The PROMISE of Early Detection and Interception in Myeloma

February 28, 2023

Yale Cancer Center Grand Rounds | February 28, 2023

Presentation by: Dr. Irene Ghobrial

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00:00Is a special lecture in our Yale Cancer
00:04Center Grand Rounds series and it's
00:08the Blanche Tolman lecture series.
00:11So this lecture series was established
00:13in 2012 by Doctor Marvin Sears,
00:15who I believe will be
00:16attending today as well.
00:18Dr. Sears was a long time chair and
00:20founder of of of Thermology and Visual
00:22Sciences at Yale and the lecture was
00:25established in honor of his mother,
00:26Blanche Tallman, who passed away
00:29from acute myeloid leukemia.
00:31So to our delight,
00:32this was the first lecture series at
00:33year dedicated solely to hematologic
00:35malignancies and it continues to
00:37bring to Yale pioneers that have
00:39made major contributions to our
00:41understanding of the current trends
00:43and hematologic malignancies.
00:44So it is an absolute pleasure to
00:47introduce the actor Irene Gabriel
00:50today as our special lecturer.
00:52So Doctor Gabriel is professor of
00:54medicine at Harvard Medical School.
00:57She received her MD from Cairo
00:59University School of Medicine in Egypt.
01:01And she then completed her internal
01:03medicine training at Wayne State
01:05University and her hematology
01:06oncology subspecialty training at
01:08Mayo Clinic College of Medicine.
01:10In 2005,
01:11she joined in a Farber Cancer
01:13Institute in the field of Waldenstrom's
01:15Macroglobulinemia and a multiple myeloma.
01:17So doctor Gabrielle,
01:18as you will all see,
01:20has risen to become one of the world's
01:22leaders in the democratic field.
01:24Not only has she advanced major
01:25novel treatments to the clinic,
01:27but she now also focuses on early
01:30detection and interception to prevent.
01:31Regression to full blown multiple myeloma.
01:35Doctor Gabriel has a broad background
01:37in the biology of multiple myeloma
01:39and in the bone Marinette so
01:41important in the focus on M gas
01:44and smoldering myeloma and again
01:47preventing disease and her her
01:49research knowledge expertise allow
01:51us to define both cell autonomous
01:53and bone marrow age dependent
01:55and also genetic and epigenetic
01:57mechanisms of disease progression.
01:58And we couldn't be more excited
02:00to hear your talk today.
02:02So welcome we wish we were in person but.
02:05This is still wonderful.
02:06And at least we didn't have to cancel.
02:08Yes. Well, thank you so much,
02:09Stephanie. And as you said,
02:11it's really a pleasure and honor to be here.
02:13And I'm sorry that it's not in person,
02:15but it's New England and we all
02:16know how to deal with that, I guess.
02:18So I'll take you through a little bit
02:20of what we do in the lab and how we
02:22translated it into the clinic on the
02:25promise of early detection and interception.
02:27These are these are my conflicts of interest.
02:33So I'll just start with a simple
02:36question that many of us ask ourselves.
02:38In general, in every Cancer
02:40Center when you see patients,
02:41it's because they either had
02:43symptoms and they want to see their
02:44primary care doctor or by accident,
02:46something happened in their blood works.
02:48They had a little bit of anemia,
02:49a little bit of a higher white count
02:51and that led to further workup,
02:53which led to the diagnosis of cancer
02:56and then they get referred to you.
02:58But if you think about it,
02:59this means that we are waiting
03:01for things to happen and then.
03:02We react to cancer and by chance
03:05some of those made by good luck
03:07have an early cancer and we can
03:09diagnose it early and we can cure it.
03:11But many of them actually have stage three,
03:13stage four cancer.
03:14And we do sit down with them and
03:16say we may give you some treatment,
03:18but we may not cure the disease.
03:20And in fact if you think about it,
03:21pharmaceutical companies as well
03:23as cancer centers put millions and
03:25billions of dollars into developing
03:27therapies that can change to survival
03:29of metastatic cancer by three or four
03:32months and we consider that. Success.
03:34So what can we do to change that?
03:36How can we become less reactive to
03:39cancer and be more proactive to cancer,
03:42trying to find it early before
03:44it becomes symptomatic,
03:45trying to define it early.
03:46And then by doing that you can
03:48intervene early and make a difference
03:50in the survival of those patients?
03:52Now I would probably say that
03:54myeloma is a great example of that
03:56as a potential model system for
03:58early detection and interception.
04:00We know that myeloma has a well known
04:03clinically defined precursor condition,
04:05monoclonal gammopathy of undetermined
04:07significance and then yet another
04:09stage of the disease that progresses
04:12just before the active cancer,
04:14sort of a stage one,
04:15stage two breast cancer if you
04:17want to call it and that's the
04:19asymptomatic smoldering myeloma
04:20Now I was lucky enough to be.
04:23Trained by Bob Kyle at Mayo Clinic who
04:25actually coined both of those terms,
04:26monoclonal gammopathy of undetermined
04:28significance and smoldering myeloma.
04:30And he had this amazing vision because
04:32he thought that when he described
04:34those asymptomatic patients who
04:35are just walking around with a very
04:38small tiny monoclonal protein that
04:39they should actually be watched
04:41carefully and we they may actually
04:43progress to develop the disease.
04:44And in fact,
04:45him and Jan Waldenstrom had a big
04:47discussion where Jan Waldenstrom
04:49wanted to call it benign gammopathy
04:51because those patients.
04:53Are completely benign and why
04:55would we worry them?
04:56Yet Bob Kyle was so good in thinking
04:58ahead and thinking that there is a
05:01potential of cancer development and
05:03he coined the name of undetermined
05:05significance to give it that
05:07sense of urgency,
05:08of understanding who would
05:09progress in their lifetime and
05:11potentially preventing it.
05:12And indeed,
05:13even the name smouldering myeloma
05:15gives you that urgency that it's
05:16going to be on fire very soon.
05:18So let's do something about it.
05:20So indeed he had that vision.
05:23As we should think of the mechanisms
05:25of disease progression in asymptomatic
05:27people and potentially intercepting early.
05:30Now in the older days we didn't
05:31have good drugs, we had melphalan,
05:33Prednisone, fat chemotherapy.
05:35So maybe intercepting
05:36early May not make sense.
05:38And indeed the trend or the standard
05:40of care was watch and wait until
05:42people have symptoms and end organ
05:44damage and then we treat them because
05:46we have palliative care and myeloma
05:48survival is only three to five years,
05:51but now we have 30 new drugs approved.
05:53For myeloma,
05:54we have amazing responses and the
05:56question is truly can we change that
06:00thinking of disease interception
06:01at an earlier time point?
06:03Now the other important piece to
06:05think about is myeloma is more common
06:08in African Americans and people of
06:10African descent 2 times or even higher,
06:12more common,
06:13more common to happen at an
06:15earlier younger age.
06:17In fact,
06:17we know that myeloma is more common because
06:20they haven't earlier stage of development,
06:23not because usually of an
06:25mgus transition to myeloma,
06:26not that we know of,
06:27but we don't think that there is a
06:29faster transition from mgus to myeloma.
06:31So really understanding what causes.
06:33Early development of MGUS in an African
06:36American population at the younger age could.
06:39That you help us understand why
06:41they've developed Milo memoir,
06:42but also intercepting it early
06:44because most of those patients,
06:45by the time they're diagnosed,
06:47they're either misdiagnosed because
06:48anemia is very common in African
06:50Americans or because of renal failure.
06:52And again,
06:53renal failure is more common.
06:54So they are getting misdiagnosed.
06:55They don't have the World Cup.
06:57And even when they have the World
06:58Cup and the disease assessment,
07:00they do not get the access to clinical
07:02trials and to car T and to transplant
07:04and all of the options that we have,
07:06so the survival of myeloma
07:08in African Americans.
07:09Unfortunately, it's still very poor.
07:11Despite all of the amazing advances we have,
07:14we still have a huge discrepancy there.
07:16So potentially closing that gap would
07:19be critical for us to understand
07:21how to change the survival of Milo.
07:24So with that in mind,
07:25our hypothesis really our model is
07:27why are we doing it any different
07:30than other cancers?
07:31If you think of breast cancer for example,
07:33you screen early because cancer
07:35screening saves lives.
07:36And I would tell you that the blood
07:38test for a serum protein Electro.
07:39Races and monoclonal protein is much easier,
07:42more sensitive and more specific and
07:44potentially much better for us because
07:46I would rather get a blood sample
07:48done than mammography or colonoscopy.
07:50It's much easier to do.
07:52But even though we with that,
07:54we don't screen for blood cancers.
07:56They're easy to screen but
07:57we don't screen for them.
07:58And even when we find the monoclonal
08:00gammopathy is when I find mgus,
08:02and it's very common in
08:04the general population,
08:053 to 5% over the age of 50 or even
08:07when I find smoldering myeloma.
08:09The standard of care to date is still telling
08:12them watch and wait until you have anemia,
08:15renal failure, fractures in your bones or
08:17lesions in your bones, and high calcium,
08:20what we call the crab criteria.
08:22That would be just like telling a
08:24woman with breast cancer, DCIS,
08:26or stage one, stage two breast cancer.
08:28You know what, you're asymptomatic.
08:30Go watch and wait until you
08:32have metastases everywhere,
08:33fractures in your bones,
08:34and then I'll treat you.
08:36Now you'll have a lawsuit when that case.
08:38So why are we not getting lawsuits?
08:39Myeloma, when we do that exact same idea.
08:43So really we need to rethink the way
08:45we think of treatment of myeloma and
08:47retrain ourselves to think that's not
08:50actually the right way of thinking.
08:52Maybe again,
08:5230-40 years ago when we only
08:54had melphalan at Prednisone,
08:56it was a good idea.
08:57Right now it may not be a good idea to
08:59watch and wait for those patients or as
09:01my patients call it, watch and worry.
09:03So how do we change that?
09:05We have three different areas or
09:08pillars of work that we're doing.
09:10Both in the lab and in the clinic we said,
09:12well, let's detect early,
09:14let's screen early because currently
09:16most patients with mgus and smoldering
09:18myeloma are found purely incidentally.
09:20So let's really understand better
09:22when you screen those patients,
09:24what is the prevalence but also who will
09:26progress and who will not in their lifetime.
09:28The next question is let's
09:29risk stratify those patients.
09:31Not every mgus we diagnose will
09:32go on to progress to myeloma.
09:35So the question is who in their lifetime
09:37will progress to myeloma because
09:38these are the ones you want to treat.
09:40And the others,
09:41you want to tell them you're OK,
09:42you're going to live a normal life
09:44without having to develop myeloma
09:46and that differential is critical so
09:48that you can truly personalize that
09:51risk stratification for patients.
09:53And then the third one is,
09:54unless you know that you can change
09:56the survival of those patients,
09:57unless you can really intercept
09:59and change their survival,
10:00why are you screening for it?
10:02Because otherwise you're
10:03causing anxiety and no change.
10:04So truly I reverse it usually and say
10:07interception is more important because
10:08without interception we should not be.
10:11Training and we should not be
10:13stratifying those patients.
10:14So let's start with early
10:16detection and why it matters.
10:18We have seen lots of nationwide studies,
10:21the first one in Olmsted County
10:23where we indeed know the prevalence
10:25of emcas in the general population
10:273 to 5% over the age of 50.
10:29But that was found in mostly
10:31Caucasian population in the area
10:33of Olmsted County in Minnesota.
10:34So the question was,
10:35can we really detect in a much
10:38more sensitive way than serum
10:40protein electrophoresis?
10:41And in the high risk population
10:42not in the general population,
10:44what is the prevalence of monoclonal
10:46hemoptysis and does a treaty occur in
10:48a younger age in African Americans?
10:50So there has been some studies indicating
10:53that people of African descent as well as
10:55people with a first degree family member
10:58are likely two to three times higher,
11:00have a higher chance of developing myeloma.
11:03So we wanted to ask why in high
11:05risk screen population and this was
11:07started four years ago with the help
11:09of a stand up to cancer Dream Team.
11:11Application where we started to say
11:13let's screen in the US for myeloma
11:16and we said we will do it nationwide.
11:18So it's online.
11:19As you can see here,
11:20you get a QR code and if you meet
11:22the eligibility criteria,
11:24you can sign up wherever you are
11:25and we send you a kit at home.
11:27You go to a quest diagnostic and
11:28you send us the blood sample.
11:30And the second thing we did is
11:31we did it by mass spectrometry,
11:33which is much more sensitive than
11:36serum protein electrophoresis.
11:38Now to do that effort,
11:39we said that we want to screen 30,000
11:42individuals to potentially get 10%
11:44screen positive because that's the
11:46number that from our preliminary data
11:49indicated we will have a positive number.
11:51And then we will follow those 3000
11:54people to understand genomics,
11:55genetics mechanisms of disease progression,
11:58immune microenvironment or non immune
12:00epidemiological causes like obesity,
12:02inflammation,
12:03autoimmune diseases and of
12:04course develop therapeutics and
12:06imaging modalities for those.
12:08People now as we started,
12:10we really had to learn to have boots
12:12on the ground to really do the effort
12:14because if you talk to anyone about myeloma,
12:17even the African American
12:18population would tell you,
12:19I didn't even know.
12:20There is more common in the black
12:22community than in the white population.
12:24So we have to do effort to even educate
12:26what is myeloma to gain the trust
12:29of the African American population
12:30and then start screening them.
12:32And that was a lot of effort
12:34from a team that we hired,
12:35just going to church events,
12:37going to healthcare.
12:38Events,
12:39understanding how to work with
12:41our Congress people like Ayanna
12:42Presley here and of course COVID
12:44hit and all our effort got shot down
12:47because you cannot do that on zoom.
12:49So it really took us a lot of effort
12:50to try and restart all of this.
12:52And indeed we just started to go back
12:54to health fair events and restarting it
12:56while while we were in COVID we said,
12:58well let's look at datasets and samples
13:01that are already collected in other cohorts.
13:04And this is when we turned to
13:06the mass general, Brigham,
13:08so mass general.
13:09Brigham is a huge sample collection
13:11study that's been going on now for the
13:13last 10 years with samples as well
13:16as of course clinical data annotation
13:18from all of the partners healthcare
13:20system or MGB as we call it now.
13:23So we collected the same criteria,
13:25African-American or people of first
13:28degree family members from 80,000
13:30samples that we have in MGB and
13:33total enrolled so far is 12,592
13:35of those from the US is
13:386485. We also opened a promised South
13:42Africa one where actually they're
13:44getting almost to 2000 samples now
13:47that they've recruited prospectively.
13:49And we're also going on into opening
13:51it now in Israel because of the
13:53family histories as well as many
13:55other countries that we can do.
13:57And we were screening in my lab almost
13:591000 samples a week and we can do
14:01even more because mass spectrometry
14:02can get to a higher throughput level
14:04and you can then get detection of
14:07monoclonal proteins as well as light.
14:09Machines in a very quantitative way
14:11compared to serum protein electrophoresis.
14:14In fact, we set up the normals for binding
14:17site and now we are part of their FDA
14:21approval hopefully soon for binding site.
14:23So these are just some of the numbers
14:26showing you from MGB from promised
14:27South Africa and promised us.
14:29But this is the largest number of
14:31African Americans who have been screened
14:32to date as well as people with family
14:34history and it was interesting when
14:36we saw families with 567 members.
14:38We have mgus and myeloma and lymphoma.
14:41Now you start asking questions of
14:44germline events of events that
14:46really can lead to that development
14:48in an early risk population.
14:50So this is the paper that we
14:52published last year just for the 1st
14:547000 people and now we're actually
14:55going on for the larger cohort.
14:57And as you can see here,
14:59the people with a family history of a
15:02blood cancer were 3866 and people of
15:06African descent or blacks were 2439.
15:09And this is the mass spectrometry
15:10and I call this the Christmas tree.
15:13So mass spectrometry is quantifiable
15:15and you can also reflects it to
15:18LCMS to give you a further detection
15:20of the monoclonal protein.
15:22So all of these were truly monoclonal
15:24proteins that were quantified and verified.
15:27What we found is anything above 1
15:29gram per liter is something that
15:31you can also detect by serum protein
15:33electrophoresis because we did spap
15:35the traditional method in the sum of
15:38the samples or in almost all of the samples.
15:39If we did anything below that at
15:42.2 grams per liter,
15:44you could potentially also
15:45detected by immunofixation,
15:47but of course you have quantification
15:49and much more sensitivity
15:50by the mass spectrometry.
15:52So we kept those terms as they are.
15:54But interestingly and I still
15:56remember it when we got the first
15:58data because we couldn't believe it,
15:59we found another 20% of people with
16:02very small monoclonal gammopathy
16:03that were much lower than the level
16:06that we can detect by immunofixation.
16:08And at first we said,
16:09well these are probably errors,
16:11so we reconfirmed them.
16:12Maybe these are people who have infections,
16:15so we rescreen them.
16:17We kept going on to try and understand
16:19what this is and we finally said,
16:21well no one has they've ever discovered
16:23very small monoclonal proteins.
16:25Let's let the research tell us what it is.
16:27Now we wanted to term this
16:29something separate that mgus
16:30because we really didn't
16:31know if this is mgus or not.
16:32So we called it mgip,
16:34monoclonal gammopathy of
16:36indeterminate potential alert ship.
16:38Don't let him have the praises
16:40of indeterminate potential.
16:41And the story goes that David Steensma
16:44is the one who coined the name chip.
16:47And I saw him once and he said,
16:48well I called chip based on M Gus.
16:51I was trying to imitate
16:52what doctor Kyle had done.
16:54So now we called M give based on
16:55chip and it keeps going round and
16:58round in hematological malignancies.
17:00But what is this chip and what is this
17:02mgus prevalence in this high risk population?
17:04So you can see here by age
17:06that mgip is very common,
17:08almost 20% of the population.
17:10It increases with age,
17:11but as you go on with age the M
17:13Gus proportion of those monoclonal
17:15gammopathy is increases more and then
17:17light chain mgus was actually a very
17:19small number in that population.
17:21If I just take a standard values 3% of
17:25the population in general population
17:26is what doctor Kyle had described
17:28before and that was based on Spep.
17:30If you double it because of the
17:33higher risk population which is
17:35true 6% in our population are espec
17:37positive and then if you look by mass.
17:40That trauma too because it's much
17:42more sensitive and can get you
17:44immunofixation than we are 13% and
17:46that's not even accounting for the mgip.
17:49So a large proportion of our high risk
17:52individuals have mgus and we need to
17:54understand better why they have it,
17:56but also who would progress
17:58in their lifetime.
17:59Now in general all monoclonal gammopathy's
18:02were associated with worse overall
18:04survival and it was not because of myeloma,
18:07it was also because of many
18:09other all caused mortalities.
18:10Autoimmune diseases,
18:11cardiovascular disease,
18:13many other lymphomas.
18:15So we started seeing maybe mgus
18:16and immune dysregulation in those
18:18patients may have other effects,
18:20not just myeloma development.
18:21And thus lead is leading us to
18:24understand more into correlations
18:26of mgus and chip mutations,
18:28both of them cause inflammation,
18:29potentially increased cardiovascular risk.
18:31We're trying to understand how that
18:33regulates the immune system and immune aging,
18:36how it correlates with autoimmune
18:37diseases and so many other questions.
18:40But what we were intrigued by is
18:42those M Gibbs and why were they
18:44present in many of those people.
18:46And most of those M gifts were
18:49actually IG M Mgip, not IG or IGA.
18:51So the first thing we said.
18:53Well, maybe it's an isotype class switch.
18:56This is the precursor of myeloma
18:58and it's IGM positive and then
19:00it's class switches to IgG as it
19:02progresses and this is the first
19:04event that requires the mutations.
19:06The other possibility was maybe
19:08these are lymphomas and they secrete
19:10very low levels of IGM that's non
19:13detectable by spep and in general
19:15we don't even screen for lymphomas
19:17by serum protein electrophoresis.
19:19So we're under we're not detecting
19:21enough of the cells and low grade.
19:23Performers and now we have a
19:25technology that can be
19:26more sensitive and indeed for us to
19:29prove that, we took samples from healthy
19:31donors from two people who have mgus,
19:34one of them had mgus and mcgiff and
19:36from 2 participants who had mgip.
19:38And we did CD19 and CD138 selection of
19:41the peripheral blood because we don't have
19:43bone marrow biopsies on those patients.
19:45And indeed we did first single
19:47cell sequencing for VDJ,
19:49so now for the BCR to see if
19:51they have clonal BCR in those.
19:54Patients and then of course we did
19:56gene expression profiling afterwards
19:57with the single cell RNA sequencing.
19:59And what was surprising as you can
20:01see here for this patient for example,
20:03they had one clone that was all VDJ,
20:06the same clone and you can see that
20:08in this patient all of those cells.
20:10So this is single cell RNA sequencing
20:12and the blood,
20:13all of the cells were for one
20:15clone only in that patient.
20:17And then this second patient had two
20:19different clones as you can see one
20:21of them was very high which is the.
20:23The red one here and then the
20:25second one here in the orange one.
20:27And indeed we reconfirmed that
20:29those patients,
20:30one of them was indeed an early CLL
20:33case because we did flow cytometry
20:35and because this patient had almost
20:3760% of the cells are all clonal,
20:39we were able to do whole genome
20:41sequencing on that sample.
20:42And indeed it was an atypical lymphoma,
20:45likely a post germinal B cell lymphoma.
20:48So either DLBCL or something like
20:50a marginal zone which was MIT
20:5388 positive and it had.
20:54Copy number alterations as you see here,
20:57chromosome 3,
20:58chromosome 18 with a gain
21:00of those chromosomes.
21:01So indeed by both DNA,
21:04by protein level in flow cytometry
21:05and by RNA sequencing we were
21:07able to indicate that two of those
21:09cases were lymphomas.
21:10Now we're expanding that cohort to
21:12another 4050 samples with single
21:14cell RNA sequencing and then it
21:16will be followed by DNA sequencing
21:18of course if we find this positive,
21:20but that opens the door for saying we
21:22can screen also for other lymphomas.
21:25And not just for myeloma.
21:26And the question is what are all
21:28of those monoclonal gammopathy is
21:30doing in our general population.
21:31So to answer some of those questions,
21:33we're moving on to other bigger cohorts.
21:36So now we're talking to the UK Biobank,
21:38they have a half a million samples that
21:40have been collected over 20 years.
21:42We're talking to end Haynes and
21:44trying to get samples from NHANES
21:46as you can see here 7937 another
21:498000 and PLO another 14,000.
21:51We are also trying to see if we
21:53can get access to the million.
21:55Veterans project to all of us and
21:57many other cohorts that have already
21:59collected large numbers of samples
22:01to ask big questions of what is the
22:03prevalence in high risk population,
22:05but also what are those early
22:08monoclonal democracies doing to
22:09the general population.
22:11And then of course we have
22:12collaborations with all link
22:13to try and look at the protein level
22:15in those patients with proteomics.
22:17So the next step I'll take
22:18you through is understanding
22:19mechanisms of disease progression.
22:21If you have mgus or smoldering myeloma,
22:23you want to know what is.
22:25My personal risk of going on to
22:27dissolve myeloma and I don't
22:28have in the slides here what we
22:30just published yesterday night,
22:31it just came out in Lancet hematology,
22:33a new dynamic model to understand
22:35risk of progression because we know
22:38that the current clinical criteria,
22:4020% plasma cells in your bone marrow,
22:412 grams M spike,
22:4220 light chain ratio for a smoldering
22:45myeloma are good but not good enough
22:47because they give you a 50% chance of
22:50progression in two years and that's
22:52basically like flipping a coin,
22:5450% chance of progressing.
22:5550% said chance of not progressing.
22:58So we need something better
22:59than that or to improve on it.
23:01So we developed a dynamic model
23:03and now this is a risk calculator.
23:05Any patient,
23:06any physician can use the risk calculator
23:08and have the prediction of five years,
23:1010 years, 20 years,
23:12what is my personal risk
23:13based on clinical markers.
23:15But clinical markers are
23:17assessing the tumor burden,
23:19how many cancer cells you have.
23:21It doesn't give you the underlying biology,
23:23how fast are they growing.
23:24So we need more.
23:26And that the dynamic model helps
23:27you because the more data you
23:29enter in the light chain increase
23:30or the M spike increase,
23:32it gives you the dynamics
23:34of tumor progression.
23:35But we need something as the genomics
23:38and immune and other factors.
23:40So here's one of the first papers we
23:42published a few years ago where we
23:45looked at whole exome sequencing in
23:47250 patients with smoldering myeloma.
23:49And now we expanded it of course
23:50so many others.
23:51And we found that there were three main
23:54mechanisms of genomic aberrations.
23:55That leads to progression or that are
23:58associated strongly with progression
24:00to myeloma and these were MAP kinase
24:02mutations like ANRAS and Karas
24:04ATM and ATR and P53 mutations DNA
24:07repair pathway and of course make
24:09alterations or translocations.
24:11In fact I think that if we have Mike,
24:13we already have myeloma and potentially
24:15some of those alterations are all
24:18secondary mutations and secondary
24:19alterations that occur when you're
24:22already going towards myeloma,
24:23when there is no coming back
24:25and hopefully these.
24:26Will become routine in our
24:29understanding of if someone has
24:30smoldering myeloma and has one of
24:33those likely they have very high risk
24:35of progression and we should consider
24:37therapeutic interventions in them.
24:39Now what we found lately is that one,
24:42many of our patients don't get
24:44bone marrow biopsies or serial
24:45bone marrow biopsies and two,
24:46whole exome sequencing is OK and
24:48it's not good enough because it
24:50doesn't give you the primary events,
24:52the translocations that occur in those
24:54patients. So this is a paper that.
24:56Just got published a few weeks ago.
24:58Work from Ankit and John Batiste
25:00where we took circulating
25:02tumor cells, isolated them,
25:03developed a method of low input DNA and
25:06were able to do whole genome sequencing
25:08from as low as 30 to 50 cells that
25:11you can get in the peripheral blood.
25:12So you can see in mgus
25:14and smoldering myeloma.
25:15Many of them have small numbers of
25:17circulating tumor cells and when you are
25:20able to capture them and purify them,
25:22you can do whole genome sequencing
25:23and you don't even have to go
25:25deep sequencing because the.
25:27Security is so good in those samples.
25:29So indeed we had head-to-head
25:31comparison of circulating tumor
25:33cells versus bone marrow cells so
25:35that you can show indeed that all
25:38of the clonal and subclonal events
25:40can also happen in the blood.
25:41And you don't need the bone marrow biopsy,
25:43but also head-to-head comparison to fish,
25:46which is the standard of care that
25:48we have right now in myeloma,
25:49yet another 50 year old technology.
25:51So indeed, of course,
25:52no surprise there that whole genome
25:54sequencing is better than fish,
25:56indeed it.
25:57And get you all of the translocations,
25:59but it can get you much more.
26:00You get mutations,
26:01you get copy number alterations,
26:03you can even get translocations
26:04you couldn't detect by fish.
26:06And indeed because you're purifying
26:07small numbers of cells especially
26:09in the peripheral bloods,
26:10you can do that multiple times during the
26:13serial development of a patients progression.
26:16So you can ask the question when
26:17the MIC clone is growing,
26:19what is going on and when can
26:21I treat this patient.
26:22Now I'll move on to single cell and
26:25I borrowed this slide from Aviva.
26:27Who basically tries to tell you why do
26:29we need to go to the single cell level,
26:31and it's basically when you
26:32do bulk sequencing,
26:33whether it's whole genome
26:34sequencing or bulk RNA sequencing,
26:36you're sequencing all of the cells
26:38mushed together like a smoothie.
26:39Now it tastes good,
26:40but you can't really tell the differences
26:42between a strawberry and a Raspberry.
26:44You can't even tell if it's a good
26:47Raspberry versus a mutant Raspberry.
26:48Single cell sequencing gives you that.
26:50It gives you that ability to
26:52differentiate them from each other.
26:54And of course spatial transcriptomics
26:56or spatial sequencing.
26:57Is the ultimate goal where you get
26:59the whole fruit tart and you can
27:01understand better the localization of
27:03all of those cells in the environment.
27:05So what we did is we said,
27:07well,
27:07let's look at the tumor cells
27:09in the bone marrow compartment.
27:11And this is a study where we did
27:12it in collaboration with MIT
27:14and of course with the broad.
27:15All of our work is with the Broad
27:17Institute where we said we're lucky
27:19enough in mgus and smoldering myeloma
27:21that not all of the plasma cells are
27:24actually malignant plasma cells we
27:25have some of them are normal plasma cells.
27:27So the potential here is instead of
27:31looking at interpatient variability,
27:33healthy versus cancer patients,
27:34we can actually look at the
27:37intra patient variability,
27:38healthy cells,
27:39plasma cells within one
27:41patient versus malignant plasma
27:43cells. And now you can ask the
27:44questions of here are the normal
27:46plasma cells here are the malignant
27:48plasma cells from the same patient,
27:49what are the differences in them and
27:51can I understand that mechanism of early
27:54genomic events and transcriptional
27:56changes that occur with malignant?
27:58Transformation,
27:58even within the same neoplastic cells,
28:02I can find subclusters that are
28:03very different from each other.
28:05There is a proliferating cluster.
28:07There is some that have higher expression
28:09of certain genes and that can help you
28:11understand when the patient is treated,
28:13which subcluster may respond and which
28:15one may be resistant to therapy.
28:17Now we moved on to do even more work on that.
28:19So this was presented in Ash this
28:22year where we showed 245 samples
28:25from 234 patients.
28:26Here we did not only do the jacks.
28:29The gene expression single cell sequencing,
28:31but we also did BCR profiling
28:33on all of those patients.
28:34So now you can get with the VGA
28:37or with the BCR sequencing the
28:39clonality of those patients.
28:41So this just shows you the potential
28:43of really understanding the tumor
28:45compartment in those patients.
28:47We've done the same thing
28:48on circulating tumor cells,
28:49but I'm not showing that data here.
28:51So of course with a huge number of samples,
28:54what was very interesting is indeed all of
28:56the malignant samples cluster separately.
28:59It was not surprising.
29:00We saw that before and the normal
29:02plasma cells clustered together from
29:03all of the patients and indeed the more
29:06you look at the number of cells are
29:07increasing as you go on to myeloma,
29:09the malignant versus normal compartment.
29:12But what was interesting is we
29:14compared head-to-head cytogenetics
29:15from those patients with fish or when
29:17we have whole genome sequencing to
29:19the single cell RNA sequencing data.
29:21And indeed you can see that the hyper
29:23deployed cases were confirmed, the 414,
29:25you can confirm it with FGFR 311,
29:28fourteen with cycling. 11416 and so on.
29:31So you can be very accurate in
29:33understanding who has a specific
29:35translocation.
29:35But then we said well 50% of our samples
29:39did not even have good fish information.
29:42Either it failed which happens a lot or
29:45they give us the fish information with
29:47an igh partner that we cannot detect.
29:50So we were basically blinded
29:52to know what is happening.
29:53So we used our single cell RNA sequencing
29:56to generate what could potentially be the.
29:59Cytogenetic information of those patients.
30:01So you can see here that all of the
30:04unavailable or we didn't know what they were,
30:06we were able to reclassify them into
30:09specific cytogenetic abnormalities.
30:10And this is the confusion matrix
30:12showing you that indeed all of
30:14the unclassified we were able to
30:16get them into a 4141114 and so on.
30:18Biggest number was the hyper
30:20deployed numbers.
30:20So that can tell you that you can
30:22use RNA sequencing to basically
30:24predict what are the cytogenetic
30:26abnormalities at the single cell level.
30:28So now you can really say.
30:29Subclusters of those patients and
30:32subclonal abnormalities and we took
30:34it even more because we have potentially
30:37the ability to identify rare events.
30:40You can now find 814 translocation
30:42extremely rare in myeloma.
30:44We miss it in many patients and
30:46now we can find it with this math
30:48A and you can even look at their
30:50expression of certain genes.
30:52So for example they express
30:53high levels of Mike,
30:54they don't express other levels
30:57of other genes for example in 14.
31:0016 or in 1420 translocations.
31:02So now you can really go into the genetics
31:05and the transcriptional changes that
31:07are occurring in those rare events.
31:10So when you go back to also looking
31:11at the normal versus malignant
31:13cells in those patients,
31:15you can also ask questions that are very
31:17specific to the phenotype of those patients.
31:19So for example,
31:20we always think that CD 56 is highly
31:23expressed on malignant plasma cells.
31:25That's not actually true for the
31:28small numbers of 1416 and 14.
31:3020 cells,
31:31they are negative for CD 56
31:32and you can go on.
31:34So now you can really say if I'm going
31:36to develop a therapeutic target not BCMA,
31:39but others,
31:40I want to understand whether it's
31:41highly expressed on those cells with
31:43certain genetic abnormalities and
31:45those are the patients that I will not
31:47or I will include in my clinical trial.
31:49Now moving on to the gene expression data,
31:52you can see here these are the
31:55top highly expressed or the top.
31:57Significantly downregulated genes
31:58across the spectrum from mgus to
32:01smoldering myeloma to myeloma.
32:02And because again we have
32:03huge numbers of cells,
32:04you have more,
32:06you have a better ability to detect
32:08genes that really are modulated
32:10as you go on to progress like.
32:13T3 which is a leukemia growth factor
32:15as well or transcriptional factor as
32:17well as many other genes that get
32:19down regulated as you progress but
32:21also you can identify new targets
32:23potentially for developing therapeutics
32:25or new by specifics or new cartes.
32:29And then we developed a signature
32:32that was developed not from the normal
32:34plasma cells but from the malignant
32:36plasma cells and it was increasing
32:38as you go on from mgus to myeloma.
32:40And that signature by NMF by non
32:42matrix factorization was able to also
32:45detect when we applied it to compass
32:47data which is the overt myeloma data,
32:49it showed us a progression free
32:51survival and overall survival
32:53difference and it could be predictive
32:55of prognostic risk in those patients.
32:57So if you put that.
32:59In those patients as well as
33:01looking at the proliferation index,
33:03you can actually stratify the
33:04patients as low risk,
33:06intermediate and high risk even in
33:07the compass data in those patients.
33:10We then applied it to the gene
33:11expression data to all gene expression
33:13data from mgus to myeloma and indeed
33:15show that this can be predictive.
33:17So again not only genomics like
33:19DNA data that we have.
33:21Like map kinase mutations and so on
33:23can be predictive of who will progress.
33:25Now at the RNA level,
33:27we also have a gene expression
33:28profile that can be predictive of
33:30who would progress and who will not.
33:32So moving on to the immune system,
33:35here I'm showing you that the
33:37tumor system is an ecosystem.
33:40You cannot look only at the cancer cells,
33:41you need to look at the cancer and immune
33:43cells and of course not immune cells to
33:46understand better what causes progression.
33:47So the first thing we did a few years ago
33:49is again we did single cell sequencing.
33:51Of the immune cells in the bone marrow
33:53from MGUS smoldering to myeloma.
33:55And indeed what was surprising is we
33:57found that there were compositional
33:59changes that happened as early as mgus.
34:01It looked almost like myeloma.
34:03And we were shocked because we usually
34:05think that mgus is a benign disease.
34:06You're walking around,
34:07you have a very small chance of progression.
34:10Why would your immune system be so
34:11altered that it looks like myeloma?
34:13So we found T regs are increased,
34:1516 monocytes are increased,
34:17NK cells are altered,
34:18and then later on you have
34:20further functional changes.
34:22You have loss of the memory cytotoxic
34:25CD8 cells and then you start having less
34:28granzyme K which are the earlier stem
34:31cells and more granzyme B in those patients.
34:34And this is just showing you
34:35some of those changes.
34:36You can see here those memory excitotoxic
34:39cells almost completely depleted in
34:41patients with smoldering myeloma,
34:43sorry, with overt myeloma.
34:44So we went on to ask a couple
34:46of other questions.
34:47One is,
34:48are those changes altered if I treat
34:50someone with smoldering myeloma
34:51and can we expand that in also the
34:54peripheral blood of those patients?
34:55So this is work by Romanos,
34:58just got published a couple of weeks ago,
35:00again also in cancer cell where we took
35:03samples from patients on a clinical trial.
35:05With Elotuzumab limited dexamethasone 51
35:07patients who were treated on high risk
35:10smoldering trial and we took samples
35:13baseline cycle nine and end of therapy.
35:15And what we found is we
35:16found a couple of things.
35:18First is of course,
35:19the compositional changes were very similar
35:21to what you expected in our other study,
35:23but now it's a much bigger #190 samples.
35:26So indeed more T regs,
35:29more CD4 TNS and so on.
35:33But what we found that was
35:34interesting is a couple of things.
35:36One,
35:36because we had single cell TCR
35:38sequencing on all of those patients,
35:40we found that you actually have
35:42a significant change in the
35:44diversity of the T cells even
35:46in early smoldering myeloma.
35:47So this is just showing you when I
35:50resample the TCR in all of those patients,
35:52always we had a smaller diversity in the
35:55healthy compared to smoldering myeloma.
35:57So it shrinks significantly and you
35:59would think that it shrinks because
36:01you have one clone that expands.
36:03So the diversity is smaller and indeed.
36:06It is clonal expansion,
36:07but it's not just one clone,
36:08it's multiple clones and
36:10some of them are very small
36:12clones that expand in those patients.
36:15Now, interestingly, that expansion
36:17was merely in granzyme BC8T cells.
36:20As well as T regs,
36:22and you can see it here, uh,
36:24nicely that those clonal T cell expansions
36:26were in the CD 8 terms in those patients.
36:29So that tells you the immune system is
36:32trying to react to the cancer cells,
36:34but it's exhaustive and it cannot
36:35do a very good job in responding to
36:38those cancer cells and that could
36:39potentially be useful for therapeutic
36:41interventions in the future,
36:43especially with TCR therapeutics as we go on.
36:46Now, the other question we said is can
36:49we use the immune system as a biomarker?
36:51Of disease progression,
36:52can I use an immune signature
36:54that tells me this patient will
36:56respond to therapy or not?
36:57And after therapy did they
36:59normalize their immune system.
37:00So indeed we found the signature
37:02that is predictive of response which
37:04is if you are reactive to the tumor
37:07cells then you have a better chance
37:09of responding to therapy and a
37:11long-term progression free survival.
37:13And post therapy if you normalize your
37:15immune system indeed you have a much
37:18better progression free survival and
37:19that tells us that indeed those patients.
37:22Can have that normalization of the
37:24immune system along with MRD and
37:26we're hoping to apply that for
37:28all of the future studies so that
37:30you don't only look for Mart,
37:32you also look for pin in those patients
37:34both therapy and your normalization.
37:36And this is just showing you some of
37:39those factors specifically for grand time,
37:41OK,
37:41as you go on to that normalization
37:43in those patients,
37:45now we moved on into the blood and said,
37:47can we use the blood instead of the
37:49bone marrow again in those patients.
37:50So indeed here is just showing you
37:52the volcano plot of those patients
37:54and indeed you have the same changes
37:57in the blood as you have in the bone
37:59marrow of those patients and the same
38:01thing also happens for the T cell receptor.
38:04So this is just showing you the T cell
38:06diversity and the peripheral blood.
38:07And it mimicked exactly what happens
38:09in the bone marrow of those patients.
38:11Not only that,
38:12if I just do another confusion plot
38:14and say give me randomly anyone who
38:16has a peripheral blood sample and I
38:18will tell you if they have mgus or not.
38:21It was very predictive in the blood
38:23by the immune cell signature that I
38:24can tell you this one is healthy,
38:27this one is mgus.
38:28Now that opened the door for us to
38:30say can we use it also for cancer
38:31screening in general.
38:33And this is something that we're
38:35trying to develop right now.
38:36So with that we have.
38:37Big data,
38:38big questions,
38:39which means that we have 317 new samples
38:42that we sequenced bone marrow and
38:44peripheral blood to really ask those
38:46bigger questions of immune regulation
38:48in mgus and smoldering myeloma.
38:50And now you can have more
38:52expression data that really
38:53defines the progression signatures
38:55because you have more samples,
38:57you can differentiate what causes progression
38:59from mgus to smoldering to myeloma,
39:01not causes what is associated with it.
39:04Hopefully causative would be all
39:06of the functional studies that we.
39:07Can do in vivo and in vitro to say
39:10what is really causing progression
39:12in those patients and then of
39:14course at the gene expression level.
39:16So at the compositional changes,
39:18most of the things happen at mgus and then
39:20they stay constant or increased slightly.
39:23But at the signatures of the genes you have
39:25a huge difference in interference signaling.
39:27You see that sudden change of granzyme
39:30B increasing and you have more of
39:32those granzyme BCZ its cells that
39:33are more senescent as you can see
39:35here with their expression of KR.
39:38One and less cytolytic.
39:40So they're not capable of really
39:42responding to the cancer cells
39:44and this is just showing you how
39:46altered immune system goes on from
39:48progression from mgus to myeloma.
39:50And then again because
39:51we have so many samples,
39:53especially low risk smoldering,
39:54which we think is likely more like
39:57an mgus and some of those mgus
39:58look more like smoldering myeloma.
40:00So the clinical factors of what
40:02we call mgus and what we call
40:04smoldering myeloma may actually be
40:07biologically completely different.
40:08And they are intermixed with
40:10mgus and smoldering myeloma.
40:11We have biological relevance from each other.
40:14So you can see here huge diversity
40:16changes that occur in some of the
40:18MGA samples as well as the smoldering
40:20myeloma samples in those populations.
40:23And then finally,
40:24we're starting to look at the
40:26spatial transcriptomics.
40:27But until then we started to look
40:29at the cells that basically are
40:30adhered to each other.
40:32What is close to a myeloma cell when
40:34we pull it in a CD130 is selection,
40:36and indeed we found many of the.
40:38B cells, granzyme key positive cells and.
40:43Megakaryocytes were highly,
40:45uh,
40:45you know,
40:46uh attached to the tumor cells
40:48indicating that there is a lot of
40:51interaction between those cells.
40:52So in the last few minutes I'll
40:54talk about clinical interception
40:55and we have done many clinical
40:57trials throughout the years,
40:59but now we're thinking of it more
41:01of that specific interception being
41:02precise in our interception what
41:04we call precision interception.
41:06So in the older days we have
41:08shown there is a proof of concept
41:10that indeed observation versus
41:12treatment treatment is better.
41:13In progression free survival and
41:15in one case overall survival with
41:17the Lenalidomide index studies.
41:19But these were early events
41:21or early attempts.
41:22Let's do something better than that.
41:24So our efforts are divided
41:26into early prevention,
41:28metformin, intermittent fasting,
41:29things that really prevent progression.
41:31Then we have targeted approaches,
41:33MAP kinase mutations,
41:351114 with venetoclax, we're looking
41:38at synthetically salty in one queue,
41:40abnormalities and so on.
41:41Then we have Immunotherapeutics,
41:43vaccines,
41:43T cell therapy with carton by
41:46specifics and so on,
41:47and then novel combinations.
41:49And we're doing now 4 drug regimen.
41:51There are RVD, which is basically
41:53the standard of care of myeloma.
41:55Bringing it on into an earlier
41:56setting with the idea that can we
41:59cure the patients at the earlier
42:01precursor stages and at least can we
42:03make sure that we do never develop
42:05end organ damage in those patients.
42:07So I'll just give you a couple
42:08of examples of those trials.
42:10This one is ongoing right now,
42:12immunol prism and this is the
42:14first time we treat patients with
42:16immunotherapy in smoldering myeloma.
42:17So we chose these inclusion criteria
42:20for high risk smoldering myeloma
42:21and we're randomizing patients
42:232 to one to tech listenable.
42:25Bcma CD3 antibody by specific
42:28antibody or landex,
42:30our first six patients were only to
42:32Christmas because the FDA mandated that
42:34we go very slowly and we do lose reduction.
42:37And then now we're actually
42:39randomizing patients and we're up to
42:4118 patients currently either treated
42:42or going to treat soon with the
42:45primary endpoint of response rate.
42:46And I can tell you preliminary,
42:48we are not seeing the same rate of CRS.
42:50We are not seeing the same rate
42:52of infections you see in other
42:54patients and we're seeing impressive
42:55responses in those patients.
42:56And then of course the other option
42:58is can I use the one and done cartee
43:00therapy as early as possible when
43:02you have less tumor burden and when
43:04you have better T cell response
43:06and potentially will this be a
43:08curative intent in our patients.
43:09So we're hoping to open soon the first
43:12car T therapy in early precursor settings
43:14in high risk smoldering myeloma.
43:17And I can tell you when I
43:18submitted it to the FDA,
43:19the first thing I got
43:21back was absolutely not,
43:22you're not doing this and we were able
43:25to convince the FDA to give us the Ind.
43:27And we're hoping soon to open that trial.
43:30So with that,
43:30I hope I convince you that early
43:33detection and early interception in
43:35one disease like myeloma matters.
43:37And hopefully this can be applied
43:38to many other diseases and we can
43:40change the survival of our patients.
43:42And I want to thank of course amazing people,
43:44the lab, the clinical teams.
43:47And our collaborators from really
43:49all over the world,
43:50but all of course our funders
43:51stand up to cancer, MRI, FLS,
43:53NIH,
43:54our collaboration with gadgets
43:55who just basically does everything
43:57with us at the Broad Institute
43:59and above all our patients.
44:01Thank you.
44:05I mean, absolutely spectacular,
44:08incredibly, incredibly exciting.
44:09So we have doctor nefarious
44:11here as our panelist too.
44:15And maybe I have a quick question.
44:20Do you see correlations between,
44:23you know, the mutational spectrum and
44:25then the immune environment? Yeah.
44:30How do they happen? Yeah, we
44:32haven't even started putting it together.
44:35I mean it's it's an so if any
44:38bioinformaticians you have,
44:39please come because we
44:40have enough data for many,
44:42many years to analyze the data.
44:44But yes, now that we have that many samples,
44:46you can start asking the question
44:48in an 1114 or in a certain mutation,
44:51what are the immune, that's regulations.
44:52The older samples were very small numbers
44:54and of course if you start subdividing,
44:56if P53 haven't foreseen, you don't have.
44:59Of data.
44:59But now as we're enlarging the cohorts,
45:02we will start to see that correlation.
45:10Now you wanna ask a question,
45:11I think there there is a question in
45:13the chat, but Irene congratulations
45:15on your really tremendous success
45:17and in terms of promise study,
45:20I think that's really a successful enrollment
45:23and of extensive data generated there.
45:26In terms of potential future
45:29clinical applications,
45:30I mean terms like number needed to
45:32screen are used for breast cancer,
45:3480 or 100 seems acceptable.
45:36What's your sense of number of
45:37needed to screen potentially for
45:39high risk patients with myeloma?
45:40Or perhaps those with family history.
45:43Yeah,
45:44great question. And this is indeed
45:45exactly the question of how can
45:47we make it standard of care,
45:48what is needed for us to
45:50switch to an early detection.
45:51I think unlike breast cancer and other
45:53solid tumors where you know that if you
45:56cut it and the patient survived in mgus,
45:58if you find it, what is the,
46:00what's the relevance, right,
46:02because we know sensitivity
46:03and specificity is very good.
46:05So that's not the problem that we have.
46:07So I think what we have thought
46:09of is actually.
46:10That showed that indeed interception
46:13matters because then early
46:14detection would matter and 13%
46:16prevalence is a huge number.
46:18I mean these are not numbers you
46:19see in any other cancer right,
46:20breast or lung and all of those.
46:22So a high risk population being African
46:25American or of African descent or
46:27black or first degree family members
46:29should be such a low hanging fruit.
46:31Like you don't need to justify numbers
46:34needed to treat with the 13% prevalence.
46:36And that's just mgus if you add the M
46:39*** which could be the taxing lymphomas.
46:41Now we have a huge number of
46:43people walking around with early
46:44lymphomas and myelomas.
46:47And if I, if I may just ask one more in terms
46:50of I think you put you,
46:51you had some of this in the slides in
46:53terms of you know fasting or metformin
46:55or other metabolic interventions.
46:57What's your potential vision on
46:59preventive intervention for those who
47:01you capture as mgus or early stage?
47:03What's your current counseling
47:04that you provide? Yeah,
47:05so you know the interceptions are
47:07easy because I can give something
47:09and I can see the response.
47:11But then so many patients have this
47:13earlier factors and there's a lot
47:15of questions of obesity microbiome.
47:17Metabolic pathways, so we're starting
47:19to do now microbiome studies.
47:21We're starting to do metabolic changes
47:22and immune and again they come together,
47:24right, the microbiome,
47:26the metabolomics and the immune
47:27dysregulation to lead to progression.
47:29So a lot of that effort we're starting
47:32to work on because that can also
47:34be therapeutically intervened with
47:35whether you have microbiome therapy
47:37or of course other mechanisms.
47:39And then Catherine Mayernik and Betsy
47:41O'Donnell are amazing and trying to
47:44develop other studies like that metformin,
47:46intermittent fasting.
47:47Exercise and fitness things that can
47:49really help modulate the lifestyle of
47:51patients for modifications basically
47:53that can help prevent progression.
47:57Yeah, I think your former
47:58answer may have to Natalia may
47:59have answered the question in the chat um
48:02by um Manju Prasad who's asking is risk
48:06stratification for mgas being offered
48:07to patients in the clinical setting.
48:10Yeah. So actually our publication that
48:12just came out yesterday and Nancy
48:14mythology was specifically to ask that
48:17question because many of our patients
48:19don't have a bone marrow biopsy.
48:21So you think they have mgus,
48:22they actually have smoldering myeloma and
48:24then you don't even know and as I said the.
48:27Clinical annotation of what is mgus
48:29and what smoldering myeloma is so
48:31hard because the bone marrow is patchy.
48:33So I can have a 10% plasma cells
48:35but I'm really mgus or I'm not
48:38really small ring myeloma. So the
48:40Pangea model was actually
48:426700 participants where we annotated
48:44all of their clinical data and we
48:47developed the clinical model of
48:48progression based on dynamic numbers.
48:50If they're M spike is increasing,
48:52if their light chains chain is
48:54increasing hemoglobin it would freezing,
48:55creatinine is increasing.
48:56Remember all of those are blood
48:58things and then we added bone marrow,
49:00uh, as well as age and we did the
49:02model with or without bone marrow
49:04biopsy to help you really say
49:06if I had a bone marrow biopsy,
49:07here's the risk,
49:08if I don't have the bone marrow box,
49:09here's the risk.
49:10But it was a model for all small ring model.
49:13So I would use it.
49:15It's available online there is calculated.
49:17So look up angia and hopefully
49:18you'll be able to find.
49:22Other conflicts? And considering the
49:26fact that so many of these younger
49:28patients who are diagnosed with full
49:30blown myeloma in their 30s or 40s,
49:32you'd have to conceive that there are likely
49:35have had endust from their teenage years.
49:37So I wonder if you have any germ line
49:41genomic data within the within the
49:44promise cohort or elsewhere? Yeah.
49:46So we are trying to sequence right now all
49:49of the samples which won't even sequencing.
49:51Uh, the MGB cohort already had their
49:55smooth arrays or now they're actually
49:57redoing whole thing security in the
49:59same samples and then of course many
50:01of those other folks had already.
50:03So you're right, we're trying to
50:05actually do that all of this data.
50:09OK, I think they're having some static
50:13from me or from somewhere else.
50:16Nope, it's. OK, it may have been
50:19your computer, but let me umm,
50:21so there this Mendez
50:22is asking a question in the question answer.
50:25So how do you think of
50:26mgip compared to lymphoid,
50:28clonal hematopoiesis and is in GIMP
50:31and the absence of lymphoma CL and
50:33manifestation of lymphoid cloning,
50:34hematopoiesis and then any information
50:38on overlapping somatic mutations.
50:41So great question. So we work very
50:43closely with Ben Ebert and Lachelle
50:44weeks and others to understand really
50:46the interlink between Chip and.
50:48Mgus and we are, as we speak,
50:51trying to sequence all our samples for that.
50:55It's hard to know whether there is
50:57an overlap of the mutations or not.
50:58I think we need to 1st see how many of them
51:00do have chip and then we try to understand.
51:03We worked with Dan Lando where we took
51:05some of our chip samples from myeloma and
51:07we did the single cell sequencing data,
51:09but most of the chip mutations were
51:11in the myeloid lineage and not
51:13in the lymphoid lineage.
51:14But that brings up the
51:16lymphoid chip question.
51:17And again until we have more
51:18data we don't know the answer
51:20but it's a great question.
51:22We have another question from American
51:24Idol and I think this highlights
51:27how important is it is that we
51:29think mechanism and disease
51:30agnostic and across specialties.
51:32So Amir is of course loving you talk.
51:34And then right we have similar similar
51:37issues in chips because MB spectrum in terms
51:42of difficulties of response assessment.
51:44And So what do you think the primary
51:47endpoint of early phase trial for high risk
51:50smoldering myeloma should be the great?
51:52Question, because if we wait
51:54for progression to myeloma,
51:55especially if you treat them in the
51:57observation arm with Rev depth,
51:58you're wait for another 1520 years.
52:01So we do have a meeting with the FDA,
52:03which actually is in Madrid to ask those
52:06questions. What are the endpoints?
52:07Can we get accelerated endpoints?
52:09Can we look at response, can we look at RT?
52:12Can we consider pure as a sustained MRD
52:15negative disease for four to five years?
52:17These are all great questions that
52:19we need answers to be able to design
52:21for this property. Yes.
52:22Let me maybe go back then to the
52:24interplay between the immune
52:26system and your clone.
52:27So do you expect that if you
52:29get rid of the malignant clone,
52:31however small, that it would have
52:33an effect on the immune system?
52:36Oh, I don't know.
52:37That's a great question.
52:38Will it normalize, right?
52:39I mean, if you look at the therapy
52:41we gave to those patients and
52:42when they were MRD negative,
52:43they normalized their immune system.
52:45But the other question is
52:47which one started first?
52:48Is it the chicken and the egg?
52:49And was it already an immune
52:50dysregulation that led to those clones?
52:52Growing.
52:52And is that already there even
52:54when you get rid of the MRI of the
52:57clone that years and years later
52:59yet another mutation will occur
53:01because the soil is fertile, right?
53:04So I don't know.
53:05And I'd love to get samples,
53:07for example,
53:07from patients before they
53:09develop mgus so that we know
53:11which one happens first.
53:13But these are all great questions
53:15that we would love to collaborate
53:16with people and answer them together.
53:22Awesome. We have a little more Natalia.
53:24Any questions from your team?
53:27Yeah, I mean, I think, uh, perhaps, uh,
53:29to answer amers question and perhaps a,
53:33an immune endpoint should be a
53:36potential secondary endpoint,
53:38how to normalize that
53:41immunosuppressive environment,
53:42you know what potential interventional
53:44strategies like whether it's
53:46nutritional or microbiome or
53:48metabolomic strategies that could be,
53:50I don't think we pay enough attention
53:52to weight loss interventions
53:54or exercise interventions in
53:56myeloma and there's so much.
53:57Data you made parallels Irene with
53:59breast cancer and there's so much
54:01commonality between the diseases,
54:03the role of inflammation,
54:04the obesity etcetera.
54:05So I I don't think we pay enough
54:08attention to those kind of
54:09interventions in myeloma prevention
54:11and even relapse prevention once
54:13you have successfully treated them.
54:15Your thoughts on that?
54:18Absolutely. And I think you and Betsy
54:20O'Donnell would really, you know,
54:21talk for hours because we're even
54:23thinking should we use some of
54:25those new obesity drugs, right?
54:26Like, there are so many things that we
54:28can do to prevent progression and some
54:30of them may be in our hands right now.
54:35Yeah, excellent.
54:38So we're getting close to to running
54:41clock and I don't see additional
54:45questions. Um, well, I'm Erin,
54:49thank you so much for this really
54:51spectacular grand rounds and
54:53congratulations on these amazing
54:55advances that are clearly, you know,
54:58advancing prevention which is so amazing
55:00for many patients and then treatment.
55:03So thank you. Thank you for sticking
55:05through you know with the zoom only option.
55:08And we look forward to you know,
55:10getting together in person
55:12and collaborating for sure.
55:14Absolutely. Thank you again and
55:15definitely look forward to seeing you.
55:17Not in person, but this was a
55:19good alternative. Fantastic
55:21talk, Harry. Thank you so much.
55:23Thank you, everyone.