The Impact of Therapy on Glioma Evolution

Transcript

• 00:00I think we can start to get in.
• 00:02It's wonderful to be here this
• 00:05morning and to introduce somebody
• 00:07who is absolutely exceptional and
• 00:09who is newly recruited to our
• 00:12department in neurosurgery and also
• 00:14to Smilo in the Cancer Center.
• 00:16Dr. Rol Vierhok is Professor in
• 00:18the Department of Neurosurgery
• 00:20at the Yale School of Medicine.
• 00:22Following graduation with a PhD
• 00:24in Medicine from their Aerosmiths
• 00:26University Medical Center in Rotterdam,
• 00:28the Netherlands Role joined the
• 00:31Broad Institute Dana Farber Cancer
• 00:33Institute as a postdoctoral associate,
• 00:36supported by a fellowship from the Dutch
• 00:39Cancer Society during the time at the Broad,
• 00:41he was part of the team
• 00:42analyzing data from the TCGA.
• 00:44He led the Identification and
• 00:47Characterization of Gene Expression
• 00:49subtypes and glioblastoma work
• 00:51that resulted in a Seminole Cancer
• 00:54Cell 2010 publication will move
• 00:56to MD Anderson Cancer Center in
• 00:592010 to start his own laboratory.
• 01:02Since then,
• 01:03the Veerhawk lab has studied tumor
• 01:05evolution and mechanisms of therapy
• 01:07resistance in low and high grade gliomas.
• 01:10The group was foundational
• 01:12in establishing the Glioma
• 01:13Longitudinal Analysis Consortium,
• 01:15which has established a resource of
• 01:18molecular profiles over time on a
• 01:20large cohort of patients with glioma.
• 01:22They identified and described genetic
• 01:24scars and cellular phenotypes
• 01:26associated with glioma progression
• 01:28and disease recurrence.
• 01:30Extra chromosomal DNA amplifications were
• 01:32discovered as critical drivers and are
• 01:35now a major part of the team's research.
• 01:37After being affiliated with
• 01:39the Jackson Jackson Laboratory
• 01:40for Genomic Medicine in 2016.
• 01:42I can tell you our department leadership
• 01:45fought very hard to recruit him
• 01:48here to Yale and he joined us in the
• 01:51Department of Neurosurgery in 2023.
• 01:52Roll is a recipient of the
• 01:55AAAS Watchal Award,
• 01:56the Agilent the Early Career Professor
• 01:59Award and the Peter Stack Memorial Award.
• 02:02He's also Co founder of Boundless Bio.
• 02:05I can tell you in the short time that I've
• 02:06had the privilege of working with him,
• 02:08he's truly exceptional and I'm really
• 02:10excited for this talk and for all
• 02:13of the work that we have to come.
• 02:15So without further ado,
• 02:17thank you so much Doctor Veerhark.
• 02:20Thanks Jennifer.
• 02:21That very, very kind introduction.
• 02:23And so I joined the Department
• 02:25of Neurosurgery in April of last
• 02:29year after some discussions with
• 02:30Doctor Grinnell and others.
• 02:31And to be honest, it wasn't that hard.
• 02:33I was pretty convinced very quickly
• 02:35that this was going to be a great
• 02:37place to continue our research.
• 02:39As we were thinking about
• 02:40becoming more translational,
• 02:42it was we felt that we that being
• 02:43in a clinical environment would we
• 02:45greatly benefit our work and what
• 02:48what grader clinical environment
• 02:50and Yale School of Medicine and
• 02:52department of Neurosurgery.
• 02:53So I'm a Co founder of a biotech that
• 02:56won't be discussing that work today.
• 02:59I am also a consultant for neurotrials.
• 03:03So gliomas are the most common
• 03:07molecular tumor type in an in adult
• 03:11patients and the most devastating ones.
• 03:13They're characterized by an infiltrative
• 03:15growth into the environment,
• 03:16into the parencoa,
• 03:17and this makes these tumors
• 03:19exceptionally hard to treat because
• 03:20she can't go in with a knife and
• 03:22cut out the entire the entire
• 03:24structure for obvious reasons.
• 03:27Nowadays we recognize traditionally
• 03:29we would classify gliomas in
• 03:32adult patients by histopathology.
• 03:34Fortunately, we've gotten away from
• 03:36that as molecular markers do much more
• 03:39precise job in doing such classification.
• 03:41Nowadays we recognize gliomas based
• 03:44on two critical molecular markers.
• 03:461st, we set, we identified the presence
• 03:49of absence of a mutation in IDH one
• 03:52or IDH 2 isocitrate dehydrogenate.
• 03:54And for those cases that carry an
• 03:56IDH 1 mutation or an IDH 2 mutation,
• 03:58we further separate them by the presence
• 04:00of our absence of 1P19Q code deletion.
• 04:02So from some arm loss of 1P and 19 Q 19 Q.
• 04:06Predominantly the cases that
• 04:08have this code deletion,
• 04:10we call them codels are a majority
• 04:13isopathologically all it goes non code
• 04:15L So those cases that are IDH mutated
• 04:18but don't have that code deletion
• 04:20are in majority astrocytomas and the
• 04:22IDH wild type cases are mostly the
• 04:24glioblastomas and the patients survival
• 04:27patterns are according meaning that
• 04:29those cases that have no IDH mutation
• 04:32do particularly poorly clinically.
• 04:35That's not to say that any of these
• 04:38tuber types are better to have
• 04:39quote UN quote this as far as you
• 04:41can ever better have a tumor.
• 04:43Patients that are that carry the IDH
• 04:46mutant non CODEL tumors are typically
• 04:49diagnosed between 35 and 44 years of age,
• 04:52so very young in life.
• 04:54The codel patient typically
• 04:56is around 45 years of age.
• 04:58So again relatively early in life.
• 05:00So those patients might have much
• 05:02better outcomes but they'll mostly
• 05:04the majority will still succumb to
• 05:06disease even prior to the median
• 05:07age of diagnosis for IDH wild
• 05:09type tumors which is around 60.
• 05:11So these are all bad tumors.
• 05:15Here's the motivation for classifying
• 05:19gliomas by these two molecular markers.
• 05:22And in part it's of course
• 05:23it's clinically it's,
• 05:24it's the survival outcomes as I
• 05:26showed you on the previous slide.
• 05:27But here in this paper
• 05:30from the TCGA from 2015,
• 05:32we demonstrated that not only behave
• 05:34these tumors differently and respond,
• 05:37they they respond different to treatments,
• 05:39but they're really biologically
• 05:41different entities as reflected in
• 05:43the sets of molecular alterations
• 05:46that are commonly detected.
• 05:48For example,
• 05:49in the codel group we find that they
• 05:52are nearly universally carrying
• 05:54mutations in the Turk promoter,
• 05:57as well as relatively spurious mutations
• 06:00in genes like Notch One and NCIC.
• 06:04The non codels,
• 06:05even though they are IDH mutated,
• 06:07rarely contain Turk promoter mutations
• 06:11but are universally mutated in P53 and
• 06:1475% carry hairy alterations in ATRX.
• 06:17So very similar tumor types but
• 06:19molecularly quite different.
• 06:21And then finally IDH wild type tumors
• 06:24again 80% are turb promoter mutated and
• 06:28they are then a majority containing
• 06:31mutations in genes like e.g., FRCDK,
• 06:33N2AP10 and so on and so forth.
• 06:35So biological not just responding
• 06:37differently to treatment,
• 06:38not just different at in terms
• 06:40of at when they present in life,
• 06:42but biologically also quite different.
• 06:47Now after we were able to refine the
• 06:51classification of gliomas in adult patients,
• 06:54a major next a major challenge
• 06:55continues to be how these tumors
• 06:57respond to treatment and the lack of
• 06:58new treatments coming into the clinic.
• 07:03Now tumors initiate from a cell
• 07:05of origin and over time as these
• 07:07cells respond to challenges
• 07:08in the tumor microenvironment,
• 07:10for example presence of oxygen,
• 07:12lack of nutrients and so forth,
• 07:14you'll find that intratumoral
• 07:16heterogeneity starts to develop.
• 07:18And this is a consequence of
• 07:20these evolutionary processes and
• 07:21clonal selection where some cells
• 07:23are better able to deal with
• 07:25these limitations than others.
• 07:27Therefore they become they they
• 07:30they show clonal outgrowth.
• 07:31So at the time of diagnosis we're
• 07:33dealing with an with an heterogeneous
• 07:36tumor with different sets of tumor
• 07:38cells marked by specific and mutations
• 07:39and other kinds of gene alterations.
• 07:43Now critically this process doesn't
• 07:45end a diagnosis of course we impose
• 07:48treatments onto these tumors.
• 07:49You know surgery initially debulking
• 07:52surgery combined with radiation and
• 07:55chemotherapy which for gliomas in
• 07:57majority is stemozolamide, stemozolamide.
• 08:01And of course these treatments
• 08:04continue to impose these bottlenecks
• 08:06onto the tumor and those cells
• 08:09best able to deal with radiation,
• 08:11best able to deal with chemotherapy
• 08:13are the ones that are going to fuel
• 08:15the outgrowth and the tumor recurrence.
• 08:18So we felt that this would be,
• 08:19this would be an important process
• 08:22to study so that we could try and
• 08:24make these treatments more effective
• 08:26and potentially identify targets
• 08:28for new treatment development.
• 08:32So with that in mind,
• 08:33we started the Glioma Longitudinal
• 08:35Analysis or Glass Consortium in 2015.
• 08:39The glass consortium has set out to
• 08:42developed on the tail ends of the
• 08:44TCGA and it set out to develop a
• 08:47comprehensive molecular reference data
• 08:48set from pairs of tumors obtained and
• 08:52diagnosis and then after treatment,
• 08:53so the first tumor recurrence.
• 08:55But in reality we have been collecting
• 08:57tumors along the whole trajectory.
• 08:59So we have cases now for glass where we
• 09:02have 6 recurrences consecutively and
• 09:04we've molecularly characterized them.
• 09:06In other words, we've sequenced them.
• 09:08And then critically for glass,
• 09:10we really try to curate and obtain
• 09:14clinical annotation for all cases in
• 09:17the cohort because the value of a
• 09:20resource like this is significantly
• 09:22amplified if we know which tumors
• 09:24got treated in between time plans.
• 09:26Now why that we needed to do
• 09:28a consortium for this,
• 09:30it's because of things like patient
• 09:32mobility and the way tumor banks work.
• 09:34If you go to your average tumor bank,
• 09:36surely you'll find some tumors for which
• 09:39there's multiple time point specimens.
• 09:41But even for MD Anderson where I
• 09:43used to where we used to be one of
• 09:46the largest centers in the country,
• 09:48we were limited to a few dozen
• 09:50cases where we would have these
• 09:52multi time point specimens.
• 09:53And then as you're dealing with
• 09:55attrition due to tissue quality,
• 09:56at the end of the day,
• 09:58you really need an international
• 10:00collaboration to get to the large enough
• 10:03numbers to do any kind of robust analysis.
• 10:07So we started the consortium also
• 10:09still being enthusiastic of how well
• 10:11the TCJ collaboration went for us
• 10:13and now have developed a consortium
• 10:15that involves over 140 people
• 10:17spread across the globe essentially
• 10:19in 14 different countries.
• 10:21This is an older picture actually.
• 10:23If you would take a picture now,
• 10:24it would fill the room.
• 10:27So an important purpose of Glass
• 10:31is not just to create this,
• 10:33this data set but also to share it
• 10:36broadly just like the TCA so that
• 10:38not just we can do interesting
• 10:40analysis with them but of course
• 10:42that the whole community can do so.
• 10:43So in 2022 we released our latest
• 10:46public version of the glass data
• 10:48resource which is a cohort of 300
• 10:50/ 300 patients for whom we have
• 10:53collected multi timepoint DNA
• 10:55sequencing and or RNA sequencing.
• 10:58Now these 300 patients and I can tell
• 11:00you in the meantime now we're 2024,
• 11:02we've nearly doubled the cohort size
• 11:04and we'll be releasing that soon.
• 11:06So we continue to actively expand
• 11:08this cohort
• 11:11Right now in our code if you
• 11:12go to the URL shown here,
• 11:14you can find variants in clinical
• 11:16annotation for over 300 cases of
• 11:19which in majority are from IDH wild
• 11:22type tumors followed by the non
• 11:24Codells Finally the Codells as you
• 11:26can see we have a relative under
• 11:28representation of Codells here and
• 11:30that's likely due to the longer
• 11:31time to recurrence or Codell tumors
• 11:33compared to IDs wild type tumors.
• 11:35The shorter the time to recurrence,
• 11:38the higher the likelihood that two tumor
• 11:40specimens will end up in the same tumor bank.
• 11:42In addition to that RE resection is not
• 11:44standard for any of these and so that's
• 11:47another factor that comes into play here.
• 11:50You can maybe appreciate that the
• 11:52median age of diagnosis in these
• 11:54three groups is relatively young.
• 11:57And that is because in order to end up for
• 11:59data to end up in our in our resource,
• 12:02the patient has to have had two surgical
• 12:06procedures in order to obtain specimens,
• 12:08meaning that the patient has had to
• 12:10be in relatively good shape to be
• 12:12able to undergo those procedures.
• 12:13So we see a bit of a bias in
• 12:15median aid to diagnosis as well as
• 12:18in survival patterns shown here.
• 12:20That is what it is.
• 12:21We can't really address that.
• 12:23We try to address it by expanding
• 12:25the resources large as we can so
• 12:27that we kept capture as many patient
• 12:29groups as we can.
• 12:30Finally, I'm going to point out that
• 12:32our annotation in my opinion is really great.
• 12:34So we know for all patients whether
• 12:36they or nearly all patients,
• 12:38whether they have received tamizolamides,
• 12:40yes or no and whether they have
• 12:41received radio radiation therapy,
• 12:42yes or no.
• 12:43And we've got many more clinical variables.
• 12:46I just chose to highlight these
• 12:47on this on this particular slide.
• 12:50So I, in my opinion,
• 12:51it's really becoming a phenomenal resource.
• 12:55Now what can you do with a
• 12:56resource like this?
• 12:57I think you can do many things.
• 12:58But we initially started,
• 13:00we initially focused on
• 13:012 important questions.
• 13:02First,
• 13:03what is the impact of temozolomite on
• 13:05tumor evolution and on these gliomas?
• 13:08And 2nd,
• 13:08what is the impact of radiation?
• 13:13So treatment with temozolomite.
• 13:15Temozolomite is a DE alkylating agent.
• 13:18The repair process of the DE
• 13:20alkylation shows up in can show up
• 13:23as mutations and nucleotide changes.
• 13:26Nucleotide changes can conveniently
• 13:28be detected using sequencing,
• 13:30and that means that in a subset of tumors
• 13:32that are treated with temozolomide,
• 13:34A hypermutation phenotype will develop.
• 13:37So these are tumors where cells
• 13:38have been able to overcome the
• 13:40damaging effect of temozolomide,
• 13:42and they do so by repairing the
• 13:44damage caused by temozolomide,
• 13:45and the damage is then showing
• 13:47up as hypermutation.
• 13:48Very high mutational burdens
• 13:52across our cohort and this is slightly
• 13:54older version of our data set.
• 13:56But across our cohort we then see
• 13:58that when we compare mutational
• 14:00burden of the initial tumor and the
• 14:02post TMZ treated recurrent tumor,
• 14:04so this is only TMZ treated cases,
• 14:08we see very high mutational burdens in
• 14:10these recurrences and this is a log scale.
• 14:11So we chose a cutoff of 10
• 14:14mutations per megabase here.
• 14:17So across the three subtypes of glioma,
• 14:20we see that a subset recurs as hypermutated.
• 14:24The relative frequencies differ by subtypes,
• 14:27ID 12 type tumors 15 to 16%.
• 14:30For the IDH mutant tumors,
• 14:32we see that the rate of hypermutation
• 14:35development is much higher.
• 14:37We think this is due to the fact that
• 14:39IDH mutated tumors take longer to recur.
• 14:42Therefore, there's a more of an
• 14:44opportunity for hypermutation to develop.
• 14:50Hypermutation has been associated
• 14:52with relatively poor outcomes.
• 14:54What we found when we EPL evaluated
• 14:57the presence of hypermutation
• 14:58in TMZ treated tumors and then
• 15:00compared to time to progression,
• 15:02that it's actually similar
• 15:04between non hypermutated.
• 15:05So tumors that did not become hypermutated
• 15:08versus those that did become hypermutated.
• 15:10So time to progression doesn't really
• 15:13depend on the development of hypermutation,
• 15:15but once a tumor has become hypermutated,
• 15:18so after that recurrence the hypermutators
• 15:20do worse than the non hypermutators.
• 15:26That's not to say that we shouldn't be
• 15:29treating these patients with tenozolamide.
• 15:31Clinical trials such as the CAD non study
• 15:34from the ERTC have clearly demonstrated
• 15:36that tenozolamide has significant
• 15:38benefits across the patient population.
• 15:41So even though sometimes people will argue,
• 15:43well temozolomite causes hypermutation,
• 15:45hypermutation is bad.
• 15:47As a patient group,
• 15:49temozolomite is clearly beneficial.
• 15:56Emma and Kevin in our lab then chose
• 16:00to study similar questions but then
• 16:02for response to radiation therapy which
• 16:04causes a different type of DNA damage.
• 16:07It causes single single strand breaks
• 16:09as well as double strand breaks.
• 16:14And Long story short,
• 16:17they discovered that when comparing
• 16:19cases not treated with radiation to
• 16:22those that are treated with radiation,
• 16:24that treated cases develop a
• 16:27relatively high number of small 2
• 16:30to 20 base pair deletions across
• 16:32their scattered across their genome.
• 16:35So it's a bit of a similar
• 16:37phenomenon to hypermutation,
• 16:38but instead of single nucleotide changes,
• 16:41we found that radiation drives small
• 16:44deletions and in the treated cases we
• 16:46see significantly more small deletions
• 16:48arise compared to the untreated cases.
• 16:53Now radiation and temozolomide are
• 16:55often used in combination its standard
• 16:57of care for IDH wild type tumors.
• 17:00So are we observing this increase in
• 17:02small deletions because some of these
• 17:05tumors will are developing hypermutation?
• 17:11The answer is yes and no.
• 17:13Meaning that when we split up our cohort
• 17:16in those cases that are hypermutated
• 17:18as well as radiation treated,
• 17:20we find that hypermutation
• 17:22independent of radiation actually.
• 17:24So these are tumors that are hypermutated
• 17:26and have not been treated with radiation
• 17:28that they also show an increase
• 17:32in the number of small deletions.
• 17:34But importantly,
• 17:35those that are not have been mutated
• 17:37and have been radiated also show
• 17:39that small deletion increase.
• 17:41So hypermutation and radiation
• 17:43are independent factors driving
• 17:45the increase in small deletions.
• 17:48And in that sense small deletions,
• 17:50the small deletion increase
• 17:52burden increase is comparable to
• 17:54hypermutation for actemozolamide.
• 17:56And in our paper we actually found
• 17:59that this is true across cancers,
• 18:00not just gliomus,
• 18:04Gemma and Kevin and also evaluated
• 18:06anuploidies, in other words,
• 18:08broad losses and gains.
• 18:09So while small deletions will
• 18:11arise from double strand breaks
• 18:13that are subsequently repaired,
• 18:16anuploidies typically are a
• 18:17result of cell cycle of errors.
• 18:19During the cell cycle,
• 18:21for example MIS segregation,
• 18:25we compared gains, broad gains and
• 18:28broad losses and we did that between
• 18:31irradiated cases and unirradiated
• 18:33cases and found no difference in gains.
• 18:37But we found a significantly higher
• 18:39number of whole chromosome arm losses in
• 18:43irradiated versus non irradiated tumors.
• 18:45Similar to the small deletion increase,
• 18:50now homocygous deletion of CDK
• 18:51into A which is of course a cell
• 18:55cycle regulator has previously been
• 18:57associated with tumor progression
• 18:59especially in Ida mutant tumors.
• 19:03We compared not just homozygous
• 19:05deletion but also hemisygous deletion
• 19:07of CDK and to a first in untreated
• 19:10initial tumors in glass this is
• 19:12codels and non codels and this is
• 19:14focusing on the IDH mutant tumors,
• 19:16Codels versus non codels first
• 19:18and we find that non codels have
• 19:21a higher rate of CDK and to a
• 19:23homozygous as well as semisygous loss,
• 19:26but this is particular particularly
• 19:28pronounced in recurrences.
• 19:30So at recurrence non codal IDH
• 19:32mutant tumors significantly show
• 19:34a significant increase in the
• 19:37number of CDK N to a homozygous
• 19:38as well as hemisygous deletions.
• 19:41And this is then particularly true
• 19:44amongst irradiated tumors suggesting
• 19:46that there's a relationship between
• 19:48irradiation and CDK N to a loss.
• 19:53And again the presence or the when
• 19:56a CDK N to a loss either hemisygous
• 19:59or homozygous has been acquired,
• 20:02we see that that correlates associates
• 20:04with worse outcomes to treatment.
• 20:10We then evaluated the association between
• 20:13these broad aneuploidies and CDK into a loss.
• 20:17Here we grouped a bunch of cases.
• 20:19Actually this is not class data,
• 20:21this is span cancer data.
• 20:22We grouped those cases by non irradiated.
• 20:26Palliatively irradiated.
• 20:27So lower doses and accuratively irradiated
• 20:30tumors and find that the increase in
• 20:34the number of chromosome losses is
• 20:36actually only found in tumors that
• 20:38have homozygous deletion of CDK into A.
• 20:40So while we previously showed
• 20:43that irradiation appears to
• 20:45drive broad chromosome losses,
• 20:48what we're actually seeing is that
• 20:51irradiation associates with CDK into a loss,
• 20:53and it's really the CDK into a loss
• 20:56that then associates with aneuploidy
• 20:59because we're seeing a significant
• 21:00increase in the number of chromosome
• 21:02losses in unirradiated cases when
• 21:04homozygous loss of CDK into A is present.
• 21:08So irradiation itself does not
• 21:10appear to drive aneuploidy.
• 21:11It appears to drive CDK into a loss,
• 21:13which then drives to aneuploidy.
• 21:17And as with the hypermutation example,
• 21:21we're finding no significant
• 21:22difference in surgical interval in
• 21:25irradiated cases associating with the
• 21:28number of acquired small deletions.
• 21:30But post recurrence,
• 21:31those tumors that acquire the most,
• 21:33the highest number of new small
• 21:35deletions are the ones that stop
• 21:37responding to further therapy
• 21:39that have very poor outcomes.
• 21:41So acquired small deletions are a marker
• 21:43for further tumor response if you will.
• 21:52So this is the model that
• 21:53seems to arise from our data,
• 21:54which is perhaps not super surprising.
• 21:57These tumors as I started with
• 22:00originate from a cell of origin
• 22:02that starts to expand more
• 22:03quickly than the cells around it.
• 22:05And upon bottlenecks in
• 22:07the tumor microenvironment,
• 22:08subclones will further arise that
• 22:10then at the time of the diagnosis
• 22:12can be detected through sequencing.
• 22:14Then we impose this therapeutic
• 22:16barrier for surgery and then a chemo
• 22:19and radio and those cancer cells that
• 22:20are able to repair the DNA damage.
• 22:22So the ones that have the hypermutation
• 22:25phenotype or the ones that have acquired
• 22:27large numbers of small deletions,
• 22:29those are the ones that are
• 22:30able to repair the A damage and
• 22:32can subsequently be detected,
• 22:34have expanded sufficiently,
• 22:35have become large enough subclones that
• 22:38we can detect them through sequencing.
• 22:40So then it's not surprising that
• 22:42in recurrence these tumors that
• 22:44have these genomic scars,
• 22:45these signatures are the ones
• 22:46that have poor outcomes and stop
• 22:48responding to treatment because
• 22:50you're looking at cells that have
• 22:52already been able to have already
• 22:54shown that they don't care about
• 22:55further DNA damage or they don't
• 22:57care about chemo radiotherapy.
• 23:01Now when you summarize these
• 23:03numbers across our glass cohort,
• 23:07we see that amongst IDs wild type
• 23:09tumors that have been treated with
• 23:11temozolomide and or radiation that 15%
• 23:14develops the hypermutation phenotype.
• 23:17And of those that are that do not
• 23:19develop the hypermutation phenotype,
• 23:20another 16% requires large numbers
• 23:23of small deletion which leaves a
• 23:26relatively large group in which no
• 23:31genomic scars can be detected,
• 23:33A recurrence and they may have
• 23:35intrinsic mechanisms to deal with
• 23:38the toxic effects of therapy.
• 23:41When we look at IDH mutant
• 23:43tumors where the picture is more
• 23:45diverse because not all patients
• 23:47will receive the same therapies,
• 23:49we find that amongst those that have
• 23:50been treated with temozolomide,
• 23:5242% acquires hypermutation,
• 23:5335% of non hypermutators acquires
• 23:56the small deletion phenotype and
• 23:59again a subset shows neither.
• 24:04Now as Jen mentioned in the intro and
• 24:07previous work we have looked at gene
• 24:09expression patterns and this is focusing
• 24:12on GBM so IDH small type tumors.
• 24:14And we found that when we evaluate gene
• 24:17expression patterns we can and identify 3
• 24:20gene expression subtypes of glioblastoma,
• 24:22IDH well type glioblastoma which we labeled
• 24:24mesenchymal per neural and classical.
• 24:28When we evaluate A subtype classification
• 24:31in glass we see that you know we
• 24:34see the the relative distribution
• 24:35is of these three subtypes is
• 24:37what we typically would expect.
• 24:39A number of cases are classical
• 24:42mesenchymal or per neural at recurrence.
• 24:44We do appear to see a minor
• 24:47shift towards mesenchomal tumor.
• 24:48So we see a high number higher number
• 24:51of mesenchomal tumors and recurrent.
• 24:52So tumor progression appears to
• 24:56and some tumors coincide with
• 24:58mesenchomal transformation.
• 25:00But perhaps more importantly,
• 25:01we find that these subtypes are
• 25:04these subtypes identifications.
• 25:06Classifications are quite flexible
• 25:07because nearly half of our cases actually
• 25:10change subtypes between the initial
• 25:12time point and a recurrent time point.
• 25:17Now, like many tumor types,
• 25:18glioblastoma has been extensively
• 25:20studied using single cell sequencing
• 25:23and single nucleus sequencing.
• 25:25Our collaborator Mario Suva has let let
• 25:27the field in this respect and published
• 25:30a very influential paper in 2019,
• 25:32the Neftal ET al.
• 25:33Study which they found four
• 25:36predominant cell states of
• 25:38glioblastoma which they labeled
• 25:40oligo progenitor cell like neuro,
• 25:42progenital cell like astrocyte
• 25:45like mesenchymol like.
• 25:47You may notice that these terms are
• 25:49actually reminiscent of the subtypes
• 25:51that we had previously identified
• 25:53and that's maybe not surprising.
• 25:55If a tumor contains a majority
• 25:58mesenchymal like cells,
• 25:59the subtype signature will be mesenchymal.
• 26:01We've shown this using combined bulk
• 26:03RNA C and single cell RNA C data sets.
• 26:08Oh, and another important thing to remark
• 26:11here is that all GBMS each of the cell
• 26:14states can be detected in all GBM's.
• 26:16It's just a shift in numbers which
• 26:20perhaps explains the plasticity
• 26:22of expression subtypes over time.
• 26:24Now my lab, Kevin and Kevin Johnson
• 26:27and Kevin Johnson and Kevin Anderson
• 26:28have worked together to do single
• 26:30cell sequencing of gliomas as well
• 26:33with the purpose of identifying
• 26:35pan glioma cell states.
• 26:37Mario's Neftal at all cell states
• 26:39are focused on IDH well type tumors.
• 26:41Our effort initially focused on
• 26:43identifying cell states that could
• 26:46be identified across different
• 26:47types of glioma and we found those
• 26:50we labeled them stem like cells,
• 26:51proliferating stem like cell and
• 26:53differentiated stem like cell.
• 26:54And of course with single cell sequencing
• 26:56you can also identify non malignant
• 26:58cell states such as elutical dendrocytes,
• 27:00parasites,
• 27:00myeloid cells, cells,
• 27:01cells that are typically residing in
• 27:03the microenvironment of these gliomas.
• 27:08Now you can take, you can infer
• 27:11signature gene signatures from these
• 27:13single cell States and you can then
• 27:16use computational methods to project
• 27:18those signatures on bulk RNA C datasets
• 27:20such as the ones we have in glass.
• 27:23So you can use single cell signatures to
• 27:26deconvolute bulk expression profiles.
• 27:31So Fred Verne, former post doc in the lab,
• 27:33now a faculty member at the Jackson
• 27:35laboratory, took this approach,
• 27:38used the single cell inferred gene
• 27:41signatures from the Kevins and projected
• 27:44those onto the glass RNA C data sets.
• 27:48This is showing on the left IDH.
• 27:49Well the summary of IDH wild type tumors.
• 27:52On the right the IDH mutant
• 27:55tumors primary and recurrences.
• 27:57So when we aggregate all the data,
• 27:59first starting with IDH wild types,
• 28:02when we aggregate the presence of the
• 28:05single cell signatures across the cohort
• 28:08and compare initial to recurrent tumors,
• 28:11we do not find major shifts in our
• 28:14Panvama cell state cell state presence.
• 28:18Actually the major difference
• 28:20we found when comparing initial
• 28:23tumors to recurrent tumors is the
• 28:26relative fraction of oligodenrocytes
• 28:27in the tumor microenvironment.
• 28:32And maybe this is not too
• 28:35surprising if you look at the the
• 28:39invasive margins of these tumors,
• 28:41this is at the time of recurrence.
• 28:43This is where most of the tumor cells will
• 28:46have come from because this is the area
• 28:48of the tumor that's difficult to resect.
• 28:50So perhaps at recurrence you can
• 28:52imagine that at recurrence more
• 28:54of that margin is cut out.
• 28:56Therefore, we might be able to see more
• 28:59cells from that micro environment,
• 29:01in this case particularly oligodenvrosites.
• 29:06What what did seem more surprising
• 29:09to us is then again when Fred used
• 29:12computational methods to not just
• 29:14count enumerate the types of cells in
• 29:18primary to recurrent tumors but also
• 29:20looked at the actual gene expression
• 29:22profile of those cells that we found
• 29:25that a significant increase in
• 29:27neuronal signaling pathways amongst
• 29:29the malignant cell population.
• 29:32Of course you'll find neuronal
• 29:33signaling and oligodendrocytes but
• 29:35what we were finding is that also
• 29:37the malignant cells activate neuronal
• 29:39signaling pathways as recurrence.
• 29:42So we're seeing an increase in
• 29:45oligodendrocytes in the microenvironment
• 29:46but that appears to be converging
• 29:48with increased levels of neuronal
• 29:51signaling by the tumor cells.
• 29:54And when we use a public data set
• 29:57consisting of multi biopsy single cell
• 30:00RNA sequencing from glioblastoma patients,
• 30:03we could again confirm that the
• 30:06malignant single cells expressed
• 30:08higher levels of neuronal pathways
• 30:12when they were when the biopsies
• 30:14were obtained from the margins of
• 30:15the tumor relative to the core of the
• 30:18tumor confirming what Fred had found
• 30:20in our bulk analysis from glass.
• 30:25I'm actually going to skip this one.
• 30:27We decided this to then take
• 30:29this one step further in a large
• 30:31collaboration that involved Mario Suva,
• 30:33Itai, T Rush, Antonio Yavarone,
• 30:36Anna Lazarella as well as many
• 30:39postdoc and junior leads in
• 30:41in in these respective labs.
• 30:43Collaborating with MD Anderson,
• 30:44Duke and a number a number
• 30:46of other institutions
• 30:50we acquired. We generated longitudinal
• 30:52single nucleus RNA seed data for a large
• 30:55number of IDH wild type glioblastomas,
• 30:57again in the context of annotation for
• 31:00different types of therapy and we also
• 31:02were able to generate exomorhol genome
• 31:04sequencing on the majority of our core.
• 31:09So previously Mario and colleagues
• 31:14identified these four cell states that I
• 31:16mentioned earlier, NPCOPCACMS like cells.
• 31:21When we analyzed over 500,000 cells from
• 31:24this cohort and again to derive cell states
• 31:28as well as transcriptional meta programs,
• 31:31we find these same 4 cell States and the gene
• 31:34express's check features that come from them.
• 31:37Again, here is OPC, AC,
• 31:40mesenchymal, and NPC.
• 31:41But of course we found many more
• 31:44because of the much larger cohort
• 31:46as well as because in his Mario's
• 31:48initial study he had only untreated,
• 31:50he included only untreated tumors.
• 31:51And now we're looking at
• 31:54both primary and recurrences.
• 31:55So our large data set enabled
• 31:57us to find the number of new
• 31:59glioblastoma related cell programs.
• 32:06As we had also observed
• 32:09in our glass analysis,
• 32:10the relative number of malignant
• 32:13cells decreased at recurrence.
• 32:14So recurrent GBMS become less
• 32:17pure or more incorporate more
• 32:20tumor microenvironment cells.
• 32:21So we see a decrease in the number
• 32:24of proportion of malignant cells
• 32:25and an increase and we confirmed
• 32:27the increase in the number of
• 32:29oligodendrocytes that's because of
• 32:30the greater resolution of the single
• 32:32cell of the new single nucleus data.
• 32:33We also see a significant increase
• 32:35in the number of astrocytes in
• 32:37the number of neuronal cells
• 32:42converging with the result from glass
• 32:45that most tumors or many tumors
• 32:47change tumor subtype at recurrence.
• 32:50We find large shifts in cell states
• 32:54between primary and recurrent tumors
• 32:57and the one that maybe is interesting is
• 33:00hypoxia and I'll get back to that later.
• 33:03So a subset of this smaller color compared
• 33:07to glass acquired this small deletion
• 33:09phenotype that I mentioned earlier.
• 33:10In fact 10 of 46 tumors where we had
• 33:14converging DNA sequencing and single
• 33:17nucleus sequencing data acquired this
• 33:19small deletion phenotype as shown here.
• 33:22And what we're seeing is that when a small
• 33:25deletion phenotype has been acquired,
• 33:27tumors will increase.
• 33:29We find that more tumor cells show signs
• 33:32of hypoxia are responding to hypoxia.
• 33:37So radiation either drives or
• 33:42shows its most significant effects
• 33:44in cells in regions of hypoxia.
• 33:50When we then went back to our glass
• 33:52data sets, we could confirm that
• 33:54indeed tumors that acquire lots of
• 33:56small deletions are also showing
• 33:57an increase in hypoxic signaling,
• 33:59hypoxia cell state signaling compared to
• 34:01tumors that do not acquire small deletion.
• 34:07This is potentially relevant because
• 34:09hypoxia is a phenomenon you can
• 34:11detect through imaging and of course
• 34:14these results built upon large
• 34:16and large amount of literature
• 34:18demonstrating the convergence of
• 34:20radiation response with hypoxia.
• 34:26We then also focused,
• 34:28we then also performed A comparable
• 34:30analysis but looking at IDH mutant tumor.
• 34:32So we generated single nucleus
• 34:34RNA CC data on IDH mutants,
• 34:38a cohort of 35 cases and this is
• 34:40work led by Kevin Johnson who is a
• 34:42research scientist in our lab again
• 34:44with the same collaborator team.
• 34:51Mario Nita's labs have previously
• 34:54found that in IDH mutant tumors they
• 34:58they found less consistent cell cell
• 35:01state and gene expression programs.
• 35:02But they found that all that most tumors
• 35:06could be projected along an axis of stem
• 35:10like to stem like Sigma from stem like
• 35:14states to a more differentiated state.
• 35:17Because IDH mutant tumors in general are
• 35:20either astrocytoma or oligodenra glioma,
• 35:23They showed that astrocyte
• 35:28IDH mutant gliomas differentiate
• 35:29from a stem like cell to a astrocyte
• 35:33like cell phenotype whereas
• 35:35oligodenrocytes go from a stem like
• 35:37state to a more oligodenrocyte like
• 35:40state as potentially expected.
• 35:41So the non codels go to the left
• 35:44and the codels go to the right.
• 35:48In our paper from 2022 with
• 35:50Fred as first author,
• 35:51we had noticed that amongst IDH mutants
• 35:56those that show signs of treatment
• 35:59response either through hypermutation
• 36:01or through acquired sydicand to a loss,
• 36:04we saw an increase in the proportion
• 36:07of proliferating stem like cells
• 36:09which would be in the in the trunk
• 36:12of the axis shown on the left.
• 36:15So we took those results and we
• 36:17took those into consideration as
• 36:19we started to analyze these data.
• 36:21So first we Kevin generated these U
• 36:24maps that you can see in many papers.
• 36:26We had generated data from large
• 36:28numbers of nuclei,
• 36:29I would say quite unprecedented to show
• 36:32that you can infer your typical sets
• 36:35of cell state programs as shown here.
• 36:38So we have now generated a very
• 36:40large number of IDH smooth and single
• 36:42nucleus data and through that we can
• 36:45create a definition of cell States and
• 36:47associated metaprograms of IDH mutant tumors.
• 36:50And the metaprograms we arrived at
• 36:54actually are quite reminiscent of those
• 36:56that are shown in IDH wild type tumors.
• 37:03Interestingly, when we looked at the this,
• 37:05when we projected the 35 cases that we
• 37:09had analyzed that we had sequenced,
• 37:11we projected them on that same
• 37:13inferred Y axis as Mario and Itai had
• 37:16previously used in their analysis.
• 37:18We see that tumors tend to shift.
• 37:20So there's the circles here
• 37:21are the initial tumors and the
• 37:23triangles are the recurrent tumors.
• 37:26We see that nearly all tumors
• 37:28shift into an upward direction.
• 37:31So the relative amount of stem like or stem,
• 37:34the relative amount of stemness
• 37:36in these tumors almost universally
• 37:39increases upon recurrence.
• 37:41So tumor seems to de differentiate
• 37:47as a part of their tumor progression.
• 37:53This is also shown here.
• 37:54These are different some of the different
• 37:57meta programs that we had arrived at
• 37:59looking at different grades amongst
• 38:01Codells and non Codells and we see
• 38:05that a differentiated differentiation
• 38:07cell state such as the AC like
• 38:10state decreases upon with grade.
• 38:12And that's true for both Codells and non
• 38:16Codells whereas undifferentiated and
• 38:18number of cycling cells increases with grade.
• 38:23And when we actually pair up the tumor,
• 38:27so not split them by grade but
• 38:29actually look at paired samples
• 38:31again we confirm that the amount of
• 38:34undifferentiated cells increases,
• 38:36the amount of cycling cells increases,
• 38:38cells that show signs of stress increases
• 38:41in proportion and finally mesenchymal
• 38:43like cells increase in proportion.
• 38:49Now what is in my view most interesting
• 38:53about these observations is when we now
• 38:56separate our you know relatively modest
• 38:58cohort but still into cases that have signs
• 39:02of therapy induced genetic alterations.
• 39:05But those are the ones that also
• 39:07are also those are the ones that are
• 39:09driving these significant changes.
• 39:11So tumors that do not reflect therapy induced
• 39:15genetic alterations such as hypermutation,
• 39:17citic anti a loss or loss of new
• 39:20aneuploidies, we find no significant
• 39:22difference in cell states.
• 39:24It's only those tumors that show a
• 39:27treatment induced alteration that
• 39:28are the ones that also show changes
• 39:30in their gene expression programs.
• 39:36So to then re annotate the
• 39:39flow charts I showed earlier,
• 39:42we're seeing that subsets of IDH
• 39:44mutant as well as IDH wild type
• 39:47tumors acquire genetic alterations
• 39:48in response to treatment and we're
• 39:51now finding using our bulk and
• 39:53single nucleus our expression
• 39:55gene expression datasets that
• 39:57this coincides with increased cell
• 39:59cycle activity and proliferation.
• 40:02And D differentiation programs
• 40:04and IDH mutant glomus but with
• 40:08neuronal and mesenchomal signaling
• 40:10activity and IDH wild type tumors.
• 40:13So it leads me to summarize at the end here.
• 40:17IDH wild type gliomas so far seem to
• 40:20undergo tumor cell extrinsic changes
• 40:22which sets them apart from IDH mutant
• 40:25glomas which appear to a majority
• 40:27undergo tumor cell intrinsic transitions,
• 40:30which I think is a peculiar
• 40:32but interesting difference
• 40:35As we think about developing new
• 40:38therapies for these patients,
• 40:39this is something to take into consideration.
• 40:43And finally amongst the IDH mutant clairomas,
• 40:47the changes we are observing are mostly
• 40:49observed when in those tumors that have
• 40:52been treated and we find convergence
• 40:55between newly acquired genetic
• 40:57alterations with cell state transitions.
• 40:59So that leads to the question,
• 41:02are these tumors changing because
• 41:04of the treatment or are the
• 41:07oncologists treating the tumors that
• 41:09are more likely to change or both?
• 41:12That's something for a next analysis.
• 41:15With that, I come to the end,
• 41:17I'd like to thank all the people
• 41:19in the lab that worked very hard
• 41:21for these results and of course
• 41:22our funding our funders.
• 41:24Thank you very much.
• 41:29Thank you. Jen had to run to the OR,
• 41:31so I will handle the questions.
• 41:33Do we have any questions
• 41:34from the room or online?
• 41:38You mentioned immunotherapy,
• 41:40so are are there protocols now that
• 41:42are using some of these markers to
• 41:44determine who should get immunotherapy
• 41:45and which ones in this disease.
• 41:46So regrettably all the results so
• 41:48far I've shown that checkpoint
• 41:50inhibition does relative does little
• 41:53for these patients and that's likely
• 41:56because of the very immunosuppressive
• 41:57microenvironment in these tumors.
• 41:59There's very few active T cells.
• 42:00So you can treat them at checkpoint
• 42:02inhibition but without T cells that's
• 42:04going to not really result in any benefit.
• 42:07So moving forward the way to get
• 42:09immunotherapies to work in these patients
• 42:12would be to figure out how can we get
• 42:15T cells into the tumor and only then
• 42:17immunotherapy is is likely to have a chance.
• 42:19Got it. OK.
• 42:20Any questions in the room? OK.
• 42:21I will walk the microphone around.
• 42:23I'll go the front row here first.
• 42:28Hello. Oh thanks. A beautiful talk and
• 42:30I think you know just to it's something
• 42:33that we are all hoping to be able to
• 42:37replicate in different tumor types.
• 42:40What a what a great example of a
• 42:42a treasure trove of information.
• 42:45My question is about epigenetic regulation
• 42:47and I saw one slide with EZH 2
• 42:50your thoughts or if you've looked
• 42:52at sort of wrapping of chromatin
• 42:55epigenetic regulation specifically
• 42:58after radiation, if that's changed,
• 43:00if we can explore that with some of our,
• 43:02for example, ECH,
• 43:03two or other regulators there,
• 43:05inhibitors there,
• 43:08that's a great question.
• 43:11So just from a data perspective,
• 43:13we have been able to collect
• 43:15the NMS elation profiles,
• 43:17other members of the glass
• 43:19consortium have looked at those.
• 43:21What we see in the IDH wild type tumors,
• 43:23we don't see many changes from
• 43:25a Dena methylation perspective.
• 43:28These tumors have lots of things going on,
• 43:30but it doesn't really seem to change
• 43:32in directly their DNA methylation
• 43:34profile and the IDH mutants.
• 43:36We see that the subset of tumors goes
• 43:38from a relatively high amount of genome
• 43:41Y DNA methylation to a decreased amount.
• 43:44So and those are the ones that
• 43:46also are also the ones that change
• 43:48that acquire genetic alterations
• 43:49that change cell state programs,
• 43:51those also seem to demethylate or
• 43:55show demethylation genome wide.
• 43:57Now whether that has implications
• 43:59for treatment with ECH 2 inhibitors
• 44:01would be a bit of a stretch.
• 44:03I know those are being considered for
• 44:07the H3 wild type pediatric GBMS for example,
• 44:12but right now I don't have information on
• 44:14whether that will work for for adults.
• 44:17Great, we can go to Doctor
• 44:18crop and then doctor Contessa
• 44:19in the chat has a question.
• 44:21So we'll get him queued up
• 44:22to ask it verbally.
• 44:25Ian, very nice talk.
• 44:26And this question actually
• 44:27is a little bit similar
• 44:28to I think what Joe's getting at.
• 44:31So you've shown that in the the subset
• 44:34of the temozolomide treated patients
• 44:36developed this hypermutated phenotype
• 44:38and that's associated that leads to
• 44:40poor outcomes in those patients.
• 44:43It would seem that if you could
• 44:44potentially if you could identify
• 44:45those patients up front who were
• 44:47going to go down that path with
• 44:49treatment with temozolemide that
• 44:50you may decide it may be in their
• 44:52overall better outcome to avoid using
• 44:54temozolemide in those patients.
• 44:55So if you looked at baseline molecular,
• 44:59molecular genomic characteristics
• 45:01of the patients who go on to develop
• 45:03hypermutator phenotype to be able to
• 45:05if you could predict those up front,
• 45:07yeah. So for ID, it's wild type
• 45:08of course we have a great marker.
• 45:09It's MGMT methylation, right.
• 45:11So for that, I would say that's
• 45:14already most largely addressed.
• 45:15For IDH mutants,
• 45:16we have not looked at this very much yet.
• 45:19There's been another publication from
• 45:21a group in China that has established
• 45:23a large number of serial cases.
• 45:26They have suggested that low level changes
• 45:30in chromosome 8 would be predictive of
• 45:34risk of developing hypermutation and
• 45:36they link that functionally to MIC.
• 45:39I think that data is interesting.
• 45:41I think it could use some further
• 45:43validation now as we are expanding
• 45:45and working on our glass effort,
• 45:46a major change relative to our latest
• 45:50release and one we are working on right
• 45:52now is that we've accumulated a large
• 45:54amount of whole genome sequencing data.
• 45:55And I'm excited about that because
• 45:57with whole genome sequencing data,
• 45:58you can do things with mutational
• 46:00signatures and mutational signatures
• 46:02would reflect for example,
• 46:03potentially DNA damage repair processes
• 46:05that are ongoing in these tumors.
• 46:08So I'm hopeful that we can identify tumors
• 46:10that have DNA damage repair processes
• 46:12going on and that that would then be
• 46:15repredictive of response to demosolomide.
• 46:17That's all speculation.
• 46:18So hopefully in a year from now or so,
• 46:21we will have more definitive answers.
• 46:23Thanks.
• 46:23Great.
• 46:24Doctor Contessa,
• 46:24I'm told we don't have access
• 46:26to allow him to talk.
• 46:27Can I can you hear me a miracle?
• 46:31Yeah, this is OK go ahead.
• 46:32Oh, great role. That was fantastic.
• 46:35Fantastic talk, very exciting.
• 46:39So yeah, I just wanted to drill
• 46:40down a little bit on the the
• 46:42radiation induced mutations because
• 46:44there is this question, right.
• 46:46Is it that you're select that
• 46:49after radiation it's a selective
• 46:51pressure and you're winding up
• 46:53you know finding those those
• 46:56mutations that have gone on and been
• 46:59propagated in in different clones.
• 47:02And you know I think that
• 47:03the main question is,
• 47:04so if you're sequencing from a tumor
• 47:07and considering the stochastic
• 47:09nature of radiation are do you
• 47:11think you're going to be able to
• 47:13find those recurrent you know,
• 47:15small deletions and isn't that
• 47:17probably more consistent with you
• 47:19have a resistant clone which might
• 47:21be you know have adna repair defect
• 47:23which enables radiation resistance and
• 47:25so then you wind up having that you
• 47:29know radiation resistant clone moving on.
• 47:32And I and I think that's similar to
• 47:33what you would see with CDK and 2A,
• 47:35but I won't be too long.
• 47:36And I guess my main question is,
• 47:37so can you know you have these
• 47:39two different possibilities,
• 47:40Could you use a single cell analysis to
• 47:42analysis to try to differentiate between,
• 47:45right.
• 47:46Is it the radiation that's
• 47:48the cause or just the,
• 47:50you know that it's the
• 47:51the selective pressure?
• 47:52Yeah. Thanks.
• 47:53Thanks very much and and great question.
• 47:55So if we take hybrid mutation
• 47:58following tamizolomide as an example,
• 48:00because of the specific mutational
• 48:02signatures of mutations acquired
• 48:04after mint temozolomide,
• 48:06we're pretty sure that temozolomide is
• 48:09actually causing these these changes.
• 48:11And I think there's a lot of
• 48:14similarities between the small
• 48:15deletions acquired by after irradiation
• 48:18to the temozolomite example.
• 48:23One reason for saying that is that
• 48:24we have taken cell line models and
• 48:27irradiated them and then passes them for
• 48:2925 times or so or have made sure they
• 48:31went through a full cell cycle 25 * /
• 48:34a period of let's say 3 or so months.
• 48:37One of our MDP disease students
• 48:39has done that in the lab.
• 48:40And she said she showed that
• 48:42after about 3 months,
• 48:43you see a significant increase in the
• 48:45number of small deletions and tumors
• 48:47with or in cell lines with radiation
• 48:49versus those that have not been irradiated.
• 48:52And actually,
• 48:52and she actually spoke with your student
• 48:55after her exciting talk just two weeks ago.
• 48:57So that to me suggests a
• 49:00pretty strong causal link.
• 49:01Also, the types of small deletions,
• 49:04we've now made some progress
• 49:05in analyzing them.
• 49:06They carry a specific signature or they are
• 49:08associated with this specific signature,
• 49:10which again to me suggested
• 49:12there's a direct causal link rather
• 49:14than radiation causing clonal
• 49:16outgrowth of a particular clone.
• 49:21Yeah, I think that's what I wanted.
• 49:23Yeah, thanks. We should connect
• 49:25because I I have some some
• 49:27more comments and discussion.
• 49:29Great. I would love to. OK, I I just
• 49:31unmuted. Doctor Robinson, do you want
• 49:33to ask your question?
• 49:36Yeah, phenomenal talk.
• 49:36I was going to ask,
• 49:38I think you already answered this
• 49:39about the MGMT methylate if there's
• 49:40a difference in patterns resistance.
• 49:42But the other question I was going
• 49:44to ask is you know has there
• 49:46been efforts to kind of pursue
• 49:47synthetic lethal screens of some
• 49:48of these identified pathways,
• 49:50So CD and K things like that,
• 49:53That's a, it's a great suggestion.
• 49:56My speculation is that probably
• 49:57somebody has done that.
• 49:58We're not doing those in the lab right now.
• 50:01I guess you know challenges of
• 50:03course exist even though tell us this
• 50:06exists of course with getting any
• 50:08kind of molecules into the brain,
• 50:10you know if you have a target
• 50:12most many of our clinical trial
• 50:13failures that we've seen so far
• 50:15are actually related to blood brain
• 50:17barrier and and things like that.
• 50:19So I think your your your
• 50:20idea of course is very good.
• 50:24It'll take a little bit longer
• 50:26before we can actually see
• 50:29drugs and treatments materialize from that.
• 50:32And one follow up question,
• 50:33one thing that's always really been
• 50:35perplexing to me is that with EGFR 3 variants
• 50:37that if you put those in a Petri dish,
• 50:40those get selected out.
• 50:41So they're actually disadvantageous
• 50:42in a Petri dish.
• 50:43But obviously we see them in
• 50:45in real human tumors.
• 50:46Do you have any any kind of
• 50:48sense or any insights as to
• 50:49why those are advantageous in
• 50:51the real human environment,
• 50:52But they're not in a Petri dish,
• 50:54so it's hard to answer that directly.
• 50:58Maybe it has to do with the types of
• 51:01ligands that exist in the micro environment
• 51:04versus those that exist in a Petri dish.
• 51:06The the spin I would gift on this
• 51:08is that we find that all V3 variants
• 51:11exist on extra chromosomal DNAS.
• 51:14So EGFR is is amplified when the V3 is
• 51:18present and these amplifications typically
• 51:20reside on extra chromosomal DNAS.
• 51:23And there's a lot of, you know,
• 51:27that does a lot of things to these cells,
• 51:31including potentially putting a higher
• 51:33burden on the cells to produce all
• 51:36the DNA needed for the high numbers
• 51:38of copies that typically exist when
• 51:40there's extra chromosomal DNA.
• 51:41So that could be one reason.
• 51:43And in general,
• 51:44I think EC DNA is very potent in
• 51:48many ways and that probably has
• 51:50to do with why these are selected
• 51:52out more so than the V3 itself.