was so clear even it was even
  • 04:42clearer to a surgeon back in 1932,
  • 04:47right with the Dukes classification.
  • 04:49You know, if a if a colon cancer
  • 04:53penetrated deeper into a colon.
  • 04:56The patients didn't do as well.
  • 04:59Fast forward Banta 2006,
  • 05:02where Jerome Galand showed that.
  • 05:06The tight density and location
  • 05:09of immune cells were oops.
  • 05:12He didn't mean predictive.
  • 05:13He meant prognostic of of clinical outcome,
  • 05:17and I will tell you that I presented
  • 05:20this paper at our Journal Club.
  • 05:22And and there were people who walked out
  • 05:27of that room saying this is nonsense.
  • 05:30And why were they so perplexed?
  • 05:34Essentially,
  • 05:34you had patients with stage one or
  • 05:38two that's shown in the in the in
  • 05:41the red and green regions of this
  • 05:44Kaplan Meier curve who didn't do well?
  • 05:51After the diagnosis,
  • 05:52you know that that sort of didn't make sense.
  • 05:55Well, it turns out that our immune
  • 05:58system really is quite important,
  • 06:00which will be the message for the
  • 06:03rest of the of the lecture here.
  • 06:06So you can go back as far as 2012
  • 06:09and you know 120 articles and
  • 06:11in in 20 different cancer types,
  • 06:15showing that you know the type of
  • 06:18immune cells that are infiltrating
  • 06:20at patients tumor or the till.
  • 06:22If you will, is really important
  • 06:25and you know it's not surprising,
  • 06:27it's great to have CD 8 cells that
  • 06:30are secreting interferon gamma.
  • 06:32We would call them TH one.
  • 06:35In your in your cancer compared to
  • 06:38you know things that are suppressive
  • 06:42of of the immune based response.
  • 06:45OK, so that's prognostic.
  • 06:47What about human tissue as
  • 06:49a predictive biomarker?
  • 06:51You know,
  • 06:52this is really talking about
  • 06:54whether patients will respond
  • 06:56or benefit from from therapy.
  • 06:58And you know,
  • 06:59we've been promising patients
  • 07:00this for a long time.
  • 07:02And you know,
  • 07:03I breakdown the predictive biomarkers
  • 07:05into in the three generations.
  • 07:07First,
  • 07:07it was important for oncologists
  • 07:09to know whether something was a
  • 07:12lymphoma or a colorectal cancer.
  • 07:13I mean, this really changed.
  • 07:15How they treated a patient.
  • 07:18The second generation really
  • 07:20came on and this is,
  • 07:22you know work that Doctor Rim
  • 07:24is is doing in really trying to
  • 07:27get down to how much a cancer
  • 07:32expresses a particular biomarker,
  • 07:35not just any biomarker,
  • 07:36but actually a biomarker that
  • 07:38we can target and her two new
  • 07:40would be a perfect example.
  • 07:42And now we're into the third generation.
  • 07:45You know, by my definitions of.
  • 07:47You know how many immune cells are present?
  • 07:50What are what's the immune contexture?
  • 07:53What immune inhibitors are present?
  • 07:56Which ones aren't present so,
  • 07:59as pathologists are our goal actually,
  • 08:02or are, you know,
  • 08:04has gotten much more complicated?
  • 08:06You know,
  • 08:07telling some telling us something was a,
  • 08:09you know an adenocarcinoma versus
  • 08:11lymphoma is a lot different than saying,
  • 08:14Oh well, there's a lot of
  • 08:17tumor infiltrating lymphocytes.
  • 08:19So immune therapy.
  • 08:20What is it? Well,
  • 08:21it turns out this guy knew a lot about it.
  • 08:24This is Doctor Coley,
  • 08:27and if you don't know much about him,
  • 08:28I highly encourage you to Google him.
  • 08:31You know he.
  • 08:33He essentially was rubbing.
  • 08:36Bacteria into the skin of patients
  • 08:39with advanced cancers and showing
  • 08:42that their cancers were regressing.
  • 08:45Now he was a surgeon,
  • 08:47so he was much maligned for his approach,
  • 08:50'cause essentially he was
  • 08:52giving chemotherapy,
  • 08:53which went when you know conventional
  • 08:56thought was that this should be
  • 09:00more of a surgical approach and
  • 09:02you know it was so hard for him.
  • 09:06To get his message out,
  • 09:08he had to travel all the way
  • 09:10down from New York,
  • 09:11which is where he was from down
  • 09:13to Johns Hopkins in Baltimore,
  • 09:15and this is a picture of him
  • 09:18lecturing in Heard Hall.
  • 09:19And this is exactly where pathology
  • 09:22grand rounds is still conducted to the day,
  • 09:26and I can identify this as as as.
  • 09:30Heard Hall because we have these,
  • 09:33you know, flags displayed in at the
  • 09:36margins of of the lecture system.
  • 09:39So what's immune therapy?
  • 09:41You know, it's it's.
  • 09:43It's basically the idea that a patient's
  • 09:46immune system can eliminate a cancer.
  • 09:49This is very patient empowering.
  • 09:51I had the misfortune of spending a
  • 09:54lot of time in a in a cancer immuno
  • 09:57or in a cancer therapy setting and
  • 10:01it was amazing to to to witness how
  • 10:06patients responded to the fact that.
  • 10:10They were mounting in.
  • 10:13Immune response to their cancer.
  • 10:15They just needed a little help removing.
  • 10:20The break.
  • 10:22So cancer immune therapy is is best,
  • 10:26you know understood with you know
  • 10:28anyone who drives a car, right?
  • 10:32So we thought the problem with cancer
  • 10:36therapy or tumor immuno tumor immunology
  • 10:39was just that we we needed to press
  • 10:43the accelerator harder but anyone who knows.
  • 10:47Anything about you know driving a car
  • 10:49if you have your foot on the brake,
  • 10:51it doesn't matter really how hard
  • 10:54you press the accelerator.
  • 10:55It's all about releasing the brake.
  • 11:00And that was recognized formally
  • 11:03with the Nobel laureates.
  • 11:06Here, Doctor,
  • 11:08Allison and Honjo.
  • 11:10Day.
  • 11:10And you know,
  • 11:12essentially both of these
  • 11:15individuals were were studying
  • 11:17immune based therapy in terms of uh,
  • 11:22immune inhibitors,
  • 11:24not immune stimulators,
  • 11:26but immune inhibitor inhibitors.
  • 11:28So what I've done here is,
  • 11:31I've lined up the immune synapse between
  • 11:34antigen presenting cells and and and T cells.
  • 11:38And I think all of us recognize
  • 11:41that we need a signal,
  • 11:43one that's MHC engaging the T
  • 11:46cell receptor and then signal 2
  • 11:50here is what's responsible for,
  • 11:53you know, driving the immune system.
  • 11:56But then it's these breaks.
  • 11:59It's the brace.
  • 12:00It's the inhibitors of the
  • 12:02immune system that went on to win
  • 12:05the Nobel prizes here,
  • 12:07and I would point out
  • 12:08very important fact here.
  • 12:12And I have the utmost respect
  • 12:14for Doctor Handjo, who personally
  • 12:15invited me to to give a talk.
  • 12:20It it was, it was actually Doctor
  • 12:23Liping Chen, one of your tumor,
  • 12:26immunologists, who really figured
  • 12:27out what PDL one did it in in.
  • 12:30In fact it was misnamed, it was.
  • 12:32It was thought to be a program,
  • 12:34death ligands and it was Doctor
  • 12:37Chen who figured out that that PDL
  • 12:40one was actually a very critical
  • 12:43immune inhibitor and ultimately the
  • 12:46key to unlocking tumor immunology.
  • 12:51So how do we reconcile CTL A4 versus PDL one?
  • 12:58You know, for me,
  • 13:00CTL A4 works in the periphery,
  • 13:02it's an early part of the immune
  • 13:05response where PDL one is a little
  • 13:08bit later and occurs in the tumor.
  • 13:10That's the key then to the
  • 13:15pathologist because.
  • 13:16Our ability to look in the
  • 13:18tumor is very helpful. Why?
  • 13:21Why are we even talking about this?
  • 13:24I mean the the rate of.
  • 13:27Of FDA approval for cancer
  • 13:31therapies is unprecedented.
  • 13:33In the last 10 to 15 years,
  • 13:37the rate of cancer therapies approval
  • 13:40with the FDA keeping in mind this
  • 13:44is a very conservative group.
  • 13:47Has just accelerated, you know,
  • 13:50you know I kind of stopped updating my
  • 13:53slide here about 2019 and it's 26 more.
  • 13:56It's probably 50 more by now.
  • 13:59You know this is really.
  • 14:02Frighteningly fast approval in
  • 14:07in terms of of tumor immunity.
  • 14:11OK,
  • 14:11so let's let's take a step back
  • 14:14and you know the first person
  • 14:17to look at A at a cancer.
  • 14:21That had undergone PD1 blockade,
  • 14:24and biopsy is me.
  • 14:28You know, so how did we get here?
  • 14:30So let's talk about PDL one
  • 14:33as a as a biomarker.
  • 14:36So we have this paper here which
  • 14:39you know back in 2012. Explored the.
  • 14:43This was really a safety paper.
  • 14:48This was a phase one trial.
  • 14:53About can patients even
  • 14:56tolerate PD1 blockade?
  • 14:59And.
  • 15:01You can see that very important you
  • 15:04know individuals to to Johns Hopkins as
  • 15:07well as as to Yale or are listed here,
  • 15:11because this is really a landmark paper.
  • 15:15And I was fortunate enough to
  • 15:17be in the right place at the
  • 15:19right time and and what what?
  • 15:21My laboratory at the time was really
  • 15:23good at measuring was the expression
  • 15:25of PD L1 protein in the tumor and
  • 15:30the results of what we found are
  • 15:33summarized in this graph here.
  • 15:35And you can see that if patients
  • 15:38were deemed to be PD L1 positive,
  • 15:41we can argue about what that means.
  • 15:44They had about a 5050 chance of response,
  • 15:48but. And very important here.
  • 15:51If patients were deemed to
  • 15:53be PDL one negative.
  • 15:58They had no chance of responding.
  • 16:01And that's very helpful as an oncologist.
  • 16:05If something has a 100%.
  • 16:08Predictive power that means
  • 16:11that it's as an oncologist.
  • 16:14You move that patient on
  • 16:16to something different,
  • 16:17and this publication here is really the
  • 16:22key to springing everything forward
  • 16:25to PDL one as a predictive biomarker.
  • 16:29Now it turns out that.
  • 16:32That 100% predictive negative predictive
  • 16:34value was was a little bit overblown,
  • 16:38and it we can talk about the details of that,
  • 16:42but it wasn't completely wrong either.
  • 16:46So what I'm showing you here
  • 16:47is one of our brilliant,
  • 16:49now few faculty member members,
  • 16:52Jill Sunshine,
  • 16:53and what they did is they looked at
  • 16:57all the different Dwan treatments
  • 16:59there are listed on the top.
  • 17:01All the different cancer types.
  • 17:03Listed on the bottom and the
  • 17:06response rate listed on the Y
  • 17:08axis here and what you can see is.
  • 17:11That patients who are PDL 1 positive
  • 17:14again we can debate about what that
  • 17:17means had about a 50% response rate
  • 17:20and if they were PDL one negative.
  • 17:25They had about a Tanner of 15 response rate.
  • 17:28OK, so this is a little different
  • 17:31than 100% negative predictive rate,
  • 17:33but it it helps.
  • 17:36So the most common question that I'm asked.
  • 17:39Is is PDL 1A good biomarker?
  • 17:43Well.
  • 17:44It's not, it's it's not an end,
  • 17:47it's it's not a perfect biomarker.
  • 17:49It's imperfect.
  • 17:50And what it does is it
  • 17:53enriches for responders.
  • 17:55So if you're an individual
  • 17:57patient and you're deemed to be
  • 18:00PD L1 positive or negative,
  • 18:01you know Doctor Robert or myself.
  • 18:04Look at your specimen in our
  • 18:06expert weighs and and and call
  • 18:08something positive or negative.
  • 18:10You you probably have about a 50% fifty 50%.
  • 18:14Or or or?
  • 18:16I'm sorry,
  • 18:16a 5050 chance of responding.
  • 18:21But if you take 100 patients and you
  • 18:26pick only the patients that are PDL,
  • 18:291 positive again by an expert reviewer,
  • 18:32you pick out a lot of winners.
  • 18:36So is PDL 1A great biomarker?
  • 18:40It depends if you're a physician trying to,
  • 18:45you know, treat an individual patient.
  • 18:47No, it's not. It's a 5050, you know.
  • 18:50Sort of coin toss.
  • 18:51But if you're a far if you're
  • 18:53a large pharmaceutical company.
  • 18:54And you wish to find the patients
  • 18:57that are most likely to benefit.
  • 18:59Then it's a fabulous biomarker,
  • 19:02because you're a you're a
  • 19:05affectively picking 50% winners.
  • 19:10OK, so maybe the maybe the issue
  • 19:13is we just need to do better.
  • 19:16We need to do PDL 1 determination,
  • 19:20immunohistochemistry, test performance.
  • 19:23Better you know,
  • 19:25maybe when it was first done in my lab.
  • 19:27You know back almost you know
  • 19:3110 plus years ago.
  • 19:33You know, I hadn't considered everything,
  • 19:35so the thing about PDL one expression is
  • 19:38we're asking pathologists to do things
  • 19:41that we've never asked them to do before.
  • 19:44First off PDL one can be expressed
  • 19:47on different types of cells.
  • 19:49It could be in a histiocyte,
  • 19:50a malignant cell or even a lymphocyte.
  • 19:53It can even change its location.
  • 19:55Sometimes it can be in the cytoplasm,
  • 19:57other times it can be in the membrane.
  • 19:59And then we're even further asking
  • 20:02the pathologist to consider all these
  • 20:05different cell types and say how many?
  • 20:09How many cells are positive and this is
  • 20:13really outside of what pathologists can do.
  • 20:17And then on top of that we have
  • 20:21different scoring systems so again
  • 20:24I'm going to go over this quickly.
  • 20:28Sometimes it's the number of
  • 20:30tumor cells that are positive.
  • 20:32Sometimes it's the number of tumor
  • 20:34cells and inflammatory cells that
  • 20:37are positive over a denominator.
  • 20:39That's just the cancer cells.
  • 20:41I mean, this is starting to get crazy.
  • 20:43I mean,
  • 20:45it's not practical for us to
  • 20:49expect pathologists to be able to,
  • 20:52you know.
  • 20:54Find down this granularity and
  • 20:56this was beautifully displayed
  • 20:58in a study by Doctor Rim who,
  • 21:00which I was fortunate enough to be part
  • 21:04of and and basically what we showed
  • 21:06or what doctor Rimm showed is that.
  • 21:11If you ask us, the proportion,
  • 21:14that's the percentage of tumor
  • 21:17cells that are positive.
  • 21:19Houses pathologists we can agree on this,
  • 21:21but if you start making the equation
  • 21:25more complicated and say well you should
  • 21:28include immune cells but not plasma cells.
  • 21:32The Inter rater of their agreement falls
  • 21:36apart precipitously, so a .86% or a
  • 21:41.86 here for inter rater variability.
  • 21:45You know that's reasonably good,
  • 21:48but something close to .2.
  • 21:50That's absolutely terrible.
  • 21:55Alright, so that's created
  • 21:57an opportunity for us.
  • 21:59In terms of pathologists
  • 22:01because which PDL went antibody,
  • 22:03there's actually many out there.
  • 22:05What's staining pattern?
  • 22:06Is it tumor cells or is it inflammatory
  • 22:09cells and and then a cut off?
  • 22:13Even changes between the different.
  • 22:18The different stains and then how many
  • 22:22pathologists do we need to agree?
  • 22:23We've never even even considered this.
  • 22:28So Doctor Robert and I have been
  • 22:31fortunate enough to be to to spearhead
  • 22:35an effort to look at ways to improve PDL.
  • 22:391, staining, evaluation and what
  • 22:41we've developed is an international
  • 22:44collaboration looking at PDL 1 staining.
  • 22:49And the the goal here is to
  • 22:51measure how well we can agree.
  • 22:54I've got a hint for you.
  • 22:55We don't agree very well.
  • 22:57And then more importantly,
  • 22:59how can we do better and what digital
  • 23:02systems may be able to help us?
  • 23:04So that's something I wish I could
  • 23:07share with you a little bit more,
  • 23:09but we're in the process of of
  • 23:11of putting together that data,
  • 23:13but I promise you that it will be very
  • 23:17revealing in that we really need to
  • 23:20do better in order to help patients.
  • 23:23OK, So what about the development
  • 23:25of of of biomarkers?
  • 23:27So it turns out that mismatch repair
  • 23:31deficiency MMR proteins or MSI?
  • 23:34You know,
  • 23:35by PCR is a really important marker.
  • 23:39A biomarker in terms of immune therapy,
  • 23:44so I would like to invoke this
  • 23:47individual Margaret Fuller.
  • 23:48She was a transcendentalist female
  • 23:50author who wrote this very important.
  • 23:53Book.
  • 23:54And what I wish to highlight
  • 23:57out of this book is.
  • 24:00Nature provides exceptions to every rule.
  • 24:05And that's not how we generally
  • 24:09think about cancer therapy.
  • 24:11So what I want you is young,
  • 24:14possibly physician scientists is.
  • 24:17Study the exceptions and I'm
  • 24:20gonna show you exactly why.
  • 24:23So pharmaceutical companies
  • 24:24have a very top down approach.
  • 24:26They would love it if 100% of patients got
  • 24:31a treatment and 10% of them responded.
  • 24:35Because 100% of them need to be treated,
  • 24:38but what I'm encouraging
  • 24:39you to think about is not.
  • 24:42That 90% that didn't respond,
  • 24:46but actually thinking about
  • 24:48the 1% or 10% that did respond.
  • 24:53So the thinking is is very upside down here.
  • 24:58As pathologists, what we want to think
  • 25:01about is we apply a therapy and if there's
  • 25:04one responder we need to drill down.
  • 25:09And focus on that responder
  • 25:11and figure out why.
  • 25:13And I'm going to tell you exactly why here.
  • 25:16So if I had a dollar for every time
  • 25:18I heard this, well since immune
  • 25:21based cancer therapy doesn't work
  • 25:23for colon cancer so very early on.
  • 25:26In my career.
  • 25:30Immunotherapy didn't work for colon cancer.
  • 25:35And and that goes all the way back.
  • 25:37Look at look at the years on this here.
  • 25:39This was 2006, 2009.
  • 25:43In which we did a phase one again,
  • 25:46this is safety. Study.
  • 25:50And that was conducted here at Johns Hopkins.
  • 25:55And you can see that the the
  • 25:57cancers that are listed here.
  • 25:58So Melanoma, lung cancer, renal cell cancer.
  • 26:03You know what? These are?
  • 26:04All immune based cancer therapies.
  • 26:07They kind of tend to respond.
  • 26:09In fact, one in four patients
  • 26:11did respond to this therapy.
  • 26:13Again, this was a safety trial.
  • 26:17We are focusing on, you know,
  • 26:18can patients tolerate this and suddenly in
  • 26:22this 2006 study we're talking about response.
  • 26:26This was crazy. And then.
  • 26:29The interesting thing is,
  • 26:31as I was hearing the chorus of well colon
  • 26:34cancer doesn't respond to immune therapy.
  • 26:37There were 14 patients who were
  • 26:39who were enrolled on this study,
  • 26:41and you know what?
  • 26:44It wasn't hard to find them right.
  • 26:46'cause colon cancer is among the most
  • 26:48common cancers and the take home was yeah,
  • 26:51you treated 14 patients with immune therapy.
  • 26:54Nobody is responding.
  • 26:57It clearly doesn't work.
  • 27:00Well, not so fast.
  • 27:02So it turns out that one patient did
  • 27:07respond and that patients Histology
  • 27:10ended up under my microscope and
  • 27:13it looked exactly like this.
  • 27:16And in fact, if any of you are are
  • 27:19familiar with Johns Hopkins,
  • 27:20it actually looked exactly like this.
  • 27:23So we were at one of these multi
  • 27:26headed scopes and Doctor Pardo,
  • 27:28a very well known tumor,
  • 27:30rheumatologist.
  • 27:30Here I was.
  • 27:31I was at this scope here or the head of
  • 27:36the scope and doctor Janice Tower was at
  • 27:39this scope and we looked underneath the
  • 27:42microscope at the one of 14 patients.
  • 27:47Who did respond to therapy
  • 27:49and this is what we saw.
  • 27:51This is the Histology,
  • 27:54typical Histology of mismatch
  • 27:56repair deficiency right all it's
  • 27:59almost medullary in quality.
  • 28:01There's a bunch of tumor infiltrating
  • 28:04lymphocytes. And you know what?
  • 28:07It's not surprising that
  • 28:09this patient responded.
  • 28:10To anti PD one therapy. Why?
  • 28:13Because everything is in place
  • 28:16and I'm I'll go through here and
  • 28:19and and and and map this out.
  • 28:23Mismatch repair deficiency.
  • 28:24They have a lot of antigens because
  • 28:26they have a lot of mutations.
  • 28:28How many mutations?
  • 28:29A lot right?
  • 28:31So here's a graph showing
  • 28:34the the frequency of of.
  • 28:37Of mutations and even in
  • 28:40something like a mutation,
  • 28:41associated cancer even MSI
  • 28:44here is orders of magnitude
  • 28:48greater in terms of antigens.
  • 28:51You know the way I think about
  • 28:52it is every one of these antigens
  • 28:54is is is is kind of like a.
  • 28:59Is kind of like a ticket to Lynn to
  • 29:01win the lottery and these people.
  • 29:03These people have thousands of tickets.
  • 29:06They also have lots of lymphocytes.
  • 29:08Lymphocytes.
  • 29:09We've known that every GI pathologist
  • 29:11known knows that we actually went in,
  • 29:14and our laboratory and measured it.
  • 29:16We measured it in the in,
  • 29:19in the tumor cells at the interface
  • 29:22between the tumor cells and the and the.
  • 29:27Are immune cells and this was work
  • 29:29of a fellow and and junior faculty
  • 29:32member at the time, and we were
  • 29:34able to count them and guess what?
  • 29:37It's not a surprise people with
  • 29:40MSI or mismatch repair deficiency.
  • 29:42They have more tumor infiltrating
  • 29:44lymphocytes. Well, what about PDL one?
  • 29:47Well, we were in the process of measuring
  • 29:50PD L1 in solid tumors like this.
  • 29:54Gastric cancer here. And what? Doctor.
  • 29:58Thompson and Basharat T here found was
  • 30:03that there's a lot of expression of
  • 30:06PD L1 at the tumor infiltrate edge.
  • 30:11The interesting thing is it's actually
  • 30:13not on the tumor cells which are up here.
  • 30:16It's actually at these immune cells
  • 30:18and then we went and quantified this
  • 30:20with Doctor Cruz lossa and by G here.
  • 30:26Everybody that I've shown by the way,
  • 30:28just as a as a as an aside here everybody
  • 30:30that I've shown who's who's rotated
  • 30:32through the lab or worked in the lab.
  • 30:34They're all faculty members.
  • 30:36So if you know if you're not even interested
  • 30:39in tumor immunology, but you want to,
  • 30:41you want to become a faculty member.
  • 30:42Just study tumor immunology.
  • 30:44The odds are in your favor,
  • 30:46so in the end, you know it's obvious.
  • 30:50Mismatch repair.
  • 30:51Deficient cancers have a lot of antigens.
  • 30:54They have a lot of lymphocytes and
  • 30:56they have a lot of PDL one expression.
  • 30:59So I'm going to show you this.
  • 31:01Particular publication here looking
  • 31:04at PD1 blockade in the setting
  • 31:07of mismatch repair deficiency.
  • 31:09There's 10 pathologists on this manual.
  • 31:15And somebody please mute who's
  • 31:17ever talking neuter phone.
  • 31:20Somebody is somebody talking. So
  • 31:23this study was presented in 2015
  • 31:27and one what they collected
  • 31:29were mismatch repair. Deficient cancers.
  • 31:35About 25 of each. They treated them
  • 31:38with PD one and it's no surprise that.
  • 31:45They responded well.
  • 31:46They were responded super well and
  • 31:49the thing that really catapulted
  • 31:52this particular finding was that.
  • 31:57The response rate here 62
  • 31:59versus 60 or 92 versus 70.
  • 32:02It didn't matter if they were colon cancers.
  • 32:05What mattered?
  • 32:08Was that they were mismatched
  • 32:09for pair deficient,
  • 32:11so suddenly now we move forward,
  • 32:14it's not mismatch repair,
  • 32:15deficient colon cancer,
  • 32:17it's mismatch repair, deficient,
  • 32:18any cancer, and that quickly catapulted.
  • 32:22Monk ologists at at at at Johns Hopkins.
  • 32:26Doctor Lee and Doctor Diaz to put
  • 32:30together a study in which the
  • 32:33enrollment criteria was mismatch
  • 32:36repair deficiency or MSI instability.
  • 32:39However you wish to call
  • 32:41it in any tumor type.
  • 32:43This is crazy.
  • 32:44We never thought that this would ever, ever.
  • 32:49B. Away we enrolled criteria patients right?
  • 32:54If you have a colon cancer,
  • 32:56you get one treatment.
  • 32:57If you have a pancreatic cancer,
  • 32:58you get a different treatment.
  • 33:01This was based on molecular diagnosis
  • 33:05and you know what it goes even crazier.
  • 33:08Because the FDA approved it.
  • 33:13Now I have sat with the FDA
  • 33:16for prolonged periods of time.
  • 33:19They are not very exciting people.
  • 33:24And the fact that they approved a
  • 33:27therapy independent of Histology
  • 33:29First off has never been done before,
  • 33:32so that's worth mentioning.
  • 33:34But the fact that they approved a
  • 33:37treatment independent of Histology,
  • 33:40and based on a non FDA approved test.
  • 33:45Are you kidding me?
  • 33:47But yet it's the standard of care today.
  • 33:51And it all came back.
  • 33:54To the fact that we were. Looking for.
  • 34:00Patients who did respond.
  • 34:03In a setting of patients who.
  • 34:06Largely didn't respond.
  • 34:11OK, so the thing that happens to me
  • 34:14most and it is probably happening
  • 34:17to doctor Shelper and Doctor Rim,
  • 34:20is let's find more biomarkers.
  • 34:24You guys are the pathologist tissue is
  • 34:27important. Let's find more biomarkers.
  • 34:30So how do we find more?
  • 34:33File markers. So.
  • 34:37Anyone who walks in my office.
  • 34:40The first thing that they hear is
  • 34:42there's no substitute for quality,
  • 34:44and that's true in terms of cancer biology,
  • 34:48and it's really a science of corollary.
  • 34:53And you know, for me,
  • 34:55how do you cure cancer?
  • 34:56You make high accurate or
  • 34:59highly accurate measurements.
  • 35:04And the emphasis here on
  • 35:07accurate and measurements.
  • 35:09Well then it brings up the
  • 35:11question how many measurements?
  • 35:13Do we need to make hundreds of measurements?
  • 35:16Four measurements,
  • 35:17you know we have that issue in front of us.
  • 35:20What are we going to do?
  • 35:21And that was the subject of a
  • 35:24beautiful paper done by one of our
  • 35:26graduate students here Steve Lu.
  • 35:28And what I'm showing you is
  • 35:31the receiver operator curve for
  • 35:33a number of different tests.
  • 35:36And for those of you who might not
  • 35:38be familiar with a narrow C curve,
  • 35:41the closer you get to this one.
  • 35:44The better so the green line
  • 35:47is better than the dashed line.
  • 35:50To put it simply. OK,
  • 35:53So what do these different lines represent?
  • 35:56And so this is the, UM.
  • 36:00This is a meta analysis in which
  • 36:03Steve Lew collected 55 studies
  • 36:07from 10 different tumor types,
  • 36:09encompassing over 8000 patients.
  • 36:11So this is about as comprehensive
  • 36:15as we could get in in 2019,
  • 36:17and what was determined.
  • 36:22Four response was either PDL.
  • 36:26One expression by immunohistochemistry,
  • 36:29that's the blue line.
  • 36:32Total mutational burden.
  • 36:35So that's you know the number
  • 36:37of mutations in a genome.
  • 36:39That's the red line. And then.
  • 36:43The expression of RNA markers.
  • 36:46Generally these are inflammatory markers.
  • 36:48That's the yellow wine.
  • 36:50And then in the green line.
  • 36:53Which is showing the best performance here?
  • 36:56All we needed. Was a minimum of two.
  • 37:02Emphasis on 2 markers.
  • 37:06And you can see the significant shift in
  • 37:10the arosi curve here with as few as two.
  • 37:16Immunohistochemistry markers.
  • 37:21So based on that,
  • 37:23on those types of findings and in
  • 37:27a lot of the work that we've done,
  • 37:29Doctor Janice Taube and I were
  • 37:32were charged with setting up the
  • 37:35tumor and microenvironment core.
  • 37:37We have a very experienced lab manager here.
  • 37:44It was set up through the Bloomberg Kimmel
  • 37:47Institute for Immune based Cancer therapy.
  • 37:52Essentially it's a Cancer Center resource.
  • 37:54Here the idea is that we would help
  • 37:57you measure tumor markers in the
  • 38:00tumor microenvironment and our current
  • 38:02holdings are are explained here.
  • 38:05We we offer hundreds of stains,
  • 38:08some of which we can do in two color and
  • 38:11some of which we can do in in multiple color.
  • 38:14We have a lot of machines in this
  • 38:19particular laboratory that we run.
  • 38:22We have 4. Our scanning machines.
  • 38:26We have two auto stainers.
  • 38:28We have an optical scanner and
  • 38:31a laser microdissection scope,
  • 38:33so that's to say you know we're
  • 38:36we're we're equipped as anyone
  • 38:38would expect us to be in order to,
  • 38:41you know, perform investigations
  • 38:43into the tumor microenvironment,
  • 38:46and you can see this is what
  • 38:48we're able to produce.
  • 38:49And again,
  • 38:50Doctor Shelper and and RIM have really
  • 38:53been leading the way in terms of.
  • 38:55Multi color immunofluorescence,
  • 38:57which is what we see here now.
  • 39:01The interesting thing is.
  • 39:03That when we broke down the the steps
  • 39:09of evaluating us a slide that's been
  • 39:14staying with immunofluorescence.
  • 39:17Janice and I found out there's
  • 39:19a lot of errors.
  • 39:21And those errors make evaluation
  • 39:24very very problematic.
  • 39:27So I'm summarizing the steps of of
  • 39:31staining something with multiple markers.
  • 39:34And and summarizing the errors here.
  • 39:39Such that the image acquisition off
  • 39:43of the microscope it was it turned
  • 39:46out that the spherical you know sort
  • 39:49of band or aperture of the objectives
  • 39:53was contributing almost to a two fold
  • 39:56error in in in image acquisition.
  • 39:58Well,
  • 39:58it's really hard to be sure what you're
  • 40:02measuring when there's a two fold error.
  • 40:05And again we systematically.
  • 40:08Went through and and looked at these
  • 40:11different errors the the method of
  • 40:15immunofluorescence is is indicated here.
  • 40:18It's a. It's a TSA method, it's actually a.
  • 40:24It's a covalent bond.
  • 40:27Such that.
  • 40:28When the two antibodies are present,
  • 40:31so this could be a CD3 antibody
  • 40:34and this would be an antibody
  • 40:37to detect this antibody.
  • 40:39In the presence of horseradish peroxidase.
  • 40:44This tyramide becomes inactive and
  • 40:48becomes active and covalently links
  • 40:52with the proteins that are nearby.
  • 40:55This is what allows us to do reciprocal.
  • 41:03Antibody staining because these
  • 41:05antibodies are then rinsed off while
  • 41:09their covalent binding is in place.
  • 41:14Uh, it's not easy to to acquire these images.
  • 41:19What I'm showing you here is
  • 41:22a typical lung cancer case.
  • 41:24Each square is a high power field.
  • 41:28Each each cancer then would be
  • 41:31represented by over a 1000 fields
  • 41:35which which is 75 gigabytes of data.
  • 41:40That's a lot of data.
  • 41:42For one patient.
  • 41:45So Janice and I were,
  • 41:46as pathologists were quickly overwhelmed
  • 41:50and we were so fortunate by Doctor
  • 41:54Jaffe to be pointed in the direction.
  • 41:59Of Doctor Alex Szalay who helped us
  • 42:03develop essentially the Astro pathology
  • 42:06platform and the idea is that Doctor Salay.
  • 42:09Here he is an expert in establishing
  • 42:14the database that's responsible
  • 42:17for imaging the entire sky.
  • 42:20Not your pretty daunting idea here and
  • 42:23then what Doctor Salay quickly realized
  • 42:25is that what Janice and I were trying to do.
  • 42:29On the microscopic level,
  • 42:31was really exactly what he had done at
  • 42:34the macroscopic level in establishing
  • 42:37the the Sloan Digital Sky server,
  • 42:40and if you've never done this before,
  • 42:42it's super cool.
  • 42:43You don't have to be in
  • 42:45astronomer or anything,
  • 42:47you can just log in as a regular
  • 42:49you know interested scientist,
  • 42:52person and and look at the sky.
  • 42:56And in fact,
  • 42:57there have been very high level publications.
  • 43:00Nature level publications that have
  • 43:03come out of. You know, sort of.
  • 43:07Public scientists going into this database
  • 43:11that doctor Slate helped establish
  • 43:15and and and finding things in a straw.
  • 43:20In the night sky that we
  • 43:22didn't even know existed.
  • 43:23So probably after the telescope.
  • 43:28This is the most important piece of.
  • 43:33Technology that we've had to look into
  • 43:36the skies and we again had just been so
  • 43:40so fortunate that this individual doctor
  • 43:43Salay was interested in our problems.
  • 43:47And again, I don't think it's
  • 43:49too complicated to think of.
  • 43:52Microscopes and telescopes are just
  • 43:54sort of the same problem, inverted.
  • 44:00So that led to this publication.
  • 44:04Where we described the method.
  • 44:07Now let let let's just step
  • 44:08back for a second here.
  • 44:10This is essentially a method describing.
  • 44:16How to interrogate in multiple colors?
  • 44:19Oh it was only seven or eight
  • 44:21colors in into human tissue and
  • 44:23it ended up with a high impact.
  • 44:26You know publication, I think.
  • 44:30That's really a testament to the
  • 44:33fact of how difficult this is.
  • 44:35What did we do in this paper?
  • 44:38We looked at metastatic melanomas
  • 44:41and found predictive biomarkers.
  • 44:43The discovery cohort came out of Johns
  • 44:46Hopkins in the validation cohort was
  • 44:48so graciously provided by Doctor Rim.
  • 44:52What? What was the what was the magic?
  • 44:57It was, it was cells that you and
  • 45:00I as tumor immunologists or even
  • 45:03as pathologists would predict.
  • 45:05It's you know PDL, one expression
  • 45:08Fox P3 expression CD 8 expression.
  • 45:12You know things that we know are important.
  • 45:14These are the the the predictive biomarkers.
  • 45:19Alright, so how can we improve upon multi
  • 45:23marker detection and cancer tissues?
  • 45:27So we're exploring adding more markers in,
  • 45:30but I don't actually think that is the key.
  • 45:33Using a codex of.
  • 45:37Method there's also RNA based methods.
  • 45:43Let's just get back down to you know,
  • 45:45I've been doing this for over a decade now.
  • 45:48And and here's my thoughts on on really
  • 45:50how to find predictive biomarkers.
  • 45:52First off, there's no substitute for quality.
  • 45:56If you don't know what you're
  • 45:57measuring or there's too much,
  • 45:58you know error and what you're measuring,
  • 46:00you're just looking at noise.
  • 46:02It's easy to generate noisy signals,
  • 46:05but it's difficult to, you know,
  • 46:07sort through that noise.
  • 46:10Don't overthink the analysis.
  • 46:13We need T cells and we need B cells
  • 46:15to make immune based, you know.
  • 46:19Cancer therapies that's sort of
  • 46:21what they're what they're aimed at.
  • 46:25I think we should focus on on spatial
  • 46:29relationships and that's described or or
  • 46:31or demonstrated here in the bottom figure.
  • 46:35So what I'm showing you here is
  • 46:37a tumor cell that's in white and
  • 46:39the T or B cells. I don't know.
  • 46:41Pick your favorite are are the yellow
  • 46:44dots here and something tells me.
  • 46:47That the image on the right.
  • 46:52With T or B cells surrounding the tumor cell.
  • 46:57He's probably more indicative of a tumor
  • 47:01immune response than the one over here yet.
  • 47:05By simple density,
  • 47:06we would report out the same numbers here.
  • 47:09So I think the spatial
  • 47:11relationships which interestingly
  • 47:15we're not part of this.
  • 47:17Publication this was simply a density.
  • 47:21Publication so I think spatial
  • 47:23relationships are very important.
  • 47:26The other thing is have a validation plan it.
  • 47:28There's no sense in in in measuring
  • 47:31a bunch of markers that you can't
  • 47:35secondarily validate like you know
  • 47:38that that's called doing science
  • 47:40without having a hypothesis.
  • 47:45And I think I think finding low frequency
  • 47:48markers at this stage is not the key.
  • 47:50Uhm, there's not some super
  • 47:53secret cell that's hiding deep
  • 47:55deep in these tumor tissues.
  • 47:58Like it's CD three,
  • 47:59it's CD 8 and it's the relationship of
  • 48:02those CD3 and CD8 cells to the tumor cells.
  • 48:05So I'll I'll wrap up here
  • 48:09with some conclusions.
  • 48:10You find cancer biomarkers by looking
  • 48:12in cancer tissue that should be great.
  • 48:15That should be great news to all of us.
  • 48:17As pathologists,
  • 48:18we can improve upon the evaluation
  • 48:21of PDL one as a biomarker,
  • 48:24and there are a lot of
  • 48:26efforts out there to do that.
  • 48:29Look for rare responders.
  • 48:31They those are individuals that
  • 48:34are really trying to tell you
  • 48:37something important and you know,
  • 48:39in our case here they were MSI.
  • 48:43Trust me, nobody wanted to fund this.
  • 48:46Nobody was interested in this.
  • 48:49Nobody, despite the fact that
  • 48:51we were standing there saying
  • 48:53these are MSI associated cancers.
  • 48:56So you really need to
  • 48:57make your argument here.
  • 48:59Focus on the people who do respond.
  • 49:02And then I think high quality analysis
  • 49:05are important and you're all very
  • 49:07lucky to have individuals again,
  • 49:09like Doctor Rim and Shelper who are
  • 49:12interested in making high quality.
  • 49:14You know tissue biomarkers.
  • 49:16I'll end here.
  • 49:18It's impossible to do this all in isolation.
  • 49:22I tried to point out the
  • 49:26key people along the way.
  • 49:28And and those in and and many
  • 49:30more of the individuals who work
  • 49:32in my laboratory or who have
  • 49:34collaborated with me over the
  • 49:36years are are listed here.
  • 49:38And I'm gonna stop right there.
  • 49:39And thank goodness because I'm exhausted.
  • 49:46Thank you Bob for that really,
  • 49:50really elegant and and sort of.
  • 49:53Focusing on very high points and meticulous
  • 49:57points of quality that we might not
  • 49:59always here in talks about Peter one
  • 50:02another biomarkers and I'm I'm, you know,
  • 50:04there's a silent applause going on,
  • 50:06which is so missed in zoom,
  • 50:08but it's definitely happening.
  • 50:09We have a few minutes for questions
  • 50:12and please, since I can't see,
  • 50:14I don't know if you wanna stop your share.
  • 50:16Or maybe you want to keep it on 'cause
  • 50:18people might want to go back to slides so,
  • 50:20but please just speak out
  • 50:22with your questions.
  • 50:24And I'll start with one just
  • 50:25to get the ball rolling.
  • 50:26I actually have two things
  • 50:28I wanted to discuss,
  • 50:29but one is I'm just gonna start with.
  • 50:31I was really intrigued by and
  • 50:34thank you for talking about.
  • 50:37You had a slide on errors in Multiplex
  • 50:41immunofluorescence and you showed
  • 50:43multiple steps along the way where
  • 50:45you found errors and I'm curious.
  • 50:47I wonder if you can say just
  • 50:48a minute about that.
  • 50:49How did you detect the errors and
  • 50:52did are you able to fix the errors?
  • 50:54And is this something that
  • 50:56everyone who's publishing in this
  • 50:57or are they as meticulous?
  • 50:59You know they doing this careful
  • 51:01detection that you're doing?
  • 51:02Can you just talk a little
  • 51:03bit more about this?
  • 51:04Yeah, sure great great question so.
  • 51:07Some you know, Janice and I are
  • 51:09ultimately were pathologists, and,
  • 51:11you know, for us what we would do is
  • 51:14everything had to be referenced back
  • 51:16to a a brown state or an IHC state.
  • 51:20So anything that we were doing,
  • 51:23whether it's digital image
  • 51:25analysis or image acquisition,
  • 51:27they were always put against
  • 51:30the gold standard and the gold
  • 51:33standard was immunohistochemistry.
  • 51:35So that's kind of how we were
  • 51:37able to go back and say, oh look.
  • 51:39The the the images are not being
  • 51:43acquired in a in a flat manner, or.
  • 51:46There is over acquisition of cells in it,
  • 51:51you know, and any reasonable observer
  • 51:53learned weren't deemed to be positive,
  • 51:56you know? In the end, you know.
  • 52:01That's a science paper,
  • 52:03and it's a methods paper which
  • 52:05is not super common. Right?
  • 52:07Like when we when we were putting this
  • 52:10together, a lot of people said, oh,
  • 52:12you know that that's just a methods paper.
  • 52:15But but I think.
  • 52:17I think it's recognized how difficult it is.
  • 52:20It took us five years to do it.
  • 52:23To to kind of sort out the
  • 52:25errors at each step.
  • 52:29And and then it is. This are those
  • 52:32errors likely to be possible in
  • 52:36in anyone doing Multiplex IF.
  • 52:39If you use our,
  • 52:40if you use the same technologies
  • 52:43which is akoya Biosystems, yeah.
  • 52:46That you know they're the good.
  • 52:48The good thing is,
  • 52:49once we figure out the errors,
  • 52:51we can send you the code that will fix them.
  • 52:55You know it's you know if a
  • 52:58if a if an objective is not.
  • 53:01Perfectly round or perfectly
  • 53:04flat in acquiring there,
  • 53:06there are easy easy.
  • 53:10As a non computer person there are
  • 53:12easier ways to to fix that, so yeah,
  • 53:15well as you say we have experts here
  • 53:17and some of them may be on the the
  • 53:20grand rounds so I'm sure they can.
  • 53:22They can speak to this as well,
  • 53:23'cause they're excellent pay.
  • 53:25I see you have your hand up.
  • 53:27Go ahead and then Dave who's done
  • 53:31this? Can you hear me?
  • 53:33Yes, OK thank you.
  • 53:34Thanks for the excellent talk.
  • 53:36I enjoy a lot so I have
  • 53:37a question about the MSI.
  • 53:40NMR question about the
  • 53:42testing at your site. Do
  • 53:45you run both
  • 53:46tests in molecular or
  • 53:48immunohistochemistry? Or you
  • 53:49select just one?
  • 53:51Yeah, great question.
  • 53:53It's probably after is PDL 1A.
  • 53:56Good biomarker,
  • 53:57it's which MSI should I or Mr?
  • 54:00Should I be testing so you know,
  • 54:01Doctor Vogelstein,
  • 54:02you know one of our most
  • 54:05eminent cancer biologists here.
  • 54:07You know he was really instrumental and.
  • 54:10You know, in in in figuring out
  • 54:12the MSI along with Stan Hamilton,
  • 54:14another GI pathologist,
  • 54:16I see Dave Pat Jane is on and
  • 54:19a few other GI pathologists.
  • 54:24We're very much a PCR based system.
  • 54:30I do believe that if a
  • 54:33patient is Ms is PCR negative,
  • 54:36it's probably reasonable to go back and
  • 54:40and do a stain for MMR just because.
  • 54:43You're almost talking about a
  • 54:46cure in 50 to 70% of patients.
  • 54:49And and I don't think we can.
  • 54:53I don't think we can miss.
  • 54:55Potential cure.
  • 54:56I do believe that the performance of of
  • 55:00PCR and MMR testing are about the same,
  • 55:05but they both have their their weaknesses.
  • 55:08Thanks for that.
  • 55:10Our notion so actually
  • 55:13hear different services
  • 55:15actually approach differently.
  • 55:18GI service using them are
  • 55:20sold at giant service.
  • 55:22Yeah, exactly what you just said.
  • 55:24I think our geologist they
  • 55:27don't want to miss a single patient
  • 55:28so we know very well you know
  • 55:31the some of the MSI high tumors
  • 55:33may turn out to be a memory.
  • 55:35You know, you know if you you
  • 55:36know can be normal. Yeah,
  • 55:38and it seems like it happens the same way.
  • 55:40That's why I think now in
  • 55:42the answer is here we do.
  • 55:44Simultaneous testing for both methods.
  • 55:47So with the sense to
  • 55:48capture. All possible patients?
  • 55:50Yeah, especially, especially since you know
  • 55:52it has to do with the turnover of the cells.
  • 55:56You know baseline and you know
  • 55:59you wanna get it. Oncologist mad.
  • 56:01Tell him that you know MSI PCR or MMR.
  • 56:06You know HC hasn't really been.
  • 56:09I mean none of its FDA approved number one,
  • 56:11but it's never really been vetted
  • 56:13on all of these different tissues
  • 56:16yet we're doing it all the time.
  • 56:18You know that'll make their brain melt, so
  • 56:20I just wanna go to David you,
  • 56:23you did a show us yourself did you one?
  • 56:25Then I see curtain Uma Dave please
  • 56:27go ahead. If you had a comment
  • 56:29I just wanted to follow
  • 56:31up with your concerns about.
  • 56:33Standardization of quantitative
  • 56:35fluorescence and bobs showing of
  • 56:38the problems with each one and
  • 56:39and comment about the MITRE study
  • 56:41which is actually being led by
  • 56:42Janice Bob's colleague at Hopkins,
  • 56:44which is a standardization study
  • 56:46of quantitative fluorescence,
  • 56:47and I think that will address that
  • 56:49question that you asked Marie exactly,
  • 56:51but as the minor one study was where
  • 56:55the data analysis was all done
  • 56:56at one site and the
  • 56:57scanning was done at 6 sites might
  • 56:59or two study will have the scanning
  • 57:01and analysis done at 6 sites.
  • 57:03And that will answer your question,
  • 57:05and Bob will probably participate in
  • 57:06that since it's being led by Janice.
  • 57:09Well, I have complete faith in
  • 57:11our unit here, that's for sure.
  • 57:13Thank you. I see Kurt and and then Uma.
  • 57:18OK, then can you hear
  • 57:20me? Yes, Yep we can hear it.
  • 57:22So when one thing we
  • 57:23touch on before Bob and I think
  • 57:26it's becoming more and more relevant
  • 57:27is the rapid shrinkage in and
  • 57:30sometimes the disappearance of
  • 57:32tissue samples in the eminent role
  • 57:35of circulating toward DNA to call.
  • 57:37You know, Microsoft instability status,
  • 57:40TMB and other things?
  • 57:41How do you foresee some of these
  • 57:45immune biology or immune biomarkers?
  • 57:46Adapting to circulating tumor DNA?
  • 57:48Do you think that something?
  • 57:50That we'll pair will and and how to,
  • 57:52you know, marry with our usual, you know,
  • 57:55diagnostic operation that is tissue based.
  • 57:58Yeah, that that's a great question
  • 58:00because you know not not every
  • 58:02patient is biopsy able right?
  • 58:04I mean, we we we kind of wish they were,
  • 58:07but it's it's not. And you know,
  • 58:09I you know I would like to see.
  • 58:11I would like to see a study of just
  • 58:13immune cell activation, right?
  • 58:15Like like someone who gets CMV if you can't
  • 58:19measure CMV like can you just tell that
  • 58:22their immune system is activating right?
  • 58:25And and I kind of think that the
  • 58:27same might be true. Then you know,
  • 58:30for for tumor immunologists it's just.
  • 58:33Hey we gave PDL one is the
  • 58:36patient's immune system activating?
  • 58:37You know, I don't.
  • 58:38I don't know what that entails,
  • 58:40but you know is there a way to
  • 58:43to not relatively non invasively
  • 58:45you know with a blood sample?
  • 58:51Measure that because again,
  • 58:53not every patient.
  • 58:54You know we're pathologists,
  • 58:56we love tissue.
  • 58:57I just told you how awesome tissue is,
  • 58:59but it's not always available,
  • 59:02so I I'm not sure if I
  • 59:03answered your question.
  • 59:04Are you currently testing MSI
  • 59:06status by city DNA at Hopkins now?
  • 59:10Even I haven't seen that outside
  • 59:13of research. OK, thank you.
  • 59:15Uma yeah thank you Doctor Andrews.
  • 59:18That is a wonderful talk.
  • 59:19My question was on heterogeneity
  • 59:21in a study I did with 100 plus you
  • 59:26know cervical and anal cancers.
  • 59:2895% were heterogeneous in their
  • 59:30staining with Foo side dead
  • 59:32negative tofu side with 100%.
  • 59:34So for an individual patient,
  • 59:36is it going to be a luck of the
  • 59:38draw when you have cut off someone
  • 59:40and 10 how much should they be?
  • 59:42You know what will be the guideline?
  • 59:44What are your thoughts on
  • 59:45that? Yeah, yeah, that's that's really.
  • 59:48That's really important, right?
  • 59:50I kind of think it's like one of these
  • 59:53things where if it's positive we learn
  • 59:56something, but if it's negative.
  • 59:58We didn't really learn anything and and
  • 01:00:01and I think that you know that's where we
  • 01:00:03need to help our immunologists or our.
  • 01:00:06I'm sorry our oncologists to say.
  • 01:00:09You know, a negative result
  • 01:00:10shouldn't really dissuade you here,
  • 01:00:12but we're a long way from that. And yeah,
  • 01:00:15any any look at heterogeneity and I,
  • 01:00:18I know Kurt and I talked about.
  • 01:00:20You know, some actually mathematical
  • 01:00:22models of of heterogeneity.
  • 01:00:24I think it's all important.
  • 01:00:27Thank you.
  • 01:00:32Other questions.
  • 01:00:34And I'll, I'll I'll ask my last one,
  • 01:00:35which just pivots off of Kurt's
  • 01:00:39comment about a circulating tumor.
  • 01:00:41You looking for markers and
  • 01:00:43circulating tumor cells.
  • 01:00:44And so I was just going to ask the
  • 01:00:47classic question, what's next?
  • 01:00:49What is the future of this?
  • 01:00:53Especially since relying
  • 01:00:54on MMR is is is easy.
  • 01:00:58Staying pretty easy,
  • 01:00:58stain to read but PDL.
  • 01:01:00One has challenges and and then there
  • 01:01:02are just things deeper than this.
  • 01:01:04That Kurt and Dave and you are working on
  • 01:01:07that are deeper than than just PDL one.
  • 01:01:10So how far are we from getting to a
  • 01:01:13blood test talking about not being
  • 01:01:16people? My talk about blood tests
  • 01:01:19'cause I you know, I don't know that
  • 01:01:21you know I was talking with Kurt a
  • 01:01:23little bit earlier as like you know I.
  • 01:01:25I would like to see some CD 8 cells and
  • 01:01:28some CD 20 cells in a tumor. Right?
  • 01:01:31Like are they close to the tumor?
  • 01:01:33Are they not close to the tumor,
  • 01:01:35or are they present?
  • 01:01:36Are they not present like?
  • 01:01:39I I think I think we have so many basic
  • 01:01:41things that we could be measuring
  • 01:01:43here and and we're all and and I'm
  • 01:01:46not blaming anyone on the call here,
  • 01:01:48but we're all like can you
  • 01:01:50do 4000 markers right?
  • 01:01:53Like and you know,
  • 01:01:55I bet you we could spitball
  • 01:01:57four or five important markers.
  • 01:02:01So yeah, dhanpat, sorry.
  • 01:02:04I think we are all your time.
  • 01:02:06I was in the liver tumor board
  • 01:02:07so I had to attend something.
  • 01:02:09Great. Talk Bob. Nice seeing you.
  • 01:02:12I have a question which is sort of
  • 01:02:14follow-up question to what Doctor
  • 01:02:15Who was asking and I want to clarify.
  • 01:02:17So I understand that you guys do
  • 01:02:19only the MSI as the starting test,
  • 01:02:21but you don't do them are to begin with.
  • 01:02:24Is that correct or you do both?
  • 01:02:26We
  • 01:02:27we tend to heavily rely on MSI.
  • 01:02:31I see the issue I think is I think
  • 01:02:35what Doctor Who was asking was,
  • 01:02:37you know whether do both
  • 01:02:38or do you only one person?
  • 01:02:40As I understand, most of those
  • 01:02:42centers do either one of the tests
  • 01:02:44and what you said is correct that
  • 01:02:46none of the tests will ever be 100%.
  • 01:02:48Like many other things that we do
  • 01:02:50and you always accept the yeah Greer
  • 01:02:53false positive or false negative you
  • 01:02:55get with each test is like how many
  • 01:02:57blocks will you stay in for PDL one
  • 01:03:00or a given case you end somewhere.
  • 01:03:02So, and I think a lot of people
  • 01:03:05tend to go with DNA mismatch repair,
  • 01:03:08yet see for two reasons that the
  • 01:03:11sensitivity is basically almost
  • 01:03:13as close to MSI.
  • 01:03:14But in addition also identifies the germ
  • 01:03:18line defect that one should be looking for.
  • 01:03:20If you're screening for is syndrome,
  • 01:03:22so there is added advantage.
  • 01:03:24But I I agree.
  • 01:03:25I mean there are pitfalls of each methods
  • 01:03:27and you will miss a few no matter what.
  • 01:03:30Yeah, yes, we're we're on the same page.
  • 01:03:34You know, I you know,
  • 01:03:35sometimes with a limited biopsy,
  • 01:03:38you don't really know if
  • 01:03:39you're getting that you know.
  • 01:03:40Are you really getting the cancer DNA,
  • 01:03:43but with an IHC you can, you know?
  • 01:03:45Clearly see whether it's
  • 01:03:47the cancer or not. So yeah.
  • 01:03:51Oh, I'm sorry. Joanna, and then
  • 01:03:54I think Jeff might. I don't know,
  • 01:03:56or I I actually.
  • 01:03:57I like this discussion that
  • 01:03:59people have been having about MRI,
  • 01:04:01HC versus MSI PCR.
  • 01:04:03I think we don't really need to
  • 01:04:05worry about which method is better,
  • 01:04:08but I do think that we need to understand
  • 01:04:10what information each method provides
  • 01:04:12and why each method was developed.
  • 01:04:14And also I really liked your
  • 01:04:16comment towards the end of your
  • 01:04:17talk where you said we're doing
  • 01:04:19MSI testing on all of these tumors.
  • 01:04:21MRI HD on all of these tumors
  • 01:04:22now because of treatment.
  • 01:04:23Options and we really don't know.
  • 01:04:25What you know? How?
  • 01:04:28How this how these MRI,
  • 01:04:30HC behave and tumors outside
  • 01:04:32of endometrial and colorectal
  • 01:04:33cancer to the same extent that we
  • 01:04:36understand in those original pores.
  • 01:04:37And I think you know,
  • 01:04:39I think doing doing MSI PCR in
  • 01:04:43addition like is not really a
  • 01:04:44problem for those cases where it
  • 01:04:46is really important for treatment.
  • 01:04:48So I think we just need to really
  • 01:04:49keep in mind why are we doing this
  • 01:04:51task right now at this very moment?
  • 01:04:53What is the purpose of it?
  • 01:04:55Is it for lynching of screening?
  • 01:04:56Is it? For. Ms for treatment?
  • 01:04:59Or is it for both and then we really
  • 01:05:00need to sort of be cognizant that
  • 01:05:02what information we get out of
  • 01:05:04it and how it's gonna be helpful.
  • 01:05:05So I I,
  • 01:05:06I really liked your summary and your
  • 01:05:08and and the fact that you brought
  • 01:05:09up my side. Yeah, I think we're,
  • 01:05:11you know we're speaking the same,
  • 01:05:13the same language and you know
  • 01:05:15I'm I'm I'm on a few of these,
  • 01:05:17you know like advisory boards and
  • 01:05:19you start telling oncologists that
  • 01:05:21none of these tests have been valid.
  • 01:05:23I mean number one not nothing is FDA
  • 01:05:25approved but they haven't been validated
  • 01:05:27in other cancers and they just.
  • 01:05:30Their their mind is. Dissolves.
  • 01:05:34Just one, there's a limitation.
  • 01:05:37I have a question.
  • 01:05:38I want to go back to leave in the middle
  • 01:05:40so I might have missed some things,
  • 01:05:41but I'm gonna go back to a point you
  • 01:05:44made early on about the negative
  • 01:05:46predictive value of absence of
  • 01:05:48ligands or PDL one PD one in tumors.
  • 01:05:53What is the thinking about
  • 01:05:55those patients who respond?
  • 01:05:58When they have no detectable antigen,
  • 01:06:01no detectable PDL want.
  • 01:06:02Is it that the test is
  • 01:06:04insufficiently sensitive?
  • 01:06:05That doesn't explain at all,
  • 01:06:07I don't think.
  • 01:06:08And is it a matter of these
  • 01:06:10other factors that they had high
  • 01:06:12mutation rates but nevertheless
  • 01:06:13had low PDL one so that compensates
  • 01:06:16for the absence of PDL one?
  • 01:06:19What's understood about the
  • 01:06:20biology of these responders?
  • 01:06:21Who were PDL?
  • 01:06:22One negative?
  • 01:06:23Yeah, I mean I think number one,
  • 01:06:25it's just you know it.
  • 01:06:26It is a concern that it's
  • 01:06:27a sampling issue, right?
  • 01:06:29We have a tiny piece of a large
  • 01:06:31tumor and the second thing would
  • 01:06:33be you know if the TMB is high.
  • 01:06:35There's not an oncologist who won't.
  • 01:06:38Go for, you know,
  • 01:06:39won't treat those types of patients.
  • 01:06:44So I you know, I don't.
  • 01:06:45I don't think we have detailed
  • 01:06:48answers about I, I'm actually.
  • 01:06:50I'm actually more interested in
  • 01:06:52the people who are PD L1 positive
  • 01:06:55who don't respond or or design
  • 01:06:58positive who don't respond right.
  • 01:07:00Everybody is interested in those,
  • 01:07:01but I think that there might be value in
  • 01:07:04the ones who are the atypical responders.
  • 01:07:07Yeah, yeah, the drug has the drugs
  • 01:07:10I assume are incredibly specific.
  • 01:07:13It's not that they're often responses.
  • 01:07:17Sorry, can I come in on that quickly?
  • 01:07:18No, go ahead, Kurt, yes.
  • 01:07:20So so there is a story published a
  • 01:07:22couple of papers showing that PDL one
  • 01:07:25can get glycosylated and when it gets
  • 01:07:28glycosylated antibodies may not recognize it.
  • 01:07:30So that's at least one direct
  • 01:07:32biological ground by which patients
  • 01:07:34may be false negative for PD L1.
  • 01:07:37I could explain why some
  • 01:07:39negative cases respond.
  • 01:07:40That's one thing that's been sort
  • 01:07:43of well established and and this is
  • 01:07:45a work from Indy Anderson Group.
  • 01:07:47And then the second layer is that
  • 01:07:49tumor mutational burden and PDL one
  • 01:07:52are not correlated in most tumors.
  • 01:07:54So to your point it is likely that
  • 01:07:55some of those period one negatives may
  • 01:07:57actually be TNB high and that could
  • 01:07:59be the driver of a clinical benefit.
  • 01:08:01So it could be you know around those
  • 01:08:04topics and in some unknown things for sure.
  • 01:08:08But presumably response has to
  • 01:08:10be through the PDL 1.
  • 01:08:13PD One you know circuit because.
  • 01:08:17That's what's with the drug works, right?
  • 01:08:19That's the part I'm doing.
  • 01:08:21There may be other things,
  • 01:08:22yeah?
  • 01:08:25OK thanks.
  • 01:08:29Great, great discussion. Well,
  • 01:08:31if there are no further questions,
  • 01:08:34we're 10 minutes over which I always
  • 01:08:36love because it means that this
  • 01:08:38is really an interesting topic.
  • 01:08:40I want to thank you Bob for spending
  • 01:08:43time with us today and for meeting
  • 01:08:45with several of us this morning.
  • 01:08:47It's been a real pleasure
  • 01:08:50and an education as well.
  • 01:08:52Congratulations on your wonderful work.
  • 01:08:55Thanks for the invite and all the
  • 01:08:57time this. Yeah. With everyone,
  • 01:09:00thank you. Bye bye OK bye.