Endocrine Adverse Events with Cancer Therapies

Transcript

• 00:00US online. It's my great pleasure today
• 00:02to introduce our Grand Round speaker,
• 00:04Doctor Kevin Harold.
• 00:05I've known Doctor Harold,
• 00:07it turns out, for 10 years now.
• 00:08We just figured it out.
• 00:10We met at the bedside.
• 00:12And I think for the fellows in the audience,
• 00:14this is a this hopefully will be a teaching
• 00:16moment because you get a sick patient,
• 00:18you're not sure what's wrong with him.
• 00:19You call the expert.
• 00:21And from that, we developed an
• 00:23entire universe of research projects,
• 00:25grants and so on that Doctor Harold
• 00:27will be talking about today.
• 00:29To me that exemplifies the beauty
• 00:31of Yale University and what we're
• 00:34about unusual clinical circumstances
• 00:35taken back to the bench,
• 00:37going back to the clinic,
• 00:38etcetera, etcetera.
• 00:39But the best part of it
• 00:41all is the collegiality.
• 00:43So I'm just remembering my
• 00:45first after we got our funding,
• 00:46the very first research meeting that
• 00:48I had with Doctor Harold and Doctor
• 00:50Eric Murphy, who's since left Yale.
• 00:52Yeah, I'm not an immunologist, but they are.
• 00:55They.
• 00:55They both are card carrying
• 00:57immunologists and Doctor Mephre in
• 00:59particular doesn't tolerate fools.
• 01:01So I was really intimidated by this
• 01:03meeting and I had established ground rules.
• 01:05I don't know if Doctor Harold remembers this.
• 01:07We decided that this is an idiot free zone.
• 01:10We're all smart,
• 01:10we can say whatever we like and
• 01:12we never have to be embarrassed.
• 01:13And I think that that principle
• 01:15has LED us in the last 10 years
• 01:18because it turns out that even I had
• 01:20something to bring to the table here.
• 01:22So collegiality, respect,
• 01:24creativity has led to a whole
• 01:26field that I think we've opened
• 01:29up here in translational research
• 01:31on immune related adverse events
• 01:34for endocrine toxicities.
• 01:35So other than this whole world Doctor
• 01:38Harold is actually really famous for
• 01:41delaying type one diabetes in kids,
• 01:44a major breakthrough in delivering
• 01:46CD3 antibodies to children who
• 01:49had started to develop type one
• 01:51diabetes giving them the anti CD3
• 01:54antibody delaying the onset of
• 01:56full blown islet cell destruction.
• 01:59I don't think he's going to be
• 02:00talking about that today,
• 02:01but today we look forward to
• 02:03listening to all the cancer related
• 02:04studies that he's done.
• 02:05So without further ado,
• 02:07thank you Doctor Harold for taking the time.
• 02:15OK, thank you very much Harriet
• 02:17for that very kind introduction.
• 02:19I, I, I I have to admit I I was also
• 02:21quite pleased that we were going
• 02:23to set up our research meeting.
• 02:25So there'll be no it would
• 02:26be an idiot free zone.
• 02:28I I I appreciated that.
• 02:32So here's my disclosures.
• 02:37So hopefully this is review to everyone
• 02:41that that basically we we live in
• 02:44a constant immunologic equilibrium
• 02:47balancing lymphocyte activation
• 02:49and control and the activation is
• 02:53controlled by a number of Co simulatory
• 02:56molecules and recognition by antigen
• 02:59by T cells and other immune cells.
• 03:04And we the the the major developments in
• 03:06the cancer field of course are that by
• 03:09disrupting this balance we can develop
• 03:12effective ways of treating cancers.
• 03:14And and indeed this has revolutionized
• 03:18the field over the past decade and
• 03:20it became very clear initially when
• 03:23these agents became available for
• 03:26clinical use that there were adverse
• 03:29events that would occur as well
• 03:31since the balance that prevents
• 03:33us from developing autoimmunity is
• 03:36controlled by the same mechanisms.
• 03:38And we and it's been established
• 03:40for many years that even normal
• 03:42patients have immune cells that are
• 03:45capable of recognizing self antigens.
• 03:47So by tipping this balance it's fairly
• 03:50clear that one would be able to develop
• 03:53autoimmune diseases and that's that's
• 03:56what I'm going to be talking about today.
• 03:59Now the endocrine organs seem to be
• 04:01particularly vulnerable to immune related
• 04:04adverse events with with biologic
• 04:07therapy particularly with checkpoint
• 04:10inhibitors and you can this is from a
• 04:13review that came out a number of years ago,
• 04:16but there are many,
• 04:17many organs that are affected.
• 04:19I've on the right side we see just the
• 04:22endocrine organs that are affected.
• 04:25Fibroid disease is the most common and
• 04:29frankly can be over 15% in some series
• 04:36and the second most common is pituitary
• 04:39disease that can be difficult to diagnose,
• 04:42certainly important to diagnose.
• 04:45And then the other endocrine organs seem
• 04:49to be affected as well including the
• 04:51the insulin producing beta cells that
• 04:54leads to the development of diabetes.
• 04:56Now I would point out from this graph
• 05:00that that the development of these
• 05:04adverse events are most common with
• 05:07combination therapies and this is going
• 05:09to come up again in some of the data.
• 05:11I'm going to present to you that the
• 05:14combination of anti C2A4 plus anti PD
• 05:19one or PDL one seems to be seems to
• 05:23impart a higher risk of developing these
• 05:25adverse events than either agent alone.
• 05:29So the timing of them varies a bit.
• 05:32And sometimes we, as a practical matter,
• 05:36have a hard time determining whether
• 05:38or not an adverse event that we may see
• 05:42is directly related to the checkpoint
• 05:44inhibitor that's been given or whether
• 05:47it was just happening by chance.
• 05:49Because some of these adverse events
• 05:51such as thyroid disease or diabetes are
• 05:54relatively common in the population,
• 05:56particularly in the older population.
• 05:58But this graph shows you the timing of
• 06:01some of the more common adverse events.
• 06:04You can see that hypothesitis can happen
• 06:08several weeks after the development after
• 06:11a checkpoint inhibitors are administered.
• 06:14Some of the others that are that
• 06:17are also quite common tend to occur
• 06:20in a more acute manner.
• 06:22Now as Harriet mentioned we started
• 06:27I'm going to spend most of my the rest
• 06:30of the talk talking about checkpoint
• 06:32induced autoimmune diabetes because
• 06:34that's where we've done the most,
• 06:36the most work.
• 06:37And let me just make it mention
• 06:39one thing about some of the others.
• 06:40You know I I I do want to say sort
• 06:44of upfront that that the mechanisms
• 06:46of some of these other checkpoint
• 06:49induced endocrine adverse events are
• 06:52not very well worked out at all.
• 06:54There is really one sort of lead paper
• 06:57that described the development of
• 07:00autoimmune hypothesitis that talked
• 07:02about expression of C of CTLA 4 on
• 07:05pituitary cells and suggested that
• 07:07what happened with anti CTLA 4 is
• 07:10that the antibodies bound to CTLA
• 07:124 on the pituitary fixed complement
• 07:14and destroyed the cells.
• 07:16But if you go through the paper carefully,
• 07:18you'll see that, well,
• 07:19it really wasn't sort of.
• 07:21It wasn't the ACTH producing cells,
• 07:23which is a common manifestation
• 07:26of hypothesitis,
• 07:26it was prolactin producing cells
• 07:29and and also TSH producing cells.
• 07:32So the precise mechanisms there
• 07:34really aren't quite so clear.
• 07:36Likewise for thyroid disease.
• 07:37I think it's still somewhat of
• 07:40an unknown or a wide open area
• 07:42for investigation I should say to
• 07:45understand the mechanisms.
• 07:46But we focused our attention on
• 07:48autoimmune diabetes and hopefully
• 07:50have made some inroads into
• 07:52understanding the mechanisms here.
• 07:54And our work began as,
• 07:55as I I pointed out to Harriet,
• 07:57if you take a look at the date on this,
• 08:00this paper almost a decade ago
• 08:05when the patient #1 here was
• 08:08referred to me by Doctor Kluger.
• 08:11And the IT was a woman with Melanoma
• 08:16who have been treated with IPI and also
• 08:21had gotten nivolumab at that point and
• 08:24presented with diabetic ketoacidosis.
• 08:26And you know this was quite striking.
• 08:29This is someone who's 55.
• 08:30And then subsequently there were a
• 08:32number of other cases that came from
• 08:35Yale of people over the age of 50 who
• 08:40were presenting with ketoacidosis
• 08:42often and new onset hyperglycemia.
• 08:45And this was kind of striking and
• 08:49to me it was striking because you
• 08:52know we hadn't seen it before the
• 08:54the the anti PD one drugs were new
• 08:58at that time but we had had anti
• 09:01CTLA 4 Ipilimab for a number of years.
• 09:04And so that was kind of kind of striking.
• 09:07So we ended up putting these series together
• 09:09and this I I know I mentioned this the
• 09:11last time I spoke but I I want to kind of
• 09:14bring this point up again particularly
• 09:16for the trainees who are here and and
• 09:21the the data that we've subsequently
• 09:23had even makes the point even further.
• 09:26So we we put this series
• 09:27together and we send it in,
• 09:28we send it into the endocrine
• 09:30journals for publication.
• 09:31And you know a lot of people,
• 09:34a lot of the journals or some
• 09:36of the journals didn't weren't
• 09:37weren't interested in it.
• 09:39And then finally it goes to one of
• 09:41the leading endocrine journals and
• 09:42it's sent out for review and we get
• 09:45comments back from the review and
• 09:47and we did a very extensive job
• 09:49answering all the all the comments.
• 09:51There were 12 pages of of responses
• 09:54and so we sent it back and and the
• 09:58reviewer comes back and says well
• 10:01if you know if this was really
• 10:05occurring the development of of
• 10:08diabetes after anti PD one we would
• 10:10have known about it already.
• 10:12So that that was the end of that journal.
• 10:14So we ended up publishing this as a
• 10:17letter actually in diabetes care and
• 10:19it is one of the most highly cited,
• 10:22certainly one of the most highly cited
• 10:25papers in diabetes care that that is
• 10:27the first description of anti PD1 antibodies.
• 10:30So the reason I wanted to mention
• 10:31this story to you is as I'm going to,
• 10:33as I'm going to show you later on that
• 10:36not only was the reviewer wrong in
• 10:38saying that we would have known about it,
• 10:42but mechanistically now we know
• 10:43why the reviewer was wrong.
• 10:45So that's kind of nice to know
• 10:47why your reviewer is so wrong.
• 10:49So what?
• 10:50What what is,
• 10:51what are some of the features
• 10:53of this form of of of diabetes.
• 10:55So first of all,
• 10:57it happens relatively very acutely.
• 10:59Here's here's some data.
• 11:01This is coming from our colleagues
• 11:03at UCSF where we've put together
• 11:04patients at the two institutions
• 11:06and you can see this here are a few
• 11:08patients who developed checkpoint
• 11:10induced diabetes and their blood
• 11:12sugars are completely normal And then
• 11:15dramatically there is a big spike
• 11:17in their in their glucose levels.
• 11:19And the other thing that's that's
• 11:21quite interesting about that is if
• 11:23you look at their endogenous beta
• 11:25cell function by measuring C peptide,
• 11:27remember C peptide is cleaved from
• 11:29pro insulin when the beta cells
• 11:31make insulin and it's a good measure
• 11:34of endogenous insulin production
• 11:35cause the insulin you inject doesn't
• 11:38have C peptide.
• 11:39So if you take a look at the kinetics of
• 11:42loss of C peptide here that it happens very,
• 11:46very quickly.
• 11:47In fact in one case it it happened
• 11:50while patients were following the
• 11:52the individual while investigators
• 11:53were following the individual in the
• 11:56hospital. And the other point about
• 11:58this is patients generally go to
• 12:010 or near 0 in other words levels
• 12:04that are clinically insufficient.
• 12:06We'll come back to that later on.
• 12:09Here's a few other bits of information
• 12:12about the demographics of patients,
• 12:15so you can see the age.
• 12:17These are people who are older than you
• 12:22might expect with presenting with diabetes.
• 12:25It generally occurs with anti
• 12:28PD ONE or anti PDL 1.
• 12:30The hemoglobin A1 CS are elevated at probably
• 12:34because of the degree of hyperglycemia.
• 12:37About half of the patients are OR and
• 12:40depending on the review some even even
• 12:43higher percentage present with ketoacidosis.
• 12:46See peptide frequently Becomes undetectable.
• 12:49The median time is about 11 weeks and
• 12:52only about 40% of individuals are
• 12:54positive for auto antibodies and this
• 12:56brings up a a classification issue.
• 12:58Some people call this type one diabetes.
• 13:01As I'm going to explain to you,
• 13:02I don't think this is type one diabetes,
• 13:04it's autoimmune diabetes induced
• 13:06by checkpoint inhibitors,
• 13:07but it's very different from classic
• 13:11spontaneous type one diabetes.
• 13:15Now there is a very large proportion
• 13:18of individuals who we don't talk
• 13:20about a lot who I think probably
• 13:23fall into this bucket,
• 13:24who are individuals who may have mild
• 13:27type 2 diabetes who then present
• 13:30with much worsening of their glucose
• 13:32control and may become may previously
• 13:35have been managed with oral anti
• 13:37diabetic agents and now all of a
• 13:40sudden may present ketoacidosis
• 13:42or may require insulin therapy.
• 13:44Now type 2 diabetes is a very common disease.
• 13:47So it may actually be that the
• 13:50frequency of this disease is much
• 13:53higher than is even represented by the
• 13:560.2 to 1.9% from the past reviews.
• 13:59Now I mentioned not everybody
• 14:01has autoantibodies.
• 14:03Here's some examples of that.
• 14:06Some patients, if you take a look
• 14:08at three patients on the bottom,
• 14:09some start out negative.
• 14:11Each of those antibodies are one of
• 14:13the auto antibodies that we measure
• 14:15in classic type one diabetes.
• 14:17You can see some patients start out negative,
• 14:19become positive,
• 14:20some patients start out positive,
• 14:22stay positive.
• 14:23So it varies about 40% overall are positive.
• 14:27But the frequency of those who are positive,
• 14:30sorry let me go back for two or
• 14:32more which is what we,
• 14:33which is kind of the hallmark of spontaneous
• 14:36type one diabetes is relatively low.
• 14:40Now curiously the the alpha
• 14:42producing cells in the islet,
• 14:45remember the islet is a collection of cells,
• 14:47alpha cells,
• 14:48beta cells,
• 14:48delta cells and so on that make
• 14:50a variety of hormones.
• 14:52The loss of of of endocrine cells,
• 14:55this seems to be limited to the beta cells.
• 14:58The alpha cells sitting right next
• 15:00to the beta cells are unaffected and
• 15:02the reason for that is not clear.
• 15:04But as you can see from this data
• 15:06from patients that we we where we
• 15:09measure Glucagon here didn't seem
• 15:11to make a difference in terms of
• 15:15their Glucagon levels.
• 15:17Now one of the early striking
• 15:19findings from our series
• 15:20of patients was that a high proportion
• 15:23of individuals were HLAD, R4. Now Dr.
• 15:26three and four are associated with with
• 15:29classic spontaneous type one diabetes.
• 15:31But this proportion of of DR4 is
• 15:34strikingly high and it's higher
• 15:36than the background population.
• 15:38And DR3, the other allele associated
• 15:41with spontaneous diabetes was
• 15:42not increased in frequency.
• 15:44So DR4 somehow or another seems
• 15:46to be important in predisposing
• 15:48to the development of type of
• 15:52of checkpoint induced diabetes.
• 15:54And I want to point out this recent
• 15:59observation that was originally made
• 16:01by Jasmine Caulfield and and Lilac
• 16:05Eisenbud from our patients here.
• 16:09And what was done is we were doing
• 16:12a a genome sequencing of tumors and
• 16:16identified a number of mutations in
• 16:18a variety of genes that seem to be
• 16:21associated what seemed what seemed to
• 16:22be at a higher frequency in people
• 16:25with checkpoint induced diabetes.
• 16:27And then we ended up going back and
• 16:30doing sequencing of of peripheral
• 16:32blood cells and finding that indeed
• 16:35there were germline mutations that
• 16:36seem to be associated with development
• 16:39of checkpoint induced diabetes.
• 16:41And interestingly the one of the the,
• 16:44the highest frequency was in this
• 16:46molecule called NLRC 5 and you
• 16:49can take a look on the right,
• 16:51the frequency of individuals with
• 16:54NLRC 5 variants was in our series 65%.
• 16:59Now it's not a huge series because
• 17:01we don't we don't have tons of
• 17:03patients we had we had 13 here.
• 17:06But you can see that at least
• 17:08the statistically it it,
• 17:09it turns out to be in a much higher
• 17:13frequency compared to those individuals
• 17:15without checkpoint induced diabetes
• 17:17who get the same checkpoint inhibitors.
• 17:20Now what's the importance of NLRC 5?
• 17:23So NLRC 5 actually tends to is is is
• 17:28evolved in a class one MHC antigen
• 17:31presentation.
• 17:32I'll tell you about that in just a moment.
• 17:34But you can see that it seems to be
• 17:37an important molecule involved in
• 17:42responses in in cancer patients that
• 17:46that methylation of NLRC 5 reduced
• 17:49NLRC 5 seems to be associated with
• 17:54impaired CTL activity and clearing
• 17:57of of tumors.
• 17:59The its expression seems to be
• 18:01correlated with survival and in
• 18:03diabetes it's also been a associated
• 18:05with beta cell antigen presentation
• 18:10and and the interferon response.
• 18:12So for example,
• 18:14the NLRC knocked down beta cells
• 18:17seem to have a decreased interferon
• 18:20induced class one MHC expression
• 18:22and seems to be associated with
• 18:26protection from autoimmune diabetes.
• 18:29So NLRC 5 is is a regulator of Class
• 18:331 dependent antigen presentation,
• 18:35much the same as the classic Class 2
• 18:43transactivator. It's responsible for
• 18:46bringing peptides into the endosome
• 18:50for processing and placing them on
• 18:55developing class one MHC molecules.
• 18:58It's expression seems to
• 18:59be induced by interferons,
• 19:01particularly interferon gamma
• 19:04through Stat 1 signalling.
• 19:07So this review actually
• 19:09describes the mechanism.
• 19:11I'm not going to go into detail about it,
• 19:13but what we ended up doing and this
• 19:15is work that Anna Pertigato did,
• 19:17we ended up looking at expression of TAP ONE,
• 19:20which is an important transactivator
• 19:25that's associated with class one MHC
• 19:28expression as well as HLAA on peripheral
• 19:31blood cells in patients with the mutation
• 19:35or with wild type type of the NLRC 5.
• 19:40And as you can see and in patients with
• 19:43the mutant there seems to be higher
• 19:47expression of TAP one and actually of HLAA
• 19:50although we haven't reached statistical
• 19:52significance for the HLA molecule.
• 19:55So it it suggests at least that there is
• 19:58some change in expression of MHC molecules
• 20:02or potentially presentation of peptides
• 20:05by individuals who have this mutant.
• 20:09So to summarize these two points,
• 20:10the there seems to be evidence
• 20:14for mutations or differences.
• 20:16In class one and Class 2 MHC molecules
• 20:19that that are associated with development
• 20:22of checkpoint induced diabetes.
• 20:24First of all HLAD R4 is common and
• 20:27perhaps that leads to the development
• 20:29of an auto autoreactive repertoire.
• 20:32This NLRC 5 mutation also seems
• 20:35to have some role in potentially
• 20:39in expression of molecules.
• 20:41A presentation of molecules by beta
• 20:45cells or even potentially in affecting
• 20:48a subgroup of CDA positive T cells have
• 20:52been associated with immune regulation.
• 20:56Now the let me just raise some
• 20:59questions about these these two points
• 21:01by make by by pointing this out.
• 21:04When we've looked at auto antigen
• 21:07reactive T cells in patients
• 21:10with checkpoint induced diabetes,
• 21:12we've looked for auto antigen
• 21:15reactive T cells that are reactive
• 21:17to conventional type one diabetes.
• 21:19Auto antigens,
• 21:20we don't really find an increase.
• 21:23So if you take a look at that,
• 21:25we've looked at T cells that are
• 21:27identified by binding to class one MHC
• 21:30tetramers that are loaded with the
• 21:32peptides that are shown on the left side.
• 21:35If you look at the frequency of
• 21:38these cells on the right side
• 21:40and the individuals treated
• 21:42with checkpoint inhibitors,
• 21:43those who don't have diabetes or do,
• 21:45there's really no difference.
• 21:46So it at least would suggest that the,
• 21:49the known auto antigens or recognition
• 21:53of the known auto antigens is not
• 21:56really increased or at least the
• 21:58frequency of cells is not increased
• 22:01in those individuals who are
• 22:03developing checkpoint inhibitors.
• 22:04Let me just you know sort of say
• 22:06as a preface to this data the the,
• 22:08the low hanging fruit on this was well,
• 22:11these individuals had an autoreactive
• 22:13repertoire. They had Dr.
• 22:15Four, we removed the checkpoint blockade.
• 22:17These cells just did their thing,
• 22:19don't think so.
• 22:20It could be that there are cells that
• 22:22are reactive to unknown auto antigens
• 22:24and as I'll show you in just a moment,
• 22:27there is some evidence that that
• 22:29might be true, but that's not all.
• 22:33There are also there's also evidence
• 22:35of inflammatory lesions that or
• 22:37inflammation that's occurring
• 22:38in the pancreas that may be very
• 22:41important for development of
• 22:43checkpoint induced diabetes.
• 22:44And this actually came from from
• 22:48actually a clinical observation from
• 22:50patients here in which we found that
• 22:53there was an increase in amylase and
• 22:56lipase in individuals who ultimately
• 22:58went on to develop diabetes.
• 22:59They don't develop clinical pancreatitis.
• 23:02But here we're looking at the amylase
• 23:05and lipase level on one individual
• 23:08who is who develops checkpoint
• 23:10induced diabetes and you can see the
• 23:12lipase on the left bumps and then
• 23:14red is when they developed diabetes
• 23:16and the amylase bumps and then red
• 23:19is when they developed diabetes.
• 23:21If you look at our entire series
• 23:26and look at the relative levels
• 23:28of lipacer amylase on the bottom,
• 23:31you can see that the that that
• 23:33both are elevated prior to the
• 23:35development of of of diabetes.
• 23:37Now interestingly it prompted us
• 23:39to look at what well like what's
• 23:42actually happening in the pancreas.
• 23:44They were not symptomatic and so we
• 23:48ended up looking at CT scans that
• 23:51fortunately we had from before and
• 23:55after individuals presented with diabetes.
• 23:57And what we found if you take a look
• 24:00at the CTS and on the on the top here
• 24:03is the the red arrow identifies the pancreas.
• 24:06The there actually seem to be shrinkage
• 24:09of the pancreas in individuals who went
• 24:12on to develop checkpoint induced diabetes.
• 24:15So it's suggested that there is more
• 24:18than just a direct attack on beta
• 24:20cells that there may actually be
• 24:23a broader attack in a a broader
• 24:25inflammatory response in the pancreas.
• 24:28And unfortunately one of our patients
• 24:31died as soon after they had developed
• 24:34checkpoint induced diabetes.
• 24:35But we had the opportunity to take a
• 24:37look at their pancreas by immunohistic
• 24:39chemistry and this is what we found.
• 24:41You can see that there are plenty
• 24:43of CD 45 positive immune cells that
• 24:46are infiltrating the islets and
• 24:48that are infiltrating the pancreas.
• 24:50They are not just in the islets and in
• 24:52fact many of them are outside of the islets,
• 24:54as you can see by standing
• 24:56for insulin on the right.
• 24:58And there are both CD4 and
• 25:00CD8 positive cells.
• 25:01Chromagranin identifies the endocrine cells.
• 25:05They're infiltrating the islets
• 25:07and they're outside of the islets.
• 25:09And if you look at cytokines that
• 25:12are present in the pancreas,
• 25:15we find both interferon gamma and TNF.
• 25:19And interestingly,
• 25:20one of the other findings from this
• 25:24immunohistochemical analysis is PDL
• 25:27one was actually induced on beta
• 25:31cells in and on the other endocrine
• 25:34cells in this patient who died
• 25:37with a checkpoint induced diabetes.
• 25:39Now that's a little weird.
• 25:41We thought that PDL one was actually
• 25:44protective against diabetes.
• 25:46So what what's going on here?
• 25:49So let me just make the point and again
• 25:52this is work that Anna Pertigato has
• 25:55done that indeed inflammatory mediators,
• 25:57particularly gamma interferon
• 25:59will induce PDL One on beta cells.
• 26:02There is a interferon response
• 26:04element in the promoter of PDL
• 26:07one and as you can see by looking
• 26:09but by flow interferon gamma.
• 26:11This is human beta cells.
• 26:13Interferon gamma and interferon
• 26:17gamma with TNF induce expression of
• 26:19PDL one on beta cells and it seems
• 26:23to be dependent through signaling
• 26:25by gamma interferon because if you
• 26:27give rexolitinib to block Jack
• 26:32signaling through Stat One,
• 26:34you can inhibit the expression of PDL one.
• 26:39Now there was good evidence for the
• 26:42importance of PDL 1 in development of
• 26:45autoimmune diabetes and most of this
• 26:48work came originally from Arlene Sharp.
• 26:51And the the work that I'm I'm showing
• 26:55on the left is from a paper of hers
• 26:59a number of actually 20 years ago
• 27:01now that showed if you knock PDL one
• 27:03out of this susceptible mouse strain
• 27:06NOD that the mice spontaneously
• 27:09developed diabetes at a very young age.
• 27:12And the the Histology is shown in
• 27:15the middle here.
• 27:16Furthermore,
• 27:16if you gave anti CD3 antibody to
• 27:19mice that spontaneously developed
• 27:22diabetes and induced remission
• 27:24with the anti CD3 antibody,
• 27:27if you gave anti PD one or anti PDL one,
• 27:30this is work by Jeff Bluestone
• 27:32and Brian Fife On the right side,
• 27:34the mice immediately redeveloped diabetes.
• 27:37So this work suggested that PDL one
• 27:41had a critical role in maintaining
• 27:44non development of diabetes in
• 27:46a susceptible host.
• 27:47And here are some additional studies
• 27:50from Arlene's lab that showed
• 27:53if you took wild type cells,
• 27:55transferred them into APDL 1
• 27:57knockout or a wild type
• 27:59host if you put them into the
• 28:02knockout recipient, which is on
• 28:04the left side in the open circles,
• 28:05mice rapidly developed diabetes whereas
• 28:07they didn't at the same rate if you
• 28:10put them into the wild type recipient.
• 28:12And it also was shown in her
• 28:14work that the importance of PDL
• 28:16One was indeed on the islets.
• 28:19Because if she transplanted PDL 1
• 28:22deficient beta cells into either
• 28:24wild type or knockout mice,
• 28:27which is shown on the on the right,
• 28:29the PDL 1 knockout islets were more
• 28:33rapidly killed compared to wild type eyelids.
• 28:38So PDL one seems to have some unique
• 28:43features that's important in in
• 28:46protecting against autoimmune diabetes.
• 28:48Now we did some additional studies
• 28:51look comparing anti PDL one and
• 28:54anti CTE 4 because let me go back
• 28:57to that paper in that that letter
• 29:00in 2015 and and the comments from
• 29:03the reviewer that pointed out,
• 29:05well if this was really important
• 29:07we would have known about it.
• 29:08Well that reviewer was completely wrong
• 29:11because indeed the only checkpoint
• 29:13inhibitor that was available prior
• 29:15to that time was anti CTLA 4.
• 29:18And if you take a look at the
• 29:20mouse data here and this has been
• 29:22reproduced in other labs,
• 29:24anti CTLA 4 doesn't do this seems
• 29:27to be unique for anti PDL 1.
• 29:30And so we did some studies to to
• 29:33try to identify what's different
• 29:36about anti PDL one and anti CTLA 4IN
• 29:41induction of diabetes and I'm going to
• 29:44go through the the data fairly quickly.
• 29:46We did this by performing single cell
• 29:50RNA seq on infiltrating cells and
• 29:56islet cells from mice that had received
• 30:00either of these checkpoint inhibitors.
• 30:02And let me first point out that in the
• 30:05presence that when when these susceptible
• 30:08mice and OD mice are given anti C24,
• 30:11there are cells that infiltrate the islets.
• 30:13It's not that they don't develop insulitis,
• 30:16it's just that they don't develop diabetes.
• 30:18They don't go on and kill,
• 30:20kill the beta cells.
• 30:21So first of all,
• 30:23when we look at and when we look at immune
• 30:26cells that are infiltrating the islets,
• 30:28you can see there is a difference.
• 30:30If you take a look at panel
• 30:32D in the MELD analysis,
• 30:34there's a difference in CDAT cells
• 30:36that are infiltrating the islets when
• 30:38the when the mice are treated with
• 30:41anti PDL 1 compared to anti cetal A4.
• 30:43And there are a number of genes that
• 30:46are differentially expressed including
• 30:47some of the the ones that you might
• 30:50expect such as as Tea Bed Interferon,
• 30:54Gamma Granzyme B and even PDL one
• 30:57as as we would have predicted,
• 31:00as well as Perfran and the volcano
• 31:03plot showing you the differences
• 31:05in expression in the CDA T cells
• 31:08as shown in the bottom.
• 31:10Now what about the cells that
• 31:11are infiltrating the eyelids?
• 31:13Are they the same? Maybe they're different.
• 31:16And this is the data that we have to date.
• 31:18And fortunately I can't go into
• 31:21this and more with more granularity
• 31:23except to point out that yes,
• 31:26they are different.
• 31:26They are not the same cells that are
• 31:29being driven to the eyelids in when
• 31:31with the two different checkpoint inhibitors.
• 31:34If you just take a look at the
• 31:36frequency of various clonotypes
• 31:37you can see with anti PDL one in
• 31:40mice that that do develop diabetes,
• 31:43there seems to be a relative selection
• 31:47of particular clonotypes compared
• 31:49to the anti C2E4 treated mice.
• 31:53Now macrophages also seem to be
• 31:56different for reasons that we
• 31:59we don't completely understand.
• 32:00But you can see that they they express
• 32:06PDL one, they themselves express PDL one.
• 32:09They produce CXCL 10, which is important
• 32:12in recruiting cells to the islets,
• 32:15as well as Stat 1 indicating they've
• 32:18they've been looking at interferon gamma.
• 32:21And this is interesting because
• 32:23work from Emil Yunanoway's lab had
• 32:26actually pointed out that these cells
• 32:28seem to be the critically important
• 32:31cells for initiating checkpoint
• 32:33induced diabetes in in this model.
• 32:38Now in addition there are there
• 32:42there are changes in beta cells.
• 32:44I showed you already in humans that
• 32:46that we found that there was induction
• 32:49of PDL one in human beta cells that
• 32:52were treated with interferon gamma.
• 32:54And indeed if we looked at
• 32:57genes that are differentially
• 32:58expressed with interferon gamma,
• 33:00you can see that there are a
• 33:02whole lot of genes that have
• 33:05some immune response properties.
• 33:07Now the reason that we think
• 33:09this is important is because
• 33:11seeing inflammatory when beta
• 33:13cells see inflammatory cytokines,
• 33:15they make a number of important
• 33:18immune ligands such as CXCL 9,
• 33:21CXCL 10 important for recruiting
• 33:24cells to the islets and as well as
• 33:29increase expression of of of class one.
• 33:31MHC when we looked at this again
• 33:35is with human cells.
• 33:36When we looked at other features of
• 33:39human islets exposed to gamma interferon,
• 33:42we found that actually there
• 33:43was induction of FAS suggesting
• 33:45that indeed that cytokine might
• 33:48induce a killing of beta cells.
• 33:51And if you take a look at impanel
• 33:55E you you can see that in the PDL
• 33:581 expressing cells we we we find
• 34:01this morphology suggesting the cells
• 34:03are are are are actually dying.
• 34:05And indeed if if we look at at
• 34:08the percentage of dead beta cells
• 34:11in panel D it is much higher with
• 34:14cells that are cultured with
• 34:16interferon gamma back to the mice.
• 34:19Now when we look at beta cells
• 34:21in the mice in site two,
• 34:23there are a number of differences in
• 34:26in in them including the development
• 34:29of a unique subgroup of of beta cells.
• 34:33If you take a look at panel C,
• 34:36the fate,
• 34:37the fate diagram here shows you
• 34:402 populations of beta cells.
• 34:43The the the standard beta cells
• 34:44that you can see in mice treated
• 34:47with anti cetal E4 or anti PDL one
• 34:49and then this unique a cluster
• 34:51of beta cells that seems to be
• 34:54uniquely found in anti PDL one.
• 34:56The main beta cells express the same
• 34:58log in so they just showed you with
• 35:02human beta cells CXCL 10 PDL one.
• 35:04Class one MHC goes up stat
• 35:06one is signaling and trail is
• 35:08actually increased as well.
• 35:10But in the unique beta
• 35:12cells there's also changes,
• 35:14including reduced expression
• 35:16of a number of the beta cell
• 35:20identity genes such as NTX 6.1,
• 35:23Maffe of course,
• 35:25insulin and and and chromogram.
• 35:28So it's what this,
• 35:29what this finding suggests is work
• 35:32that we've done in other models
• 35:34of diabetes that there is some
• 35:39pathway leading to beta cell survival in
• 35:41the presence of checkpoint inhibitors
• 35:44that that seems to be turned on when
• 35:48these drugs are given. All right.
• 35:51So that's that's kind of where things are
• 35:54in terms of what's going on in the islet,
• 35:57what how human beta cells respond
• 36:00similarly to inflammatory mediators.
• 36:02So what what is, what is,
• 36:04what's the point of that and
• 36:07what can we do about it.
• 36:08So let me point out that in follow
• 36:14up work that that we did to try to
• 36:17figure out could we based on this
• 36:19knowledge stop the development
• 36:21of checkpoint induced diabetes.
• 36:23We first tested whether anti cytokine
• 36:26antibodies might be able to do that.
• 36:28And I've shown you already the
• 36:31critical role of interferon gamma and
• 36:33potentially TNF in development of
• 36:36checkpoint induced diabetes at least
• 36:38in mice and evidence in humans that
• 36:40both of these cytokines were present
• 36:43in the pancreas of an individual who
• 36:45died with checkpoint induced diabetes.
• 36:47What happens if you neutralize
• 36:50those cytokines?
• 36:51And you can see in the on the top here
• 36:54that if you gave the combination of
• 36:57anti PDL interferon gamma and anti TNF
• 37:00to mice treated with anti PDL one,
• 37:03you could indeed prevent the development
• 37:06of checkpoint induced diabetes in the mice.
• 37:09Furthermore,
• 37:09if you blocked a little further downstream
• 37:12with a Jack inhibitor and this is,
• 37:15I'm sorry, this says Jack inhibitor 1,
• 37:17Jack inhibitor 2 and I should
• 37:19just mention this is an ongoing
• 37:21collaboration with folks at Pfizer
• 37:24and with two new Jack inhibitors,
• 37:26The identities of which we don't know
• 37:28except we know they're different.
• 37:29But as you can see Jack inhibitor 1
• 37:32looks pretty good in terms of developing,
• 37:35preventing the development of
• 37:38checkpoint induced diabetes.
• 37:40So to summarize what I've just told
• 37:42you then what we think is there's
• 37:45actually an inflammatory cycle that's
• 37:47going on between immune cells and beta
• 37:49cells that leads to the development
• 37:52of of a checkpoint induced diabetes
• 37:54in response to interferon gamma.
• 37:57Beta cells in turn make a number
• 38:00of immune regulatory molecules
• 38:02that recruit other immune cells,
• 38:05activate immune cells leads to increased
• 38:09production of inflammatory cytokines
• 38:12particularly interferon gamma.
• 38:14It leads to expression of PDL one.
• 38:16When you block PDL 1 you seem to
• 38:20block the stop signal in immune cells
• 38:24that otherwise would would cause
• 38:27them to leave the eyelid and and
• 38:29the immune cells then are there in
• 38:31the eyelid and capable of going on
• 38:34and killing the insulin producing
• 38:36cells so and and killing beta cells.
• 38:40So what is,
• 38:41is there anything we can take home from
• 38:43this in terms of treating patients?
• 38:45And let me just start by mentioning
• 38:49this patient that was again another
• 38:53another letter in diabetes care
• 38:57that was treated in Switzerland.
• 39:00This is a patient who had presented
• 39:02with type 2 diabetes and let me go
• 39:05back to a point I made earlier.
• 39:07Type 2 diabetes is a common disease
• 39:09and so it follows that there are
• 39:11patients who are going to develop
• 39:14checkpoint induced diabetes who already
• 39:16may have pre-existing type 2 diabetes.
• 39:19And that's the explanation I'm going
• 39:21to give you for for this this case
• 39:24report that appeared in the literature.
• 39:26So this is an individual with pre
• 39:29pre-existing type 2 diabetes had
• 39:32much worsening glucose control.
• 39:35You can see with a hemoglobin A1C of 11.6%
• 39:39but did have detectable beta cell function.
• 39:42The C peptide was 993 which is
• 39:45you know plenty respectable and
• 39:48was also auto anybody positive.
• 39:50So they believe that this patient
• 39:52had immune mediated diabetes.
• 39:54They gave the patient infliximab,
• 39:56the anti TNF antibody and as you can
• 40:00see the the glucose is improved.
• 40:03The hemoglobin A1C came down
• 40:05and so that was
• 40:09that seemed to be very impressive
• 40:11to those investigators.
• 40:12The patient had been treated with insulin.
• 40:13They stopped the insulin.
• 40:15Now since we saw that,
• 40:17we've also treated a few patients
• 40:19here and I want to mention this
• 40:23work that's been ongoing by Noam
• 40:25and Anna for treating patients
• 40:28here who've developed checkpoint
• 40:30induced diabetes with infliximab.
• 40:33Let me show you 2 cases.
• 40:36This patient had a history of type
• 40:392 diabetes like the previous one
• 40:42that I showed you and presented with
• 40:45very very high glucoses and the the
• 40:49hemoglobin A1C in the past had been
• 40:52a fairly reasonable and the patient
• 40:54had not been treated with insulin.
• 40:57There was a bump in the amylase
• 40:59and light paves just as I showed
• 41:01you in in one of the first slides.
• 41:04And then the glucose became markedly
• 41:06elevated and as you can see the
• 41:08patient received 3 doses of infliximab.
• 41:11And if you take a look at the response
• 41:14curves and in terms of the C peptide,
• 41:16it actually did seem to these
• 41:18are random C peptides.
• 41:19I should point out the C peptide
• 41:21did seem to improve after the
• 41:23patient was treated with infliximab
• 41:25and the glucose was also better.
• 41:27Now these are, these are anecdotal,
• 41:31these are not performed in a rigorous
• 41:34endocrine setting where we're actually
• 41:36stimulating beta cell function.
• 41:38But nonetheless and I think from
• 41:39the patient's point of view,
• 41:41the fact that he was able to get
• 41:43off of insulin and his hemoglobin
• 41:45A1 CS were subsequently improved
• 41:47is is clinically meaningful.
• 41:50Here's another case.
• 41:52This individual with metastatic
• 41:55Melanoma was treated with EPI
• 41:57and Nevo and had adverse events
• 42:00including uveitis and diarrhea that
• 42:03have been treated with steroids and
• 42:06hyperglycemia was noted at cycle 21.
• 42:10There was no prior history of
• 42:12diabetes in this patient and previous
• 42:14hemoglobin A1 CS have been normal.
• 42:17This patient again presented with
• 42:19a very elevated hemoglobin A1C and
• 42:22the glucose was also quite elevated.
• 42:25This patient did not have evidence
• 42:27of ketoacidosis whereas the previous
• 42:29patient that I showed you did.
• 42:31And remember that ketoacidosis is a
• 42:34sign of of substantial insulin deficiency.
• 42:37This patient was auto antibody negative.
• 42:41So here we're looking at the
• 42:43random C peptide levels,
• 42:45one of them is stimulated,
• 42:47the last one that was just done a
• 42:49few days ago and the glucose levels
• 42:51and you can see that the glucose did
• 42:54improve probably with the medical care
• 42:56of the patient received but the C
• 42:59peptide also seemed to be pretty substantial.
• 43:01This is markedly different than what
• 43:03I showed you in in in one of the
• 43:06first slides where the C peptides
• 43:08pretty much go to undetectable in
• 43:10in the majority of patients who
• 43:14present with checkpoint induced
• 43:16diabetes and do so fairly rapidly.
• 43:19So to conclude adverse events are not
• 43:21infrequent with checkpoint inhibitors.
• 43:23In fact I would change that to
• 43:25say adverse events are common
• 43:27with checkpoint inhibitors.
• 43:28Most common is thyroid disease
• 43:31and hypothesitis but diabetes
• 43:32also occurs in about 1%
• 43:34of checkpoint induce a checkpoint
• 43:37inhibitor treated patients.
• 43:39Now one thing I should mention is for
• 43:42patients and you know we see them.
• 43:45Thanks to all of you in our clinic.
• 43:47But for the patients this
• 43:48is a difficult disease.
• 43:50I mean you know it's it, it,
• 43:52it's a lot different when a
• 43:5512 year old presents with.
• 43:56It's not that the disease
• 43:58is easy for a 12 year old,
• 43:59but it's even more cumbersome
• 44:01for a 65 or 75 year old who now
• 44:04has become insulin deficient,
• 44:06completely dependent on exogenous insulin
• 44:09for maintaining metabolic control.
• 44:12So it is quite a burden for patients.
• 44:15So preventing the disease would obviously
• 44:18be would result in very significant
• 44:21improvements in quality of life.
• 44:23It's most common in patients treated
• 44:25with anti PD one or anti PDL 1
• 44:28antibodies and in patients or HLAD R4.
• 44:30Still a lot of work needs to go on to
• 44:33understand what is the significance of
• 44:36DL DDR4 or the significance of NLRC 5.
• 44:40But it nonetheless suggests that there
• 44:43is some some change or some difference
• 44:46in these patients in presentation of
• 44:48either class one or Class 2 or both.
• 44:51MHC presented antigens,
• 44:53pancreatic inflammation is is
• 44:57frequent prior to the development
• 44:59of checkpoint induced diabetes.
• 45:01Curiously, PDL one's expressed on beta cells.
• 45:04And I think we have to conclude
• 45:06that in spite of expressing PDL
• 45:08One on beta cells and in spite of
• 45:11showing its extraordinary protective
• 45:13effect in animal models of disease
• 45:16that when you give a checkpoint,
• 45:19when the checkpoint inhibitor
• 45:21is given that protective,
• 45:23that protective blockade is gone.
• 45:26And even afterwards PDL one
• 45:30expression is no longer able to
• 45:33stop the development of diabetes.
• 45:35And I think the identification of
• 45:38mechanism suggest have suggested a
• 45:40therapeutic strategy inhibition of
• 45:42inflammatory mediators may potentially
• 45:44halt progression of diabetes and
• 45:46beta cell loss with checkpoint
• 45:48induced diabetes and a short acting
• 45:51inhibitor potentially Jack inhibitors
• 45:53would warrant some further testing.
• 45:56One last one last comment,
• 45:58let me mention that you know I
• 46:00think one of the interesting things
• 46:02about all of the adverse checkpoint
• 46:04induced adverse events is,
• 46:06is it a feature of the checkpoint inhibitor,
• 46:08a feature of the tissue or a feature
• 46:11of the patients or all three of these.
• 46:13And let me just point out this work
• 46:16from Jackie Mann in our group who
• 46:19looked at checkpoint inhibitor induced
• 46:21colitis and she did this by single cell RNAC.
• 46:25This work was published fairly recently,
• 46:28but let me point out that a number of
• 46:30the molecules that I just told you
• 46:33about being found in the pancreas of
• 46:36checkpoint induced diabetes can also
• 46:38be found in patients who develop colitis,
• 46:41suggesting that we might even think about
• 46:44a broader use of of various inhibitors,
• 46:48not inhibitors.
• 46:49Obviously that would prevent the anti
• 46:51tumor effect of the checkpoint inhibitors,
• 46:54but might be given sequentially
• 46:56after the anti tumor effects of the
• 47:00checkpoint inhibitors and that might
• 47:03be rapidly tapered in the event that
• 47:07further cancer therapy is needed.
• 47:09So I'm going to close with that.
• 47:10I want to thank a number of individuals,
• 47:13particularly Harriet, who's been,
• 47:16you know, a colleague for a decade now,
• 47:19and a number of individuals in
• 47:22her group who've I've had the
• 47:24good fortune of working with.
• 47:25As well, I want to mention
• 47:30Lalak's work on identifying
• 47:33the LLRC 5 mutations.
• 47:35I showed you some of Jackie's work.
• 47:37Nolan is continuing this work with
• 47:41particularly with giving with the
• 47:44NLRC 5 mutations and therapies
• 47:46of checkpoint induced diabetes.
• 47:48Anna Perdigata did a lot of,
• 47:50did actually all of the work,
• 47:52the single cell work with the mouse models
• 47:54and it's continuing to go on to do that.
• 47:56And we have colleagues at UCSF and funding
• 48:00you can see on the right side here.
• 48:03So I'll stop there and I'm
• 48:05happy to answer any questions.
• 48:14Thank you, Kevin for a
• 48:16wonderful presentation. Kurt,
• 48:23thank you for an excellent
• 48:23talk. I wanted to ask,
• 48:25so LRC 5 is a little bit
• 48:27kind of superficially counter intuitive
• 48:29in terms of germline mutation.
• 48:30I was wondering if there was a
• 48:31role in central tolerance and
• 48:32if you saw increased checkpoint
• 48:34inhibitor autoimmunity in
• 48:36hypothesitis or hypothyroidism.
• 48:41I'm sorry I I missed the second part.
• 48:43I, I, I, I, I understood your
• 48:45question about central tolerance
• 48:47but so and so whether you saw
• 48:49rather than an LRC 5 mutations,
• 48:52germline ingest type one diabetes
• 48:55or well check one inhibitor diabetes
• 48:57or whether also intra,
• 49:00I think that's still somewhat
• 49:03of a ongoing question.
• 49:07I think it's unlikely Harriet may have a
• 49:11thought as to whether it's more likely.
• 49:13Yeah, I can Norm can answer it as well.
• 49:15So we have looked in at NLRC 5
• 49:18SNPs in other other toxicity,
• 49:21it seems to be higher as well in
• 49:24hypothesitis but not colitis.
• 49:25That's as far as we know so far.
• 49:28But the statistics are they're
• 49:30not this numbers are small.
• 49:31Still, that's exactly what
• 49:32Norm is working on right now.
• 49:46Yeah. I mean it could be
• 49:48the only. So I I I think
• 49:51that's an interesting question.
• 49:54But you're taking us back
• 49:55to the original model.
• 49:56These patients had a repertoire ready to go.
• 50:00And look, it could be right.
• 50:02I mean just because we don't
• 50:04see the usual suspects doesn't
• 50:05mean that there aren't suspects.
• 50:07Kevin, that was an amazing lecture.
• 50:09It it reminds me of 2015 or earlier when
• 50:11we first started using these agents
• 50:13and we're seeing wonderful responses.
• 50:15And you know patients with lung
• 50:16cancers and others would have these
• 50:17problems and you know they'd be
• 50:19on the throughout the hospital and
• 50:20they wouldn't get the care they
• 50:22needed because no one recognized
• 50:23that these toxicities were were
• 50:24part of this even though they were
• 50:26benefiting from the the therapy.
• 50:27I have a two-part question for you and
• 50:29you you now know who's at most risk,
• 50:31you have the NLR, other other risk factors.
• 50:33So my first question would be 1,
• 50:36would you treat prophylactically or
• 50:38or would you wait until they develop
• 50:40the toxicity to to start treating
• 50:43And then the second would be you see
• 50:44that the activity against the cancer
• 50:46is is increased in the patients
• 50:48that have these abnormalities.
• 50:50Yeah that's a let.
• 50:51Let me address the second question
• 50:53first because there is some literature
• 50:55suggesting that those who develop
• 50:57these adverse events do better in
• 50:59terms of their anti cancer activity and
• 51:02indeed our patients did well in general,
• 51:04but there is a publication for sure
• 51:07suggesting that those who develop
• 51:09hypothesitis had better outcomes
• 51:11in patients with Melanoma.
• 51:13So,
• 51:14so I'm not certain but I think it's
• 51:17certainly not a negative thing
• 51:20in terms of the cancer response
• 51:22and it may look it may,
• 51:23I mean just because you don't develop
• 51:26toxicities doesn't mean you can't
• 51:28do well with checkpoint inhibitors.
• 51:29So in terms of when I would treat
• 51:32if I if if if we knew how to
• 51:34treat type autoimmune diabetes,
• 51:37if we knew what the antigens
• 51:38were for example,
• 51:39we could we could dream about coming
• 51:42up with some sort of antigen specific
• 51:45prophylactic therapy and give that
• 51:47before we give the checkpoint inhibitor.
• 51:49At this point,
• 51:50I don't think we have that.
• 51:52And so my suggestion would be
• 51:54to carefully follow patients,
• 51:56look for the signs that identify
• 51:58those who are at risk of developing
• 52:00it and then when is appropriate in
• 52:03terms of the cancer therapy strategy,
• 52:05if if it's possible come in with
• 52:08some short term inhibitor. Thanks.
• 52:13Thanks, Kevin. Dr. Wagner.
• 52:15And then just just great talk,
• 52:19just a couple of simple questions.
• 52:21Are there gender differences in toxicity?
• 52:27We
• 52:30not that we had seen in diabetes.
• 52:32Not significantly different.
• 52:35Harry looks puzzled.
• 52:36Why I would ask that only because
• 52:38autoimmune disease is so much
• 52:39more common in is more common
• 52:41in women than men. Yeah. We
• 52:45didn't find that we we'd
• 52:47love. Yeah. The only the only
• 52:48thing I could say is type one
• 52:50diabetes is not really general.
• 52:51No, I I realized that, but
• 52:53this isn't type 1 to obvious and
• 52:57and this is for either of you.
• 53:00I mean do you think that that clinicians
• 53:02really have a sense of how abrupt the
• 53:07onset is of of of diabetes in this
• 53:10situation and are looking for it.
• 53:13I mean because you know it's
• 53:15happening not at week two,
• 53:16it's happening at week six or eight.
• 53:19The presentation is very acute.
• 53:22I mean you know there are some
• 53:24number of people out there as
• 53:25these therapies are used more and
• 53:27more we're going to die from this.
• 53:28So there have been deaths,
• 53:31there will be deaths where
• 53:33there isn't sufficient there,
• 53:36there isn't sufficient insight.
• 53:38The cup, the two patients that we
• 53:40haven't showed that we were able to
• 53:42give the TNF that was just chance.
• 53:43The first one was in hospital because
• 53:45of colitis or something else and that's
• 53:47when they noticed the ship going up.
• 53:49The second one is an EMT and
• 53:50he he noted his only party,
• 53:52that's it called a million
• 53:53started checking his glucose.
• 53:55But there there's not sufficient awareness.
• 53:57Yeah. The but the other sort
• 53:59of take home point from that is
• 54:01you need to be aware of this
• 54:02acutely because I showed you the C
• 54:04peptide levels when it goes to 0,
• 54:06there's no turning back.
• 54:08So I think close surveillance was important.
• 54:13Yeah.
• 54:15Well, I can tell you that I don't
• 54:19educate patients, you know so, so look
• 54:23for these kinds of things.
• 54:24Can I just a quick, very,
• 54:25very interesting data,
• 54:26Two quick questions.
• 54:27One for the germline NLCR 5 mutations,
• 54:31you may have said this,
• 54:32but are those associated with
• 54:35standard classic autoimmune
• 54:37diet type one diabetes as well?
• 54:39Yeah, there there's,
• 54:40there's that one paper from Dejo Isrich
• 54:44suggesting that the answer is no not really,
• 54:48not one of the important players.
• 54:50None the less though seems to be
• 54:52important and it it can affect
• 54:55antigenicity and development of diabetes.
• 54:57And and did you go back so you made
• 54:58a comment early that you know the,
• 55:00the, the, the, the,
• 55:04the 40% of the patients who have autoimmune,
• 55:07who have auto antibodies to to,
• 55:09to, to the islet cells.
• 55:13There's only relatively it was
• 55:15only 40% as opposed to all of them.
• 55:17And that was one of the reasons
• 55:18why this looked like this,
• 55:19one of your conclusions why this was
• 55:22different than standard, you know,
• 55:23type one diabetes did when you went back
• 55:25and you started looking at all these
• 55:27mechanisms in your patient population,
• 55:29did you, did you look at the difference
• 55:31in those patients who had auto
• 55:33antibodies and those who did not,
• 55:35You know, yeah it's interesting point.
• 55:39No, to my knowledge,
• 55:42I don't think we've done that.
• 55:45That's an interesting point way to kind of.
• 55:47Yeah, yeah.
• 55:48Yeah, confirm this here,
• 55:49this hypothesis.
• 55:50Yeah.
• 55:50So we
• 55:52have a couple of online questions.
• 55:54Oh, oh comments that would be
• 55:55easy for you to look at it there.
• 55:57OK. Anna has a comment.
• 56:01It'd be helpful to monitor blood
• 56:03glucose more carefully in patients
• 56:05who have lipase elevation and in some
• 56:08patients there's mild elevation in
• 56:10glucose before severe presentation.
• 56:13So monitoring them more more
• 56:15carefully may be valuable that that's,
• 56:17yeah, a very good point.
• 56:19And then there's a question about
• 56:21racial differences in in toxicity,
• 56:23not that I know of
• 56:29most. Yeah, I think we, I think that's right.
• 56:31Most of our patients are Caucasian.
• 56:36Yeah. Time for two.
• 56:39Oh, what are we going to do here?
• 56:40You see you should have sent your
• 56:42paper to the New England Journal to the
• 56:45clinical oncology inside the diabetes.
• 56:47That's right. That's right.
• 56:53That was an amazing talk. Thank you.
• 56:55I had a question about the the,
• 56:57the lipase elevation occurring before
• 57:00the onset of diabetes as well.
• 57:03You showed that it's it's
• 57:04common that that occurs,
• 57:05but did you look at patients
• 57:07that have lipase elevations and
• 57:09how often they develop diabetes.
• 57:11We don't routinely follow
• 57:13amylocin lipase in patients but
• 57:14occasionally on clinical trials we
• 57:15do we are required to look at it.
• 57:17And so that may be it would be
• 57:19interesting to see is it common
• 57:21that it it it is pre occurring or or
• 57:24that's a very good point.
• 57:26I I don't believe we've done the
• 57:28analysis that way unless area
• 57:29to know them you or Anna you
• 57:31know of of doing it differently.
• 57:33It's an interesting approach
• 57:34because we use the a lipase
• 57:36elevated or not often but when we
• 57:38see elevated amylase or lipase and
• 57:39patients are asymptomatic we we just
• 57:41we don't really do anything about it.
• 57:43We just watch them.
• 57:43But if you knew that that had a higher
• 57:45incidence of going to diabetes,
• 57:47maybe that's a population you could treat.
• 57:52Yes.
• 57:59Hello, I'm relatively new to immunobiology,
• 58:03but I had a question about
• 58:05the slide where you showed the
• 58:08immunohistochemistry results and
• 58:09you said that you saw signal or you
• 58:12saw standing outside of the eyelids.
• 58:14And I was wondering if you could
• 58:16further explain the significance
• 58:18on why you were excited about
• 58:20them being outside of the islets.
• 58:22Oh yeah, look, I would have been more
• 58:24excited if they were inside the islets.
• 58:27But the I think I think the point from
• 58:32that is that this is not just there's
• 58:36a broader inflammatory response and
• 58:40our assumption is that the islets
• 58:43cells can see the soluble mediators.
• 58:47So I I think you know we at least in
• 58:50the type one diabetes field we tend
• 58:53to think of you know single T cell
• 58:56clone going into the islet hitting a
• 58:58single target and I think this is this
• 59:01is a bigger inflammatory response.
• 59:04Thank you.
• 59:04And I think that's why the lipase
• 59:06and amylase are elevated.
• 59:10I have, I have many questions
• 59:12but I'll I'll just ask you.
• 59:13Had you mentioned or you
• 59:16had referred to the potential
• 59:18implication of regulatory
• 59:20CDAT cells and was wondering in
• 59:22your comparison between anti PD1,
• 59:25anti CTLA 4 differences, did you see any,
• 59:28no differences, haven't seen it.
• 59:30And then have you also,
• 59:31but we're going to look
• 59:32for it, you know if there
• 59:33are any differences in HLAC allotypes
• 59:36or HLAU or non canonical MHC.
• 59:42That's a good question
• 59:44and not that I know of,
• 59:47but that certainly is something
• 59:51worth doing EG and yeah,
• 59:55yeah, for the yeah,
• 59:57for the Kurds probably the
• 59:58C and EI think.
• 01:00:02Yeah. Kevin, thank you so
• 01:00:04much for a wonderful talk.