The PROMISE of Early Detection and Interception in Myeloma

Transcript

• 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.