Chronic Lymphocytic Leukemia: Genotype to Phenotypes and Beyond

Transcript

• 00:00Good morning. So for those of you who
• 00:04either can't see me or don't know me,
• 00:07I'm Eric Weiner and I'm really pleased
• 00:10to be here to introduce Kathy Wu.
• 00:14This is the inaugural lecture of what we
• 00:19hope will be a new series and many of us
• 00:23in the Cancer Center spent a lot of time
• 00:26thinking about how we want to do conferences.
• 00:28And we looked at attendance and we looked
• 00:31at who goes to what and ultimately came
• 00:34to the decision that Grand runs as it was,
• 00:37which is now trying to be in
• 00:39person as much as possible,
• 00:42was largely attended by clinically
• 00:43oriented people in population,
• 00:45scientists and people who are otherwise
• 00:48looking for lunch and or breakfast.
• 00:50And and that there was really a need for
• 00:54a conference that focused a bit more
• 00:57on translational and basic questions.
• 01:01And so after some thought,
• 01:03a small committee of people that
• 01:06included Katie Politi and Megan King
• 01:09came up with the idea of trying a
• 01:12conference like this on a monthly basis.
• 01:14And this is the first of those.
• 01:17So I'm really pleased to have Kathy Wu here.
• 01:20I've known Kathy for many years.
• 01:23She was a fellow at Dana Farber and of
• 01:27course it's still with Dana Farber when
• 01:31I was a substantially younger attending.
• 01:35And in fact we worked together in clinic,
• 01:39yes, briefly.
• 01:40She dabbled a little bit in seeing a
• 01:44patient with breast cancer or one or two.
• 01:47And so I've known Kathy now for 20
• 01:51plus years and Kathy has built really
• 01:54a phenomenal career at at Dana Farber.
• 01:57Her own interests are broad.
• 02:00I learned last night something
• 02:02I didn't know before,
• 02:03which is that she even had an
• 02:05interest in sickle cell disease and
• 02:08therapeutic approaches to sickle
• 02:10cell disease way back when,
• 02:12but ultimately decided that some
• 02:14some amount of focus was needed.
• 02:17And her interests have really
• 02:19focused on immunotherapy and Col.
• 02:21and and beyond that,
• 02:24the development of vaccines and
• 02:27and tumor specific vaccines.
• 02:31She is presently a Professor of
• 02:33Medicine at Harvard Medical School
• 02:35and the chief of the Division of
• 02:38Let Me See If I Get This Right,
• 02:40Stem cell transplantation and
• 02:42cellular therapies at the the
• 02:45Dana Farber Cancer Institute.
• 02:47So it's really a pleasure to have you here.
• 02:51We're all looking forward to
• 02:53your talk on largely on CLL.
• 02:56And thanks so much for coming.
• 02:58We had I will,
• 02:59I will just say that a small group of us
• 03:01had a great dinner with Kathy last night.
• 03:04And in addition to being a great scientist,
• 03:08she's also just a delightful
• 03:09person to have dinner with.
• 03:19Well, it's really an honor to be here
• 03:22and and happy New Year, Happy Snow day.
• 03:26Thank you everyone in the
• 03:28room for trudging in this.
• 03:30It's really great to see you in person
• 03:34and and also to all the folks out in Zoom.
• 03:37I hope this is a successful series
• 03:39because I do think that the intersection
• 03:41between the clinical and the basic
• 03:43and really kind of being able to look
• 03:46at the translational opportunities
• 03:48that are afforded by the patients that
• 03:50we treat in the study are are are
• 03:52immense and so and very rewarding.
• 03:54So and as as Eric said I I do have many,
• 03:58many different different interests.
• 04:00I think that's a hallmark of a of a happy MD.
• 04:04So like we we're interested in a
• 04:06lot of things and and thank you for
• 04:08giving me the opportunity to maybe
• 04:10share some of the work that we've
• 04:11been doing in CLL Genomics. OK.
• 04:15So we'll start.
• 04:17Let's see
• 04:27here we go. Disclosure slide,
• 04:30OK, I thought I'd start here,
• 04:31which is you know I think just a a
• 04:34challenge to all of us in the cancer
• 04:37community whether or not we study
• 04:38CLL or not is really the challenge of
• 04:41tumor heterogeneity and evolution.
• 04:42This has really been kind of
• 04:45understood for quite some time now,
• 04:47made ever more clear through all the
• 04:49genomic studies that have been out there.
• 04:51But we know for sure that cancer
• 04:54is a heterogeneous population,
• 04:56for better or for worse.
• 04:57Unfortunately,
• 04:58by the time that we are diagnosing
• 05:00patients with cancer,
• 05:01we're really here at the time of
• 05:03escape where there's already so many
• 05:06different resistance mechanisms
• 05:07that have really come into play
• 05:09that make the tumor fit to expand
• 05:11and grow in the patient host.
• 05:14We also increasingly know that
• 05:15this is not happening in a vacuum,
• 05:17that there's an interaction
• 05:18with the host immune system.
• 05:20But again,
• 05:21by the time that we're seeing patients,
• 05:22there's so many different immune based
• 05:25escape mechanisms that are at play as well.
• 05:27And so a lot of the questions that
• 05:29I think as a field that we're
• 05:31really interested in asking is
• 05:33not only this question of tumor
• 05:35heterogeneity and evolution,
• 05:36but also how do we understand this,
• 05:38these heterogeneous tumor
• 05:40microenvironments are T cells there
• 05:42at the right place at the right time?
• 05:45How are we responding to
• 05:47diverse immunotherapies?
• 05:47And then what is the role of a tumor
• 05:50antigen in shaping the tumor response?
• 05:52I'm not going to talk today so
• 05:54much until the very, very end,
• 05:55but this is a very large area
• 05:57of interest in my group.
• 05:59And as I said,
• 05:59I'm going to focus on chronic
• 06:00lymphocytic leukemia,
• 06:01which honestly the questions that I'm
• 06:05asking could be in any sort of tumor system.
• 06:08But CLL really has a lot of very
• 06:10unique features about the disease
• 06:12that have made it exceptional
• 06:13for the study of genomics.
• 06:15First,
• 06:17in a small tube of blood you have
• 06:19very pure tumor that can is readily
• 06:21accessible directly from the patients.
• 06:23The other thing is for a cancer,
• 06:25it's quite indolent.
• 06:26And So what that means is that
• 06:29we really have really long
• 06:30disease histories of patients.
• 06:32We can really take snapshots in
• 06:35time and study evolution in real
• 06:38time along with the patient.
• 06:40And so for some time now,
• 06:42our group together with colleagues
• 06:43in the Boston area,
• 06:44we've actually had this program
• 06:46where we've been trying to study
• 06:47the link from genome to phenome.
• 06:49How can we genomically characterize CLL,
• 06:52how can we understand the clinical
• 06:54course in response to therapy and
• 06:56then how can we also functionally
• 06:58characterize the pathway dependencies and
• 06:59really thinking about how we can do better.
• 07:02So what I'm going to talk about today
• 07:05is update the group on recent genomic
• 07:08studies and CLL driver discovery bid
• 07:11on our efforts in looking at tumor
• 07:14heterogeneity in our CLL GEM models.
• 07:16And then just a few perspectives
• 07:18of where we're going
• 07:20next in terms of the genomics.
• 07:21Again, as I said this is a
• 07:24very in general for cancer an
• 07:27indolent disease, it's typically
• 07:31marked initially by what
• 07:33we call watch and wait.
• 07:34So there can be a long lead time,
• 07:37but ultimately with treatment there can
• 07:40be cycles of recurrence that happen
• 07:43with shorter and shorter intervals,
• 07:45much like what we see in other tumors.
• 07:46I think a question that has
• 07:49always fascinated people in this
• 07:50field is how do we understand
• 07:53who progresses faster or slower?
• 07:54And what I mean by that is that
• 07:56there are some patients who succumb
• 07:58to their disease within two years.
• 08:00There are others that can have a
• 08:02little bit of therapy here and there
• 08:03go on for more than 1015 years.
• 08:05So why is that?
• 08:06What are the differences between
• 08:08the patients despite all their
• 08:10cells looking relatively similar
• 08:12under the microscope?
• 08:13And so for since forever there
• 08:15has been a long effort to try to
• 08:19understand those markers that we could
• 08:21use to distinguish amongst patients
• 08:24initially looking at clinical features,
• 08:26protein markers.
• 08:26But I would say over the past 10-15 years
• 08:28since there's been the next generation
• 08:30sequencing that's been available to us,
• 08:32there's really been an explosion of
• 08:34knowledge in terms of the genetic
• 08:36alterations later on top of that,
• 08:38the transcriptional alterations and
• 08:39even the epigenetic alterations so
• 08:42that we can understand what's going on.
• 08:44This slide really summarizes a lot
• 08:46of work that has been done since
• 08:49next generation sequencing has come upon us.
• 08:51I would say that the first studies
• 08:54in genomics arrived around 2010,
• 08:582011.
• 08:59We were among the first
• 09:02to describe mutated SF3B1.
• 09:04So a splicing factor that kind
• 09:05of came out of the sequencing.
• 09:07No one had until then kind of
• 09:10puts altered splicing and and
• 09:12lymphoid malignancies together.
• 09:14There's been large scale studies
• 09:17in looking at clonal evolution.
• 09:19So again CLL was one of the first
• 09:22places that studied really this kind of
• 09:25concept of clonally evolving subpopulations.
• 09:28And then and you can see initially our
• 09:32studies were about 100 patients and then
• 09:35around 2015 about 500 patients per cohort.
• 09:38What I'm going to describe for you
• 09:40now is our recent work trying to
• 09:41put together all of these different
• 09:43studies together so that we could
• 09:45get a cohort of more than 1000.
• 09:47I want to say that during this
• 09:49time that we've kind of performed
• 09:52these sort of genomic studies,
• 09:53there has been vast changes in the
• 09:56therapeutic landscape of CLL therapy.
• 09:58So whereas previously it was very
• 10:04standard to get chemo immunotherapy.
• 10:06I would say that in the in the same
• 10:08time that time frame that I'm speaking
• 10:10there has been the introduction
• 10:11of targeted inhibitors of BCL,
• 10:13two of the B cell receptor signaling
• 10:18and also introduction of immunotherapy.
• 10:20So the really big changes,
• 10:22you know as we start to think
• 10:24about the the genomic lesions.
• 10:26So how do we build an integrative CLL map?
• 10:30Well,
• 10:30we joined forces between our
• 10:33colleagues in North America but
• 10:35also with our colleagues in Spain
• 10:37and Germany and together collected
• 10:41cases for which there was exomes,
• 10:43genomes, RNA sequencing
• 10:45and methylation profiling.
• 10:46And there was a nice overlap of these
• 10:50different platforms in in several hundreds
• 10:52of patients samples that we collected.
• 10:55And this is a kind of a
• 10:59intimidating commute plot,
• 11:00but I think it just speaks of
• 11:02a number of different things.
• 11:04First, I want to acknowledge the young
• 11:06people who were the leaders of this project.
• 11:09It was really an international collaboration.
• 11:11So I had the pleasure of working with
• 11:14Binyamin Nisbacher and Ziao Lin and
• 11:17Gaddy Goetz's group computational gurus
• 11:19and then Cindy Hahn from Dana Farber.
• 11:22Awesome lymphoma oriented fellow
• 11:25and then Ferran,
• 11:27Nadeau and Marty from the group in Spain,
• 11:31the Spanish CLL group in Barcelona,
• 11:33Barcelona.
• 11:34And then when we looked at these
• 11:36more than 1000 patient samples,
• 11:38in fact we were able to have
• 11:41greater sensitivity.
• 11:42In the magenta are all the new
• 11:45drivers that we identified.
• 11:46So each row is a driver alteration,
• 11:50each column is a different case and
• 11:53what you can see is in fact there is
• 11:55a a list of recurrent alterations,
• 11:57but a long tail.
• 11:59You can see that a lot of our discovery
• 12:01is down here at the one 1% or less level.
• 12:04So many,
• 12:05many different sort of driver
• 12:08alterations that we had greater
• 12:11sensitivity to identify because of
• 12:14the increased power of our cohort.
• 12:16Just to make a a really beautiful
• 12:19Long story short,
• 12:21we were able to double the number
• 12:23of CLL drivers that we were able
• 12:26to identify previously.
• 12:27There were about 10% of patients
• 12:29that we couldn't account for.
• 12:31There wasn't any sort of driver
• 12:33alteration that we could point
• 12:34to that was this is the reason
• 12:36that they have CLL and we've been
• 12:37able to close that gap so that
• 12:39there's only by now 3.8% that we
• 12:42can't account for the two large
• 12:44categories of CLL that are well
• 12:47known in the clinical arena on the
• 12:51basis of their immunoglobulin locus,
• 12:53the mutated and unmutated IGHV.
• 12:56We finally had enough power to
• 12:58actually break those two groups
• 13:00apart and look and look at them
• 13:02separately and they really look
• 13:04like very different diseases.
• 13:05They each have distinct molecular landscapes.
• 13:08It highlights the diverse
• 13:10trajectories of clonal evolution.
• 13:12So maybe by virtue of where
• 13:13you start as AB cell,
• 13:15maybe there's a path of different
• 13:16paths of least resistance that gets
• 13:18you to where you're going to be.
• 13:19And what was super interesting is
• 13:21that at least for the unmutated CLLS,
• 13:24their their source of heterogeneity
• 13:26was genetic.
• 13:27There was a lot of lot more putative
• 13:29drivers in this unmutated group,
• 13:31but in the mutated group,
• 13:34relatively few drivers,
• 13:35but a lot of transcriptional diversity.
• 13:37So really a different path to
• 13:39achieving that type of heterogeneity.
• 13:42And then what I want to show you is
• 13:45that when we looked at the expression,
• 13:47you know,
• 13:48Benjamin was able to identify what
• 13:50he called E CS expression clusters.
• 13:53And then the nomenclature here is some
• 13:54of them were enriched for M for mutated,
• 13:56some for unmutated.
• 13:57And what you can see is that it
• 14:01actually breaks down the group's
• 14:02more or less based on mutated on
• 14:05mutator or by their epigenetics.
• 14:07But you can also see by the fact
• 14:09that there's two colors within each
• 14:10column that there was contribution
• 14:12from both mutated and unmutated to
• 14:14these different expression clusters.
• 14:15And one example in one one.
• 14:18One thing that was really interesting
• 14:19is that by the yellow asterisks we could
• 14:22see that certain genetic alterations
• 14:24actually also segregated together
• 14:25with these expression clusters,
• 14:27suggesting that they were a
• 14:29cohesive entity each of these
• 14:31different expression cluster group.
• 14:32So for example trisomy 12,
• 14:34which is a very well known cytogenetic
• 14:37abnormality associated with CLL,
• 14:39but for which there's great
• 14:41heterogeneity in kind of the
• 14:43behavior of those trisomy twelves.
• 14:46They actually split out into two groups,
• 14:48one that's in a more
• 14:50predominantly unmutated group,
• 14:51another in a predominantly mutated group.
• 14:53And this maybe provides us with
• 14:56some understanding for why some
• 14:58samples with the same sort of
• 15:00cytogenetics might behave differently.
• 15:02And what was super interesting is when
• 15:04when Benjamin started to look at these
• 15:06different expression cluster groups,
• 15:07they actually did display
• 15:09different clinical outcome because
• 15:11we had very long clinical.
• 15:13These were also clinically
• 15:15annotated samples as well.
• 15:17And this is just kind of the
• 15:18final data slide related to this,
• 15:19which is indeed when we kind of
• 15:22breakdown the samples based on
• 15:24their classical clinical group,
• 15:25based on the expression clusters,
• 15:27whether they were concordant or
• 15:29discordant to that classification,
• 15:31we could actually see differences in
• 15:34their clinical outcomes suggesting
• 15:36that our expression cluster system
• 15:38was actually increasing the accuracy
• 15:40of what we're trying to do in
• 15:43terms of prognostication.
• 15:44So we've been really excited to,
• 15:46I mean this is really,
• 15:47this was really a Tour de force
• 15:49effort to bring together not
• 15:50only all these different groups
• 15:52together and their expertise,
• 15:53but also to layer on all of these
• 15:57different genomic layers to kind of
• 15:59identify unique molecular subtypes.
• 16:01And I do want to say that this,
• 16:04these studies were samples that were
• 16:06collected in the era of chemo immunotherapy.
• 16:09We are actively trying to look now
• 16:12how these relate to the modern era
• 16:15of targeted inhibition and we also
• 16:17are interested in in trying to look
• 16:19at whether or not the different
• 16:20molecular subtypes have differences
• 16:22in therapeutic vulnerabilities.
• 16:26Now I think you know as we've gotten
• 16:28better with our therapies we we
• 16:30always have to kind of reckon what
• 16:31is the area of most unmet need.
• 16:33And I think right now clinically
• 16:35for the for CLL there are so many
• 16:38different therapies available,
• 16:39but we are still really faced with
• 16:41the conundrum of Richter syndrome.
• 16:43This is really it's a rare,
• 16:46it occurs in five to 10% of patients
• 16:49with CLL but it is a transformation
• 16:52of a small indolent histological type
• 16:55into a high grade lymphoid malignancy.
• 16:5890% have Histology similar
• 17:00to diffuse large B cell,
• 17:03large B cell lymphoma.
• 17:05The majority are clonally unrelated.
• 17:08We know that because if we
• 17:10follow their immunoglobulin,
• 17:11the clonal immunoglobulin,
• 17:11we could see the same in the patient.
• 17:14Shown here is a micrograph that shows a
• 17:18sample where you can see the coexistence
• 17:20of these two entities within the same
• 17:22sample and you can see the really the
• 17:24big kind of histological differences.
• 17:26These are the patients that we typically say.
• 17:28I'm so sorry. Please get your affairs
• 17:30and orders that there's really not
• 17:32much more that we can do for you.
• 17:34And it's been very difficult to
• 17:37understand molecularly much about this
• 17:39entity because there's been limitations
• 17:41of tissue sampling and and and it's
• 17:44really based on morphologic diagnosis.
• 17:46There's been a lack of markers and
• 17:49understanding of genetics and for
• 17:50a blood based malignancy like CLL,
• 17:52Richter's is really like a solid tumor.
• 17:54I mean, this is really so unlike
• 17:57what I said before where there's ease
• 17:59in kind of having blood draws here.
• 18:02We have to get biopsies often
• 18:04FFP specimens in order to study.
• 18:06And and this has not been, not been easy.
• 18:10But I would say that over the past couple
• 18:13years that because of the availability
• 18:15of all these nice genomic platforms,
• 18:18there's there's been really an
• 18:19explosion of new studies that have
• 18:22come out in the past year and a half.
• 18:24And at the same time there's been
• 18:26modeling that's been done trying
• 18:28to really put our attention to
• 18:30how we can generate mouse models,
• 18:32whether they're PDXS or or Gem
• 18:34models to try to understand this.
• 18:35And there's been actually a lot of
• 18:38progress in understanding the genome
• 18:39that the genetics looking at the
• 18:42epigenetics and the transcriptomics.
• 18:43And So what I'm going to demonstrate
• 18:45for you in the next couple slides
• 18:47is some of our efforts in this area.
• 18:49This is really work that's been that
• 18:51was led by Aaron Perry who is now
• 18:54a new junior faculty member at the
• 18:56Dana Farber in the lymphoma group,
• 18:58Roman Guiz who's part of Philo back
• 19:01in in France and Ignot Lechner who is
• 19:05now a junior faculty member at BU.
• 19:07And what we tried to do was assemble
• 19:10a nice paired matched cohort.
• 19:12So in other words,
• 19:14not just Richter samples in isolation
• 19:16but antecedent CLL matched together
• 19:18with the Richter's where we could track
• 19:21evolution in time across these patients.
• 19:23This was about 50 patients
• 19:24that we collected samples on.
• 19:26I think the point of emphasis that
• 19:28I want to show you on the left
• 19:29side here is the CLL course,
• 19:31the green is the different lines of therapy.
• 19:33On the right side is the Richter's
• 19:35and I want to show you that on the
• 19:37left side it's years where whereas
• 19:38on the right side it's months.
• 19:40So this kind of gives you a sense of kind
• 19:42of the the time course of these patients.
• 19:44The black dots are the different samples
• 19:46that we collected on the CLL course.
• 19:48The yellow here is the Richter
• 19:50diagnostic sample.
• 19:51Unfortunately,
• 19:51there's a lot of red here,
• 19:53which is that the patients
• 19:55did succumb to their disease.
• 19:56There's a number here with
• 19:58black arrows that are living.
• 19:59For the most part,
• 20:01these are patients who.
• 20:02We received therapy and then went
• 20:03on to stem cell transplant and
• 20:05really did a complete overhaul.
• 20:08So we we obtained eggsomes on most of
• 20:11these patients also had some genomes,
• 20:13RNA sequencing and single
• 20:15cell sequencing data.
• 20:16But I want to point out to you
• 20:17that you know a lot of these
• 20:19studies are really quite different.
• 20:20I think that the the conundrum that
• 20:22we've met with Richter's is that it's
• 20:25really two malignancies in the same sample.
• 20:27So how do you pull apart the genomic
• 20:30contributions of one versus the other.
• 20:33And for that we had a come up
• 20:36with a computational approach
• 20:37that was quite challenging,
• 20:39but we were able to succeed where
• 20:42we really optimize the copy number
• 20:44analysis to deal with FFPE artifact.
• 20:47We had a number of different filters
• 20:50that allowed us to kind of increase
• 20:53the sensitivity of our analysis
• 20:54and deal with contamination of
• 20:56whether tumor in the normal or
• 21:02the reverse.
• 21:03As I said the artifact from FFPE.
• 21:05And then we were able to put in our
• 21:08algorithms that allow us to identify
• 21:09clones and then also establish phylogeny.
• 21:12So at the end of the day,
• 21:14we were able to separate out the
• 21:16contributions of the CLL clones
• 21:18compared to the Richter's clones.
• 21:19And in doing so then we could look
• 21:21at start to look at phylogeny and
• 21:23understand which branches were CLL
• 21:25versus Richter's and look across time.
• 21:27So again, Long story short,
• 21:29I think one of the questions that has been
• 21:31asked in the field is it is Richter's,
• 21:33is it a distinct entity,
• 21:35is it similar or is it different
• 21:38from the Novo DLBCL?
• 21:40And here we had the advantage of being
• 21:42able to access older data of more
• 21:45than 300 samples of lymphoma that our
• 21:47colleague market ship had collected.
• 21:50And then using those data we
• 21:55performed unbiased NMF clustering.
• 21:57And you can see across the purple
• 21:59on the top that the Richter's
• 22:02really stand different.
• 22:03They're you know separately
• 22:06from DLBCL and so the the,
• 22:11so this is clonally unrelated Richter.
• 22:14So these are the few samples here
• 22:17do appear to be like de Novo DLBCL,
• 22:21but the vast majority,
• 22:23the clonally related stand separately
• 22:28among the Richter's itself.
• 22:29We were also because of all the
• 22:31genomic alterations that we found we
• 22:33were able to also perform unbiased
• 22:36clustering and discern that there's
• 22:38actually it appears to be molecular
• 22:41subtypes within Richter's itself
• 22:44and these TP 53 has long been
• 22:47associated with Richter's but we can
• 22:49see that there's different flavors.
• 22:50So this one here has enrichment in
• 22:54whole genome doubling this group.
• 22:57Here RS3 has Co occurrence with
• 23:00Notch one also deletion 15 Q which
• 23:04covers MGA which is effects Mick
• 23:09and then RS5 also has Notch one
• 23:13as well wild type Notch one and a
• 23:17lot of copy number alterations.
• 23:18There were also two other
• 23:19subtypes that did not have
• 23:23TP53K Ras S Pen, Notch one together with
• 23:26Trisomy 12 and also SF3B1 with EGR Two.
• 23:29And again these different subgroups
• 23:31appear to have different clinical
• 23:33behavior where the ones that have
• 23:35TP 53 seem to have worse prognosis.
• 23:38Now what is the meaning of kind
• 23:39of trying to look at all these
• 23:41different genomic alterations?
• 23:42Well one thing we realized is that
• 23:45maybe we could harness all of this
• 23:47and actually look to see this,
• 23:49whether this could help us devise a non
• 23:52invasive approach to identifying Richter's
• 23:54and getting us to earlier detection.
• 23:58And it turns out that with simply ultra
• 24:01low pass genome sequencing $150.00 a pop,
• 24:05you can focus on these different alterations
• 24:08that we identified and start to look.
• 24:11And in fact we were able
• 24:12to see in this example,
• 24:14this is a patient where we could
• 24:16identify the Richter's alterations
• 24:18even close to five to six months
• 24:20before the actual diagnosis.
• 24:22So if you follow this in the blood,
• 24:24the blood cells have CLL at this
• 24:26time early on and it's a very,
• 24:28very quiet genomic profile.
• 24:32Whereas the plasma shows all of
• 24:34these different alterations that
• 24:36match very similarly to what was
• 24:39detected much later when the actual
• 24:41the the tissue diagnosis was made.
• 24:43We've been able to see that in a
• 24:45number of different other cases.
• 24:47This is a nut.
• 24:48Whoopsie, this is another case.
• 24:51Well anyway,
• 24:52let's see
• 24:55where the in the plasma we were able
• 25:00to again follow find those kind of
• 25:03Richter's genomic alterations that
• 25:04was not evident in the blood cells.
• 25:08And finally, this is a case of a
• 25:10patient who went through transplant and
• 25:15we were able to identify post transplant
• 25:18relapse months before the actual diagnosis
• 25:21and then institute therapy and you
• 25:23see those alterations go away again.
• 25:26So I think just to summarize
• 25:27this part of the talk,
• 25:28I I would say that we've been able to
• 25:31actually find that the majority of
• 25:34Richter's does evolve from CLS subclones
• 25:36through acquisition of additional drivers.
• 25:38Clonally related Richter's is
• 25:40distinct from de Novo DLBCL.
• 25:43There are molecular subtypes
• 25:46of Richter's that have and and
• 25:48these different subcategories do
• 25:50have prognostic significance.
• 25:52And then the we're very excited about
• 25:54the self free DNA as a way to get us
• 25:57to non invasive earlier diagnosis
• 25:58because I think this could be really
• 26:01quite impactful for our patients.
• 26:05I think we're always trying to.
• 26:06So I'm going to transition now in
• 26:10terms of talking about the immune
• 26:13microenvironment for Richter's.
• 26:14You know,
• 26:15I think we're always trying to gain
• 26:17a bird's eye view of the landscape
• 26:19and really the advent of single
• 26:21cell analysis has really been
• 26:23so impactful all around.
• 26:25This is something I put together with
• 26:27one of my postdoctoral fellows where
• 26:29we tried to look at across the field.
• 26:31You know single cell sequencing was
• 26:34named the method of the year in 2013
• 26:36and then subsequently 2019 in multi
• 26:38ohmic analysis was the method of the year.
• 26:41CLL has had a bit of a lag time in
• 26:43terms of the the rest of the field,
• 26:45but again the easy access to material
• 26:48has really kind of stimulated us to
• 26:50start to look a little bit more closely.
• 26:53We've been able to apply this approach.
• 26:55Again I mentioned that Richter's
• 26:57is this area where the therapeutic
• 27:01opportunities are not great,
• 27:05but what has caught the attention of
• 27:08many is that it turns out that there is
• 27:11a response to immune checkpoint blockade.
• 27:13So fit 42 to 65% responses to
• 27:16PD1 blockade in Richter's.
• 27:18This is really quite remarkable because
• 27:20a lot of blood B cell malignancies
• 27:23do not have a great response to
• 27:25to PD anti PD one and so this sort
• 27:30of across these many studies.
• 27:32This raises the question are
• 27:33there determinants of response
• 27:35and resistance to PD1 blockade.
• 27:36We were able to partner together
• 27:38with our colleagues at MD Anderson.
• 27:41Again this is the work of Aaron
• 27:43Perry where they had already started
• 27:45a trial where they had patients
• 27:48initially on nivolumab and then then
• 27:50after the first cycle then ibrutinib
• 27:52was started and then response
• 27:54assessment happened at three months.
• 27:57And so we were able to collect bone
• 27:59marrow samples from these patients,
• 28:01a number in the green that had either
• 28:03a partial or complete response to
• 28:06patients that had progression even
• 28:07at the three month time point.
• 28:09And then just for comparison to CLL,
• 28:12patients were treated on the same
• 28:14trial and what Erin did was she was
• 28:16able to take marrow samples from
• 28:18these patients and through flow
• 28:20cytometry you can see that the
• 28:24the small cells were the CLL cells,
• 28:25the large cells were the Richter's
• 28:27and then there was another
• 28:29population here which was neither
• 28:31and this was the immune cells
• 28:33that were in the bone marrow.
• 28:34And then she was able to
• 28:36perform a single cell
• 28:39characterization.
• 28:40And again to summarize a large body of work,
• 28:43what was really clear is that the
• 28:45responders compared to the non
• 28:47responders when you started to look
• 28:48at all of those single cell transcriptomes,
• 28:51those there was a kind of a cluster
• 28:53of cells that kind of segregated
• 28:54with a unique phenotype and
• 28:56we called this cluster one.
• 28:58It turns out it was high
• 29:01expression for a transcriptional
• 29:03factor called Hobbit ZNF 683.
• 29:05And as she started to look
• 29:07at this population,
• 29:09she was able to perform some functional
• 29:11studies and demonstrate through cut
• 29:13and cut and run and various various
• 29:16different sort of over expression and
• 29:18knockout kind of analysis that ZNF
• 29:21683 does appear to regulate T cell
• 29:24pathways with activation cytotoxicity.
• 29:26When we started to look at the
• 29:29trajectories the ZNF 683 high
• 29:30seemed to be a divergent pathway
• 29:32from terminal exhaustion.
• 29:34We also looked across other different
• 29:37solid tumor till settings and it turns
• 29:40out that the ZNF 683 high does mark
• 29:43a population that's of patients that
• 29:45have better response to PD one therapy.
• 29:48Notably we looked at Melanoma
• 29:50across and other settings and also
• 29:53in she was also able to see that
• 29:56you know we did our analysis in
• 30:00the marrow but to make it more
• 30:03clinically facile could could this
• 30:05be actually detected in the blood.
• 30:07And so she was able to look at independent
• 30:09patients who are responders or non
• 30:11responders on the MD Anderson trial.
• 30:13And in fact the responders have a
• 30:16very distinct profile in the blood T
• 30:18cells compared to the non responders
• 30:20where there is high expression of
• 30:22Z and F683 and and other cluster
• 30:25one genes as well and this is we.
• 30:31So we were very proud of Aaron and
• 30:34Camila to get this into cancer cell.
• 30:36We actually tried to for a cover.
• 30:41It did not work.
• 30:42So you will never see this published
• 30:44only here in the seminar series.
• 30:47But we were trying to make a play on
• 30:50ZNF 683 and The Hobbit and the idea
• 30:53that if those of you were Middle
• 30:56Earth aficionados or token lovers,
• 30:59you know,
• 31:00the idea that you can either take
• 31:04a path and get to the valley of
• 31:06death with exhaustion or you can
• 31:08take a divergent pathway and end
• 31:10up back in the Shire happy.
• 31:12So that was our idea. Didn't work.
• 31:15Whatever.
• 31:16So, so that.
• 31:18I'm going to move on to
• 31:20the second set of study,
• 31:22second chapter shall we say
• 31:24in trying to look at function.
• 31:27And here you know in the same
• 31:29way that in the,
• 31:30in the genetic realm we've been able
• 31:33to study heterogeneity in patients.
• 31:36Well,
• 31:37can we not actually generate mice
• 31:41that are actually faithful to the
• 31:44disease through the by mimicking
• 31:46some of these genetic alterations
• 31:48that we've identified And then
• 31:50that provides us a platform with
• 31:52studying mechanism of disease
• 31:54and testing novel therapies.
• 31:55And I just want to point out that
• 31:57there are different flavors of models.
• 32:00I I don't need to tell this audience
• 32:02or folks that yelled at, but
• 32:06the GEM models in general in,
• 32:07in particular I just want to point
• 32:09out have the advantage that this is
• 32:11kind of in a physiologic setting.
• 32:12It does allow us to look at tumor evolution
• 32:16and also immune micro environment analysis.
• 32:20And so for the past period of time,
• 32:23my group has really been
• 32:24interested in this question, well,
• 32:25how do you get from AB cell,
• 32:27what are the kind of pathway hits
• 32:29that happen that gets you to CLL?
• 32:31And can we study some of these alterations
• 32:34that we spent a lot of time genomically
• 32:36identifying such as SF3B1 or IK,
• 32:41CF3 or DMT3A and so and so forth and
• 32:45can we start to look at these things.
• 32:47So I won't go over these past studies
• 32:50only to say that it has in fact been
• 32:53very gratifying to generate these
• 32:55mouse models and to demonstrate that,
• 32:57yes, these putative drivers that
• 33:00we've identified through sequencing
• 33:03actually generate CLL in mice.
• 33:05Most recently we had a very nice
• 33:08study ELISA 10 Hacken generated
• 33:13the setting where using CRISPR she
• 33:15was able to introduce combinations
• 33:16of different alterations and release
• 33:19combinatorial study the different models
• 33:21of CLL and Richter's that we identified.
• 33:23But for today, I'm going to talk
• 33:25about new unpublished data where
• 33:27we've been focused on one of the
• 33:29newer drivers that we identified,
• 33:30RPS 15 and some of the insights
• 33:33that we've identified there.
• 33:35So RPS 15, what is it?
• 33:38It is a ribosomal protein.
• 33:42It's identified in 5% of CLL patients.
• 33:45It's enriched in patients who
• 33:48are relapsed following therapy.
• 33:51It's associated with shorter
• 33:53progression free survival and it
• 33:56commonly Co expresses with TP53.
• 33:58One of the things that we found
• 34:00interesting about RPS 15 is that there
• 34:03does seem to be a hotspot region
• 34:05where a lot of the alterations happen.
• 34:07And so this kind of piqued our interest
• 34:10in trying to learn more about RPS 15.
• 34:12I do want to put this in the context
• 34:14that they're across different cancers.
• 34:16There's been a lot of different ribosomal
• 34:19mutations that have been found for CLLR.
• 34:22PS15 is the only ribosomal
• 34:25mutation that's been identified.
• 34:27But certainly across other
• 34:29cancers including breast cancer,
• 34:30Melanoma, myeloma,
• 34:32you see that this biology seems to be there.
• 34:35And carbosomopathies have been
• 34:37associated with a variety of
• 34:39different altered functions,
• 34:41so including DNA damage,
• 34:45proteasomal alteration and metabolic
• 34:47rewiring.
• 34:47So we were interested in trying to dig
• 34:50a little bit deeper about this in CLL.
• 34:54So we used our,
• 34:56we used this in a similar fashion to
• 34:58the other mice that we've generated.
• 34:59We introduced one of these hotspot mutations
• 35:04that was intercross with CD19 cream mice.
• 35:07So this alteration is only present in B
• 35:09cells in the context of CD19 expression.
• 35:12So in B cells we were able to generate both
• 35:16heterozygous and homozygous mutated mice.
• 35:18We also intercross also with deletion 15,
• 35:22sorry TP 53,
• 35:24so that they were also mice that
• 35:27had double mutations as well.
• 35:31And so this is just a bit of
• 35:32the targeting strategy.
• 35:33This was really studies led by
• 35:35an MDPHD student and currently
• 35:38at MGH as a as an intern.
• 35:41And then Marwan Kwok is a awesome postdoc
• 35:44in my group right now who's leading
• 35:45up on some of the functional studies.
• 35:47Neil Ruthin is in grad Graduate
• 35:50School in the New York area
• 35:52for computational biology.
• 35:54So RPS 15 mutations,
• 35:55we we're very able through our mouse
• 35:58models to confirm that it does have
• 36:02oncogenic potential because certainly
• 36:04over time we're able to identify that
• 36:08there is a population of RPS 15 mice
• 36:12that are do have expanded B cells.
• 36:15You can see this also in
• 36:18screen sizes over time.
• 36:20It does take quite a bit of time
• 36:22consistent with the human disease.
• 36:23It does take about 15,
• 36:28about 818 months,
• 36:2918 to 218 months to two years
• 36:31in order to see disease.
• 36:33So this is really a labor of love.
• 36:36But I would say that for sure with
• 36:39the RPS 15 mutations mutant mice we
• 36:42do see onset of disease less so with
• 36:46just the TP single mutant TP 53 but
• 36:49with a double mutant we also see not
• 36:52only CLL but evidence of Richter's.
• 36:55But what was interesting is in the
• 36:57setting of hypo hyper proliferation
• 36:58when we look early on it seems to
• 37:01there seems to be hypoproliferation.
• 37:03So if we measure the B cell percentages
• 37:06in the homozygous mice in the
• 37:08setting of pre leukemia it's actually
• 37:10depressed compared to wild type.
• 37:12So what is going on?
• 37:14How is this kind of hypoproliferation
• 37:17turning into hyper?
• 37:18And so to kind of gain some clues,
• 37:20we really focused on these pre
• 37:22leukemic mice for which we collected
• 37:24B cells and started off by just
• 37:27looking at gene expression profiling.
• 37:29And it became quite evident that there
• 37:31was quite a few different altered
• 37:33pathways including cell cycle checkpoints,
• 37:36MIC targets, DNA repair.
• 37:38And looking close more closely,
• 37:40we could see that this was related
• 37:42to either reduction in proliferative
• 37:44capacity as well as there was increased
• 37:48G1 checkpoint activity after mitogenic
• 37:51stimulation and increased apoptosis.
• 37:53Now these alterations in in cell
• 37:58cycle could be due to cell stress.
• 38:01So we started to look at the question
• 38:04of whether or not there was changes in
• 38:07oxidative stress and in fact using a
• 38:10Mitosox assay in our homozygous mice,
• 38:13we do see evidence both at baseline
• 38:16and with stimulation that there
• 38:18is increased enhanced oxidative
• 38:20stress which is supported by the
• 38:22fact that when we use the inhibitor,
• 38:24so that pro oxidant we actually see
• 38:27that the RPS 15 mice are more sensitive
• 38:30to this inhibitor than the wild type.
• 38:34Now because of the cellular stress,
• 38:36does this actually can this actually
• 38:39support acquisition of genotoxic injury?
• 38:44And in this case,
• 38:46we were able to use gamma H2 AX and
• 38:48we see in the homozygous mice that
• 38:50there is increase in gamma H2AX.
• 38:53And as we started to,
• 38:55there's a lot of westerns that
• 38:57I could have shown you.
• 38:58But suffice it to say that through
• 39:01examination of the mutant mice,
• 39:03we do see impaired cell cycle
• 39:05checkpoint response to DNA damage,
• 39:07impaired response signaling,
• 39:08abrogation of ATM and CHECK 2
• 39:11signaling and heightened intrinsic
• 39:13aberrant DNA damage response.
• 39:19And Despite that, there's also
• 39:22increased proliferation signaling.
• 39:24So one of our highest hits in
• 39:27our gene expression was ZAP 70,
• 39:28which has relevance to CLL.
• 39:30So we see that here.
• 39:32And there's also enhanced ABCR signaling.
• 39:34So definitely a balance between
• 39:36different forces at play.
• 39:38Going on, our next question was that is
• 39:42seeing these different sort of phenotypes,
• 39:44since this is a ribosomal protein,
• 39:47is there actually alteration?
• 39:49Is there effects of mutant
• 39:52RPS 15 on translation?
• 39:54And so we asked could RPS 15
• 39:57mutation cause ribosomes to
• 39:58preferentially translate certain genes?
• 40:01Could the mutation cause ribosomes
• 40:03for example to stall at specific
• 40:06protein coding sequence motifs
• 40:08interrupting translation of certain
• 40:10genes or could it read through
• 40:12stop codons and then result in
• 40:14misfolded and degraded proteins?
• 40:16And so for this we performed
• 40:19A ribosomal profiling.
• 40:20And when we started to look at
• 40:23whether or not there was evidence of
• 40:25differential translation efficiency,
• 40:26there were certainly many genes that
• 40:28were appeared to be have enhanced or
• 40:32depressed translational efficiency.
• 40:34And as we started to look at the
• 40:37pathways that were impacted,
• 40:39these included many of those pathways
• 40:41that I already talked to you about
• 40:43in the pre leukemic setting.
• 40:44So cell cycle, MC target,
• 40:45cell cycle checkpoints and DNA replication.
• 40:49And specific examples that we could
• 40:51see were genes that are have very well
• 40:53known roles in these different pathways.
• 40:58We were able to support this this
• 41:01kind of ribosome Riboseek analysis
• 41:03by looking at protein expression
• 41:06and we can confirm that what we saw
• 41:10as as having depressed translation.
• 41:12So the GPX one we could actually
• 41:15confirm at the protein level for
• 41:19GPX 1 and O2O2 four and increase
• 41:24expression in PTP 4A2.
• 41:28So that was actually very nice to see
• 41:31that linkages between translation and
• 41:33the the pathways that we were impacting.
• 41:36When we started to look at,
• 41:41we were also able to see evidence
• 41:44not only in a in a murine cells
• 41:47but also in a human cell line.
• 41:50We saw evidence of stop codon stalling.
• 41:54So you can see kind of a pile
• 41:56up here in terms of the relative
• 41:59position to the stop codon,
• 42:01but we also saw evidence of
• 42:03stop codon read through.
• 42:05And so we do see that there's enrichment of
• 42:09certain codons in that kind of stop site,
• 42:13suggesting that this is not a random process,
• 42:16but there's actually motifs that
• 42:17are kind of guiding this process.
• 42:20And finally,
• 42:21as we started to look at the
• 42:23leukemic B cells,
• 42:24we could see up regulation of Mick targets.
• 42:28And I'm going to just skip over this,
• 42:30but only to say that as we
• 42:31start to go through our model
• 42:33of what we think is going on,
• 42:35we do see that in this mutated
• 42:38ribosomal protein that there is
• 42:41evidence of altered translation
• 42:43through a couple of different
• 42:45mechanisms that these do initially
• 42:47lead to hypoproliferation.
• 42:49There is elevated ZAP 70 and BCR
• 42:54signaling as well as make activation.
• 42:56But in initially there's P53
• 42:58mediated apoptosis and cell cycle
• 43:01checkpoint changes that are leading
• 43:03to that hyper proliferation,
• 43:05but that over time there's acquisition
• 43:07of DNA damage and genomic instability
• 43:09that tip the balance and get us to
• 43:11the state of hyper proliferation.
• 43:13So just to conclude this part of the talk,
• 43:16I'll just say that again our our new
• 43:19work suggests that RPS 15 mutation
• 43:22is ACL driver and reinforces the
• 43:24notion that CLL has these core
• 43:27pathways that are affected.
• 43:28So I didn't go into this,
• 43:30but across our different mouse models
• 43:34we are seeing common pathways through
• 43:37different mechanisms that appear to be
• 43:40involved and current ongoing work is
• 43:44starting to look at the immune micro
• 43:46environment so that we can start to
• 43:48link the genotype with whether or not
• 43:52they're related to distinct changes
• 43:54in the immune micro environment.
• 43:58In the final slides,
• 43:59I just want to say that you know I
• 44:03think that where we're going next
• 44:04in sort of sort of our studies,
• 44:06a lot of the CLO work until now I
• 44:09think across the field has been really
• 44:11focused on the blood easy access,
• 44:14lots of tumor there.
• 44:15But I think increasingly we do
• 44:17need to look at these specialized
• 44:19hematolymphoid organs where there is
• 44:21a specialized immune microenvironment
• 44:24that we can understand better.
• 44:26I think that there is a
• 44:29priority and interest in trying
• 44:31to go earlier in disease.
• 44:32So how can we understand those early events?
• 44:36How can we intervene early?
• 44:38How can we change Natural History?
• 44:40We're only going to get there by
• 44:42understanding a little bit more about this
• 44:45earlier time Multiomic profiling for sure.
• 44:47There's so much data out there and how
• 44:50can we link them all together and kind
• 44:52of not have them as separate entities,
• 44:55but really trying to coalesce
• 44:57into kind of archetypes that we
• 45:01can understand spatial analysis.
• 45:03So our group is actively working
• 45:05on efforts to try to look at the
• 45:08architecture of lymph nodes and
• 45:10bone marrow to see how malignant
• 45:14cells are organized and also in
• 45:17relationship to their genotype.
• 45:19So their mutations and do specific
• 45:22clones segregate with specific types
• 45:25of niches and and are they organized
• 45:29in certain type of neighborhoods.
• 45:31And finally I I touched upon with our
• 45:33self free DNA work some of the early
• 45:35detection I'm going to end with the
• 45:37last couple slides speaking about early
• 45:39intervention we hope in the future.
• 45:41But another big part of the work
• 45:44that my group does is think about
• 45:47cancer neo antigens.
• 45:48And from all the genomic studies
• 45:51that we've been doing,
• 45:53we've realized that there there
• 45:55is the opportunity for these
• 45:57mutations to generate epitopes that
• 45:59can be recognized by by T cells.
• 46:01I'm not going to go into this in
• 46:04great length only to say that there's
• 46:06straightforward algorithms by now
• 46:07that allow us to take start with the
• 46:10sequencing data and identify for us
• 46:13what those new antigens might be.
• 46:16I want to say that some of our earliest
• 46:18work in the new antigen field and
• 46:20kind of setting up these pipelines
• 46:21were in CLL because that is where
• 46:23we had the data and all the tools to
• 46:28help us construct some of the these
• 46:31first pipelines that were available.
• 46:33And certainly our vaccine neo antigen
• 46:38work that Doctor Weiner alluded to has
• 46:41taken our group very far afield from CLL.
• 46:44We've gone into the solid tumors and
• 46:47we've been able to conduct some early
• 46:49proof of concept studies that such
• 46:51an approach of starting with tumor
• 46:53looking for genomic alterations and
• 46:55generating a personal vaccine is feasible.
• 46:58But I've always been super interested
• 47:00in trying to bring it back to CLL.
• 47:02And so I'm happy to say that right
• 47:04now we have a phase one study for
• 47:08patients with unmutated IGHV led by
• 47:11ine on and supported by Matt Davids
• 47:14and Jennifer Brown to study and
• 47:18look at the impact of this vaccine
• 47:21alone vaccine together with low dose
• 47:24cyclophosphamide as a way to kind of
• 47:26alter the immune micro environment
• 47:28and maybe address T regs.
• 47:30And then also a third cohort to actually
• 47:33add immune checkpoint blockade together and
• 47:39we already have enrolled in a number
• 47:42the first three patients we're already
• 47:44seeing interesting immune responses
• 47:46compared to our solid tumor settings.
• 47:49These are patients who actively
• 47:50have circulating disease.
• 47:52So is it possible to even vaccinate and
• 47:55generate meaningful responses when there's
• 47:58leukemia that's that's in circulation?
• 48:00And the the short answer,
• 48:01it seems like yes.
• 48:02So we're we we are actually seeing
• 48:04very nice brisk immune responses
• 48:06to actually some of our patients.
• 48:08So I hope you stay tuned and
• 48:10hopefully we'll have more to say
• 48:12about that in the time to come.
• 48:14I've tried to acknowledge
• 48:16folks along the way,
• 48:17but here's a more extensive
• 48:20list and I really appreciate
• 48:22your attention and thank you.
• 48:33Yes. So how do you think
• 48:35about driver mutations,
• 48:36specific driver mutations
• 48:38related to transformation,
• 48:41related to potential for
• 48:48these differentiation B cells
• 48:49leading to the clinical outcome?
• 48:51You listed a whole list of
• 48:53potential driver mutations.
• 48:54It's not clearly what the individual
• 48:56driver mutations are doing.
• 48:58And how you think about getting
• 48:59the answer to that question,
• 49:00if it is an important question,
• 49:04yeah, no, I think I skipped
• 49:05over a lot of stuff.
• 49:06And so I think one of the things that
• 49:08we can do when we have these driver
• 49:11lists because we can see whether they
• 49:14segregate into particular pathways.
• 49:16And by virtue of kind of separating
• 49:20out the CLL versus Richter clones,
• 49:22we were able to kind of identify which
• 49:25of those drivers seem to be CLL,
• 49:27which were Richter's and which were which
• 49:30were in a path on the way to transformation.
• 49:34And so some of those pathways that
• 49:38we see affected are related to Mick,
• 49:44for example, they're related to cell cycles.
• 49:47So this it's not a surprise,
• 49:50but it and metabolic rewiring as well.
• 49:54So there's many.
• 49:57So I think the drivers do help us think
• 50:01about the biology of what is going on,
• 50:04but I think that I hope that we can also
• 50:07use them as ways to for early detection.
• 50:11I don't know if this is answers
• 50:13your question,
• 50:13but
• 50:15yeah, I don't want the questions online,
• 50:17but what what do you think about the
• 50:19role of RGS 15 in normal CD5B cells?
• 50:23So it's there, yes.
• 50:25So the question is what is its
• 50:28function in thinking about what
• 50:29CD5B cells are doing in terms of
• 50:32maintenance and tolerance for
• 50:33example and their potential product
• 50:35activity and where they are,
• 50:36right. So we haven't looked into that.
• 50:38I mean I think we we have the tools and
• 50:44so we've we've really been focused on the,
• 50:47the mutant setting. Yeah.
• 50:49But I I think it's a really interesting
• 50:53question and I think that it would
• 50:58be a separate question where it
• 51:00could be like all of these different
• 51:04mutations that we're finding. Yes.
• 51:07Yes the the genes and and what are
• 51:08their roles in in normal business.
• 51:12I think I I I think you are
• 51:15absolutely correct. Yeah. Yes.
• 51:18Yeah I'm I'm getting discredited I think
• 51:21you said that the unmutated CLLS have
• 51:24a re urgent headed with the nursery
• 51:27so yeah so the quest so the unmutated
• 51:31has there are there's a far longer
• 51:34list of mutated drivers in unmutated
• 51:39CLLI see. So I guess the question then
• 51:42is do you think that the mechanism
• 51:45that's leading to the mutations of the
• 51:48IGH locus is unrelated to the genetic
• 51:52diversity that we're getting or is there
• 51:54a relations to them and how does that I
• 51:56I I think that's a great question.
• 51:57So the the question is whether or not
• 52:01how the immunoglobulin mutational status
• 52:07relates to kind of the genetic diversity.
• 52:11So. So yeah, it's been understood
• 52:13that whether or not the CL LS have a
• 52:17mutated or unmutated immunoglobulin
• 52:19relates to their cell of origin,
• 52:22kind of where are they in kind of B
• 52:25cell development and and whether or
• 52:28not those kind of normal physiological
• 52:30mutational processes are are present.
• 52:33So I think it does speak to the
• 52:36underlying biology of that cell
• 52:39of origin and probably it helps us
• 52:42understand why there there could be
• 52:45more mutations in in these different
• 52:50genes compared to the unmutated.
• 52:54So that that that would be a way
• 52:55to put it together.
• 52:59I have some questions. OK, yes.
• 53:03So Marcus Bosenberg asks are
• 53:06there any recurrent genetic or
• 53:08epigenetic changes in CLL arising
• 53:11at later time points in RPS 15?
• 53:16Marcus, hello, great question.
• 53:18We haven't actually looked at that.
• 53:21I think that's a great question and
• 53:23and probably something I should take
• 53:25back to the group and we should look,
• 53:27but we we haven't,
• 53:28we haven't looked at that.
• 53:30So thank you.
• 53:35One last question,
• 53:37there's George Miller asks,
• 53:39can you comment on the role of Epstein
• 53:42Barr virus in conversion of CLL to
• 53:47PLVCL?
• 53:51I really can't maybe yes,
• 53:54we we have not looked at that.
• 53:56It's a great question and certainly
• 54:02EBB does is does play a role in
• 54:06immortalization of B cell lines.
• 54:08But but I I don't have much
• 54:11deep thoughts about that.
• 54:13So my regrets. Thank you.
• 54:15Well, thank you very much for
• 54:17visiting us. Yes, thank you.