"Synthetic Lethal Therapy of TP53-mutated Head and Neck Cancer" and "Environmental and Sex- Specific Molecular Signatures of Glioma Causation"

Transcript

• 00:00What you know it's 1202 or
• 00:02why don't we get started?
• 00:05And I know there are folks still logging on,
• 00:08so for those of us who are here,
• 00:12thank you for joining cancer grand rounds.
• 00:14I hope all of you had a restful
• 00:18and enjoyable Thanksgiving and.
• 00:20Obviously, I know we're all
• 00:22looking forward to year's end and
• 00:25hopefully celebrating a better 2021,
• 00:27but we're really very fortunate to have
• 00:31two exceptional speakers today and I'll
• 00:34start by introducing our first speaker,
• 00:36who frankly needs no introduction.
• 00:39Doctor Barbara Burtness is
• 00:41professor of Medicine Co,
• 00:43leader of the Developmental
• 00:45Therapeutics research program.
• 00:46And leader of the head neck cancer program
• 00:49and Yell Barbara is internationally
• 00:51known for her leadership in clinical
• 00:54development and research and
• 00:56understanding the biology of heading
• 00:58that canceran among her many accolades.
• 01:01We can add now only in the past
• 01:04month is the principle investigator
• 01:06of the head and Explorer of which we
• 01:10are just so proud of both Barbara
• 01:12and the entire team being awarded
• 01:15this really coveted an elite grant.
• 01:17Of which I think will be from
• 01:20not correct if I'm not mistaken,
• 01:22there only two head neck spores
• 01:24now in the United States are the
• 01:26leader of one of them,
• 01:28which is an extraordinary distinction for
• 01:30the people who work in this space at Yale.
• 01:33So Barbara was kind enough to
• 01:34share with us the work she's doing
• 01:37on head neck cancer and forever.
• 01:39Thank you for joining
• 01:40us today. Well,
• 01:41thank you for the invitation and for.
• 01:44All the support that's gotten us this far.
• 01:47So what I wanted to do was talk
• 01:49about P53 mutated head neck cancer,
• 01:52which is something I have a
• 01:54longstanding interest in.
• 01:55And. Obviously, P 53 is a very
• 02:00critical tumor suppressor gene.
• 02:03It's meant to be the cells way of
• 02:05reacting to cellular stress signals,
• 02:08and among these are many that we know
• 02:11are important in head neck cancer.
• 02:14So hypoxemia, DNA damage,
• 02:16replicative stress,
• 02:17and ideally in response to these P.
• 02:2053 is activated and promotes the
• 02:22transcription of target genes and
• 02:25domains of cell cycle arrest DNA
• 02:27repair a pop ptosis and others.
• 02:29However,
• 02:30in head neck cancer were aware that
• 02:33P53 functionally disrupted in the
• 02:36majority in HPV associated head neck cancer.
• 02:40P53 is wild type, but its degradation
• 02:43is fostered by viral proteins,
• 02:46an in HPV, negative head neck cancer.
• 02:50Over 85% have genomic disruption of P53,
• 02:53including in frame mutations,
• 02:55truncating mutations and missense mutations,
• 02:57and you can see here that many of
• 03:00these are clustered in the DNA binding
• 03:03domain and we know that this type
• 03:07of mutation is Villa terius for the
• 03:10Natural History of head neck cancer.
• 03:13So this figure comes from a large trial
• 03:16that the legacy Kog Cooperative Group
• 03:18ran over 500 respected head neck cancers.
• 03:22All respected to margin,
• 03:23negativity,
• 03:24and all offered appropriate risk
• 03:26based animal therapy is with
• 03:28standard at the time and then P 50
• 03:31three was sequenced and you can
• 03:33see here that long term outcome.
• 03:36Was worse for those patients
• 03:38who had P53 mutation,
• 03:40and if you classified the mutations
• 03:42as disruptive or nondisruptive,
• 03:44it was worse for those with
• 03:46disruptive mutation and the definition
• 03:48that was used in this paper.
• 03:51That was for disruptive was a
• 03:54mutation that was either truncating
• 03:56or in the DNA binding domain.
• 03:59So on the basis of these outcome data,
• 04:02we were interested in the cognitive
• 04:05Akron Head Neck Committee,
• 04:06which I chair in studying intensification
• 04:09of therapy for these poor prognosis
• 04:11patients with disruptive P53 mutation.
• 04:14But the first thing we wanted to
• 04:17do was examined how we really
• 04:19should be calling the P53 mutation.
• 04:22So we started with what we called
• 04:24the poeta rule,
• 04:26so those were the rules from the
• 04:28paper I just showed you and we
• 04:30compared them to 14 other cloud 13
• 04:33other classifiers that are out there,
• 04:35many of which are based on in
• 04:37silico predictions of disruption,
• 04:39some of which are based on experimental
• 04:42evidence actually of the decrease in
• 04:44OIF 1 activation for every specific
• 04:46mutation and then we also examine Dar
• 04:49poeta rules augmented with information
• 04:50about the splice site mutations.
• 04:52And you can see that this very simple
• 04:55definition of truncating or DNA
• 04:57binding domain actually outperformed
• 04:59in terms of clinical prognosis.
• 05:01All of the other indicators,
• 05:03and so in our clinical trial.
• 05:06We moved forward with this poeta
• 05:09rules plus splice site mutations
• 05:11and the trial that we're now about
• 05:14halfway through
• 05:15is a randomized phase. Two trial of.
• 05:18Post operative therapy for patients
• 05:20who meet the criteria for radiation
• 05:23but have negative margins,
• 05:25don't meet the criteria for chemotherapy,
• 05:27and then we want to ask in those
• 05:30patients with disruptive mutation,
• 05:32do we see an advantage for the
• 05:35addition of platinum that we
• 05:37don't see in other patients?
• 05:39This is supported by.
• 05:41Is Bisquick grant that takes care of
• 05:44all of the sequencing and we have
• 05:47two investigators who are doing
• 05:50the mutation calling in real time.
• 05:53So continue to support this trial
• 05:55and see this is kind of an important
• 05:58resource in terms of all the sequencing
• 06:01information that we're going to
• 06:03have on top of the clinical outcome.
• 06:07We also have support for a clinical
• 06:10trials planning meeting at the NCI
• 06:12which is going to happen in January.
• 06:14The goal of this is to write trials
• 06:16both for locally advanced and
• 06:19recurrent metastatic disease,
• 06:20identifying promising therapies
• 06:21for P53 mutated cancer.
• 06:23We also want to develop a
• 06:26national infrastructure.
• 06:27For the sequencing and mutation,
• 06:29calling with the consensus approach
• 06:30that all of the groups within the
• 06:33NCT and will accept the breakout
• 06:35groups for this have been meeting
• 06:38for about five months now.
• 06:39I can tell you that the focus is
• 06:42very strong and immunotherapy and
• 06:44synthetic lethal strategies and I'll.
• 06:46I'll mention both of those in in
• 06:49the remaining minutes of this talk.
• 06:51So head neck cancer is one of the
• 06:54cancers where it appears that increase
• 06:57tumor mutation burden is predictive
• 06:59of response to immunotherapy.
• 07:01And we know that this is a cancer with
• 07:04a higher number of nonsynonymous mutations,
• 07:07particularly in the HPV negative cancers.
• 07:10And in the platinum refractory setting,
• 07:12both for Pember Lizum app in this early
• 07:15single ARM trial and for development
• 07:18in a randomized phase three trial.
• 07:21In the also in the platinum
• 07:24refractory setting,
• 07:25in both cases we see that as
• 07:28tumor mutation burden rises,
• 07:30the likelihood of benefit
• 07:32from immunotherapy increases.
• 07:34So working with my long term
• 07:36collaborator at Fox Chase Circle,
• 07:39Columbus,
• 07:39we wanted to examine whether or
• 07:42not mutations in not only P.
• 07:4453,
• 07:44which is the most common most
• 07:47commonly mutated tumor suppressor
• 07:48and head neck cancer,
• 07:50but also CDK into a which is
• 07:53mutated in slightly over half
• 07:55of HPV negative cancers as well.
• 07:57See how these related to DNA damage
• 08:00as reflected in tumor mutation
• 08:02burden with the idea of establishing
• 08:05whether or not P53 mutated cancers.
• 08:07Would be particularly susceptible or
• 08:10appropriate for study with immunotherapy.
• 08:13We had access to a data set of 1010
• 08:16HPV negative cancers that have been
• 08:19profiled at Caris Life Sciences.
• 08:21There gene panel is about a 600 gene panel.
• 08:25They exclude HPV associated cancers
• 08:27with standard methods and then the
• 08:29CDK into a mutations that we saw were
• 08:32almost invariably truncations or deletions.
• 08:35So we included all of those,
• 08:37but for P53 we were interested once
• 08:40again in what's the best way of calling?
• 08:44Meaningful mutations,
• 08:44so we started with the American College of
• 08:48Medical Genetics variants calling
• 08:50this included essentially all the
• 08:52P53 mutations that that we SPA.
• 08:55We then looked for consensus between
• 08:57the ACM G and two other variant calling
• 09:01algorithms, interference linver.
• 09:02We use the International Agency for Research
• 09:05on Cancer guidelines for what was dominant,
• 09:08negative or loss of function.
• 09:11We then looked at the variance is defined
• 09:14by the poeta rules that I just alluded to,
• 09:18and then we called out those patients who
• 09:21seem to have gain of function mutations,
• 09:24most of which are defined experimentally
• 09:26across a range of publications,
• 09:28and TMB was.
• 09:29Measured just by counting all nonsynonymous
• 09:32missense mutations across the about 1.4
• 09:35mega bases that are included in this panel.
• 09:38This shows you the the patient
• 09:41characteristics so predominantly
• 09:42oral cavity in order.
• 09:44Pharynx cancers as we see in the clinic.
• 09:47Males outnumbering females,
• 09:48and as you see at the bottom,
• 09:52the number of patients who had P53
• 09:55mutation by the karris was higher
• 09:57than if we looked at the consensus
• 10:00calls or the disruptive call gain of
• 10:04function was less than 10% of all of
• 10:07the mutations that we saw in be 53,
• 10:11and indeed it turned out that either P.
• 10:1553 or CDK into a mutation.
• 10:17Was associated with an increase in TMB.
• 10:20Here we looked for threshold of 15
• 10:22per per per megabases as being likely
• 10:25predictive of response to immunotherapy.
• 10:28And you can see that across the board
• 10:31having both genes mutated was associated
• 10:33with higher TMB than having one or the other,
• 10:37and The only exception here was that
• 10:40those patients with gain of function
• 10:42mutations in P53 did not have an
• 10:45increase in tumor mutation burden.
• 10:47So you know,
• 10:48we concluded that mutation of P53
• 10:50or CDK in two ways associated with
• 10:53increased tumor mutation burden.
• 10:55This is highest when they're damaging
• 10:58mutations in both jeans and so just to
• 11:01kind of segue to the next part of the talk,
• 11:04where I'm going to talk a little bit
• 11:06more about synthetic lethal strategies.
• 11:09P53 mutated head neck cancer, I think,
• 11:12remains a really important subject for study,
• 11:14because it's common.
• 11:16It has a poor prognosis.
• 11:18We still don't,
• 11:19after many decades of people examining this,
• 11:22have agents which directly target mutated P.
• 11:2553.
• 11:25And so the increasing evidence that
• 11:29synthetic lethal strategies might have
• 11:31promise in these patients is has kind
• 11:34of attracted our attention in in the lab.
• 11:37And so one of the things that we
• 11:41know about disruptive P53 mutation
• 11:43is that you lose the cell,
• 11:46loses the ability to perform cell
• 11:49cycle arrest at the G1 S transition,
• 11:52and as a result it becomes much more
• 11:55dependent on transition at G2 M and so
• 11:58we I mean obviously many people have
• 12:01been interested in this across many cancers,
• 12:05but we were interested in
• 12:07examining some of the.
• 12:09Potential targets that regulate G2
• 12:11M We know that auroras increasing.
• 12:13I'll show you a little bit about this.
• 12:16We know that Aurora expression
• 12:18is increased in head neck
• 12:20cancer and. Aurora content
• 12:22will go up at the end of G2.
• 12:28Its activity is required to localize
• 12:32CDK one to the to the centromere to to.
• 12:39Foster mitotic entry Aurora also,
• 12:42in addition to its roles
• 12:45in centrosome maturation.
• 12:46It also has the property of.
• 12:51Activating the city.
• 12:52See 25 phosphatase,
• 12:54which removes an inhibitory phosphorylation
• 12:56from CDK one and on the other hand,
• 13:00it's important to know that that
• 13:04inhibitory phosphorylation is
• 13:06placed by the mitotic checkpoint
• 13:08kinase we want and so both we
• 13:11won an Auror recognized is up
• 13:14regulated and head neck cancer.
• 13:16Both of them are potential.
• 13:19Points of synthetic lethality
• 13:21in P53 mutated cancers,
• 13:23but they appear to have kind of
• 13:26contradictory or opposing roles,
• 13:28and so that the data that I'm going
• 13:32to show you now will try to make
• 13:35the case that by Co treating these
• 13:38cancers with an Aurora inhibitor,
• 13:41which will lead to.
• 13:43Abnormal spindle formation.
• 13:45Defective cytokinesis,
• 13:46but by inhibiting the rural will
• 13:49lose the ability to remove the
• 13:53inhibitory phosphorylation from
• 13:54CDK one and that will result in
• 13:58cell cycle arrest that we can
• 14:00counter that by inhibition of
• 14:03we won so that phosphorylation
• 14:05isn't placed and accelerate these
• 14:08cells into mitosis where given
• 14:11the spindle disruption that's been
• 14:13caused by the Aurora inhibition.
• 14:16They will be unable to complete a normal
• 14:19mitosis and instead will apoptose
• 14:22are undergo mitotic catastrophe.
• 14:24So,
• 14:24um,
• 14:25it's been recognized that Aurora content
• 14:27is increased in the face of loss of
• 14:30P53 and their host of publications,
• 14:33which demonstrate that increased Aurora
• 14:35levels are correlated with poor prognosis.
• 14:38I'll show you some of our work.
• 14:41This is a panel of cell lines
• 14:43that that we use in the lab,
• 14:46all of which have either
• 14:47mutated or P53 null status,
• 14:49and you can see that all of them
• 14:51increase the expression of arorae
• 14:53relative to either fibroblasts
• 14:55or normal epithelial tissue.
• 14:57And when I was at Fox Chase,
• 15:00we worked on a Aqua essay and insight
• 15:03to fluorescence assay for Aurora that
• 15:06could be applied to tissue microarrays.
• 15:09And so you see here that green is
• 15:11for carrot and defines where these
• 15:14head neck cancer nests are within
• 15:17the tissue core Blues for dampit.
• 15:20So that will be your nucleus and red is
• 15:23for Aurora and in this Aurora high cancer.
• 15:27What you can particularly appreciate
• 15:29is the high level of expression
• 15:31of Aurora within the nucleus.
• 15:33When we looked at nuclear Aurora
• 15:35in the tissue microarray first
• 15:37for all cases we saw that high
• 15:40Aurora expression was associated
• 15:41with worse survival.
• 15:43This is also true,
• 15:44just is a reflection of Natural
• 15:46History in those patients who had
• 15:48had no post operative treatments
• 15:50and never been exposed.
• 15:52Any DNA damaging agents and we were
• 15:54able to show that this was entirely
• 15:57driven by the HPV negative cancers.
• 15:59So on the basis of this these
• 16:02data we went to Millennium.
• 16:04And argued that.
• 16:05Aurora could potentially be a good
• 16:08target in head and neck cancer.
• 16:11They were doing a trial of Al assertive,
• 16:14which is in Aurora a inhibitor.
• 16:17Val asserted monotherapy across
• 16:19a bunch of solid tumors,
• 16:21and we were able to convince
• 16:23them to add a head neck cohort,
• 16:26but this was crushingly disappointing
• 16:28because the response rate for Aurora
• 16:31monotherapy in in head and neck
• 16:33cancer turned out to be about 9% and
• 16:36given the increasing experimental
• 16:37evidence that are or inhibition may
• 16:40always be intrinsically limited.
• 16:42Limited by this kind of compens
• 16:44atory cell cycle arrest.
• 16:46We were then interested in what would be.
• 16:49The rational combination with
• 16:52Arorae inhibition that could.
• 16:55Optimize the targeting of what we
• 16:57continued to think was likely to be
• 17:00an important target in this disease,
• 17:03and so any Mendez and colleagues at the
• 17:06University of Washington together with
• 17:08Dell Yarbrough are former colleague
• 17:10here had undertaken a functional kind,
• 17:13ohmic screen.
• 17:14In P53,
• 17:14mutated head and neck cancer and
• 17:17actually Aurora came out of that screen.
• 17:20But another thing that came out
• 17:22was this mitotic checkpoint kinase
• 17:24that I just alluded to.
• 17:26We want and.
• 17:27People have been interested in
• 17:29the idea that inhibitors of G1
• 17:31will abrogate the G2 checkpoint.
• 17:34You have.
• 17:34The G1 checkpoint is already
• 17:36advocated by P53 mutation and that
• 17:39this might accelerate cell death,
• 17:41particularly in the presence of DNA
• 17:43damage such as you might generate with
• 17:46cisplatin and they showed in animal
• 17:48models that we one inhibitor MK 1775,
• 17:51which is now known as the data sorted,
• 17:54was synergistic with platinum in
• 17:56P53 mutated head neck cancer models.
• 17:59Eddie Mendez then took this forward as
• 18:01a window trial and head neck cancer,
• 18:04so a small number of patients treated
• 18:06with a DAB assertive together with
• 18:08low dose weekly chemotherapy.
• 18:10And you can see here that the majority
• 18:13of patients had some diminution in
• 18:15tumor size and a number of them had
• 18:19rather major pathologic responses,
• 18:21and most intriguingly,
• 18:22you can see that there was evidence
• 18:24of target engagement,
• 18:26and so among those patients who
• 18:28had responses,
• 18:29there was a decrease in
• 18:31phosphorylation of CDK.
• 18:33There was a decrease in
• 18:36fast focus Stone Age 3.
• 18:39And potentially you could see
• 18:42some increase in gamma, H2, ax.
• 18:45They also were able to correlate
• 18:48both pathologic and clinical
• 18:51response with the presence of P53
• 18:54mutation in the HPV negative cancers
• 18:58and across the board.
• 19:00These P53 mutations are
• 19:03disruptive or deletions.
• 19:05So we've been exploring whether or
• 19:07not you can combine Aurora A and we
• 19:11want inhibition and observe synergy
• 19:13in P53 mutated head neck cancer,
• 19:15and you see here a picture of John Wooley,
• 19:19my colleague in the lamp,
• 19:21who has done the majority of these
• 19:24experiments and so you'll see MLN,
• 19:27that's the Aurora inhibitor,
• 19:29Azd 1775 that's the wee one
• 19:31inhibitor and we see synergy.
• 19:34In terms of cell viability,
• 19:36soft auger oncosphere formation and
• 19:39this was present in two separate
• 19:42HPV negative head neck cancer cell
• 19:44lines that bear P53 mutations.
• 19:47Trying to figure out whether or
• 19:50not our our guests
• 19:52about the mechanism was correct.
• 19:55You can see here that when you give the.
• 19:59Aurora inhibitor there's a dramatic
• 20:02increase in phosphorylation of CDK one.
• 20:04This happens in a slightly different
• 20:07timeline in the two South and
• 20:09the two different cell lines,
• 20:11but seems to be a reproducible phenomenon,
• 20:14and that's abrogated by the addition
• 20:17of the wee one inhibitor and completely
• 20:20abolished when you gives it to together.
• 20:23This results in an increase in the number
• 20:26of mitotic figures that's abnormal to the.
• 20:30The. Presence of of really only
• 20:33single digit normal mitotic figures
• 20:35in the presence of the combination.
• 20:38So if you just walk through here,
• 20:40these are the normal mitotic figures.
• 20:42When you give the wee one inhibitor,
• 20:45you get some dis aggregation of
• 20:47chromatin reflected here in the
• 20:49fast food Stone Age 3 stain.
• 20:51When you give the Aurora inhibitor,
• 20:53you get the formation of these multipolar
• 20:55spindles three to four spindles,
• 20:57Purcell, and when you give the
• 21:00two together you get a, uh.
• 21:02Abnormal catastrophic mitotic figure.
• 21:06We also showed in using Annexin 5
• 21:09flow and looking for cleaved PARP that
• 21:13there's an increase in a pop ptosis.
• 21:17And we wanted to compare this
• 21:20to Aurora B inhibition,
• 21:21which completely cuts off mitotic entry
• 21:25by aggregating the phosphorylation
• 21:27of histone H3 and there was no
• 21:30synergy between these two agents
• 21:32and and you can see there.
• 21:34The lack of fastball, histone H3,
• 21:38an increase in DNA damage.
• 21:42Taking this into xenograft models here at
• 21:45either of two doses of the wee one inhibitor,
• 21:49the standard dose of the Aurora
• 21:51inhibitor tumors continued to grow
• 21:53not too differently from vehicle,
• 21:56but when we gave the two together,
• 21:59there was control of tumor growth and
• 22:02actually statistically significant
• 22:04improvement in survival for the animals.
• 22:06Looking at the tumors under the microscope
• 22:09when we gave the two agents together,
• 22:12there was a decrease in proliferation
• 22:14reflected in decreased Ki 67,
• 22:16there was increased cleaved caspase and
• 22:19there was a decrease in fast for CDK,
• 22:22one within tissue and if we did
• 22:24Aquaphor phospho CDK one and
• 22:26counted the amount of phosphorus,
• 22:29IDK one signal in the tumor leading edge.
• 22:32You can see this was dramatically decreased.
• 22:35Ellisor to has been a difficult
• 22:38drug to work within the clinic.
• 22:40It's associated with Mila suppression,
• 22:42and there's been a negative
• 22:44phase three monotherapy trial,
• 22:45and lymphoma,
• 22:46and so we were concerned that the development
• 22:49of that agent might not go forward.
• 22:52However,
• 22:52there's been a number of 2nd generation
• 22:55or or inhibitors that have come
• 22:57forward and we've had access to a
• 23:00compound from Taiho called task 119
• 23:02recently been acquired by Bit Track.
• 23:05And it's gonna be called Vic 1911
• 23:07moving forward and once again across a
• 23:10range of P53 mutated cell lines we see
• 23:14dramatic synergy for the two agents.
• 23:16Once again,
• 23:17we see synergy in xenograft models.
• 23:20This is confocal microscopy that
• 23:23again shows you the multipolar
• 23:27spindle formation with the use of
• 23:30task 119 is the
• 23:33Aurora inhibitor.
• 23:34But with the cells really arresting in
• 23:37that or becoming quiet sent in that
• 23:40multipolar spindle state an as they
• 23:42then attempt to enter mitosis in the
• 23:45in the presence of both the wee one
• 23:48inhibitor and the Aurora inhibitor,
• 23:50developing these very catastrophic
• 23:51mitotic phenotypes, an notice that
• 23:53I'm sort of running out of time here,
• 23:56so I won't March you through this,
• 23:59but the mechanism looks to be identical here,
• 24:02as what we saw with assertive.
• 24:05I'm working with our Columbus
• 24:07is Lambert at Fox Chase.
• 24:08We undertook a high throughput
• 24:10screen to see if we could find
• 24:13additional partners that would be.
• 24:15Both hindering and an fostering mitotic
• 24:18entry again with the attempt to exploit
• 24:21these multiple regulators of G2,
• 24:23M and another hit that appeared
• 24:26very strong was the check one
• 24:29inhibitor Prexasertib agent.
• 24:30It's not really moving forward in
• 24:33the clinic because of its toxicity,
• 24:36but I wanted to show this just
• 24:39because with very low dose Ng and
• 24:42a single dose we saw a profound.
• 24:45Energetic survival effects that make us
• 24:47hopeful that with a number of these pairs,
• 24:49we might be able to go to very
• 24:52low doses in the clinic.
• 24:54So test 119 has completed
• 24:56two clinical trials.
• 24:57There's a recommended phase two dose.
• 25:00The toxicity seems to be very manageable
• 25:03with diarrhea and eye disorders.
• 25:06Probably the prominent most
• 25:08prominent side effects,
• 25:09and so we are moving forward
• 25:12with a window trial in HPV,
• 25:15negative head neck cancer that will
• 25:17have both an initial dose escalation.
• 25:20Looking at the combination
• 25:22of Vic and DAB assertive.
• 25:25And followed by a dose expansion and
• 25:27that will be part of Project two of
• 25:29our head next 4 so I wanted to leave
• 25:32a couple of minutes for questions,
• 25:35but I didn't want to end without
• 25:37first of all calling out all of
• 25:39the fabulous colleagues who were
• 25:41part of the team that they put
• 25:44the head and explore in and then
• 25:46acknowledging all the people whose
• 25:47work I've just talked about,
• 25:49particularly John Wooley Jannike Parameshwar
• 25:51on in Teresa Sandoval Schaefer in the lamp.
• 25:54So thank you very much.
• 25:55Barbara,
• 25:56thank you.
• 25:56That's fabulous work.
• 25:58Congratulations on all of it and
• 26:00and folks can submit questions on
• 26:03the on the chat box of of the zoom,
• 26:06but I wanted to ask, you know,
• 26:09with regard as you look through
• 26:11the combination of an Aurora
• 26:13kinase and we want inhibitors,
• 26:15do you have a sense of 1st what might emerge?
• 26:19I'm even when you're getting response
• 26:21because of the complexity of those pathways.
• 26:24What might emerges mechanism?
• 26:25Resistance that will occur when
• 26:28you have dual inhibition of any and
• 26:30then the second question I have is
• 26:33what do you anticipate will be the
• 26:35toxicity profile or the therapeutic
• 26:36therapeutic window for the combination
• 26:38clinically. So the we want inhibitors
• 26:40been quite tolerable 'cause I'm
• 26:43going to take the second question
• 26:45first 'cause I've already wrestled
• 26:46with X and a lot that we want.
• 26:49Inhibitors been quite tolerable
• 26:50in the clinic but when it was
• 26:52combined with PARP inhibition,
• 26:54diarrhea really became the dose limiting.
• 26:56Side effect, and so this second
• 26:59generation Aurora inhibitor did have
• 27:01about a 25% rate of high grade diarrhea
• 27:04at the recommended phase two dose.
• 27:07So the two things that were
• 27:10sort of hoping is 1.
• 27:12That will get away with lower doses as we
• 27:15have in the animal models and second of all,
• 27:19the diarrhea as it dose limiting
• 27:21toxicity is one of the easier ones to
• 27:24manage and so that if we're on top of
• 27:27this with an Imodium regimen early on,
• 27:29hopefully that will be helpful in terms
• 27:32of resistance mechanisms with this
• 27:34is not something that we've really
• 27:35gone into with the combination yet,
• 27:38but is well studied for both of the
• 27:41agents independently and one of the.
• 27:43Resistance mechanisms to the Aurora
• 27:45agents has been a kind of conformational
• 27:48dependence on the inhibitor,
• 27:50so inhibitor binds to the activated
• 27:53form of Aurora A and if you get a
• 27:56an adaptive process where the cell
• 27:59just generates more inactive Aurora,
• 28:02the current generation of inhibitors may
• 28:05not work as well and there is a group.
• 28:09Kevan Shokat lab has been developing
• 28:11novel Aurora inhibitors that maybe.
• 28:14More able to bind the inactive
• 28:17confirmation as well. And in terms
• 28:21of we want inhibitors there there is.
• 28:28Suggestions that? The the.
• 28:36Do you need damage effects of?
• 28:41That we want inhibitors may have an S phase.
• 28:45Could actually upregulate some checkpoints
• 28:49that are earlier in the cell cycle.
• 28:53But it's a good question.
• 28:55Probably something we should
• 28:56devote more effort to.
• 28:57Yeah, well, I'm sure it'll
• 28:59it'll definitely emerge.
• 29:01Emerge as you get samples from your trial.
• 29:04So it's really exciting.
• 29:06And congratulations so I know we're at 12:31.
• 29:09Wherever so why don't we
• 29:11will turn out to people?
• 29:13Can submit questions to Barbara Online,
• 29:15but will turn now to our second speaker
• 29:18and very fortunate to have another
• 29:21valued member of our faculty speaking.
• 29:23Doctor Elizabeth Klaus is a professor
• 29:26of Biostatistics and neurosurgery.
• 29:28Focused not only on brain tumors
• 29:30but also the Epidemiology,
• 29:32most notably the genetic
• 29:34Epidemiology of these malignancies.
• 29:35She received her MD and PhD from Yale
• 29:39and completed her surgery here in
• 29:41their surgery and through her work
• 29:44she really has been an international
• 29:47leader in in the investigation of
• 29:49the Epidemiology of CNS Malignancy's,
• 29:52most notably serving as the leader
• 29:54of the Meningioma consortium,
• 29:56that meningioma Genome Wide
• 29:58Association study.
• 29:59Also,
• 29:59a leader of the AL Acoustic neuroma study,
• 30:03and we again were so pleased to have
• 30:06talented people who bridge the gap
• 30:08of Epidemiology in biology of cancer
• 30:11and Elizabeth thank you so much for
• 30:14sharing your work with us today.
• 30:17Thanks very much.
• 30:18Can you see my slides?
• 30:20OK,
• 30:20yes great.
• 30:21So I'm going to
• 30:22talk a little bit about something
• 30:24we've been working on an I do want
• 30:26to note that this is work done in
• 30:29collaboration with Jeff Townsend's Group
• 30:30who I think you all know very well.
• 30:33And Vincent Kenna Tarot as
• 30:35well as Steven Gaffney.
• 30:36So despite all the things
• 30:37that we've attempted to do,
• 30:39we still don't know much about
• 30:41risk factors for glioma.
• 30:42And we wanted to take a look
• 30:44and see if they were different
• 30:46methods that we could use.
• 30:48To see if we could tease out
• 30:50both environmental and then also
• 30:52another hot topic is sex specific
• 30:54signatures of glioma causation.
• 30:56So you all know that gliomas are the most
• 31:00common type of malignant brain tumor,
• 31:02accounting for about 1/3 of all brain tumors,
• 31:05and the majority of malignant tumors.
• 31:07But they proved to be very
• 31:09heterogeneous and we have not done a
• 31:12great job identifying risk factors,
• 31:14be they genetic or environmental for glioma.
• 31:17And so we were interested in doing that,
• 31:19particularly in light of the poor outcomes
• 31:22that we see with this group of patients.
• 31:24So we do know that there are sex specific
• 31:28differences in glioma risk and outcome and
• 31:31the plots I have here are for all gliomas,
• 31:35an then glioblastoma or
• 31:36sort of an IDH positive.
• 31:38Excuse me,
• 31:39IDH negative tumor and then
• 31:42lower grade gliomas.
• 31:43The males being the blue,
• 31:45the females being the red.
• 31:47And it's interesting in that we
• 31:49see this sex specific difference
• 31:51across the entire age range,
• 31:53so it's a little bit different
• 31:55than we see with,
• 31:57for example,
• 31:57meningiomas where we see the
• 31:59women having greater risk,
• 32:01but the risk difference decreasing once
• 32:03women passed through the menopause.
• 32:05Whereas here we see the sex
• 32:07differences for glioma across the
• 32:09age spectrum and across all subtypes,
• 32:11and so that obviously suggests
• 32:13that other mechanisms in addition
• 32:15to a good sex hormones must be.
• 32:17Are behind the difference men are
• 32:19at greater risk of being diagnosed
• 32:21with the disease.
• 32:22And again,
• 32:23that's across pretty much all
• 32:25the subtypes and they also have
• 32:27lower survival in general then
• 32:29for females across all subtypes.
• 32:31So we've looked at this a little
• 32:33bit and I've been lucky enough
• 32:35to collaborate with a group of
• 32:38individuals called the glioma.
• 32:40International Case Controls Consortium,
• 32:42and that's led by Melissa Bondy,
• 32:44initially at MD Anderson, then it Baylor.
• 32:47Now she heads up the Epidemiology
• 32:49section at Stanford,
• 32:51but we were able to gather over
• 32:5310,000 cases and 10,000 controls,
• 32:55and so these are essentially looking
• 32:58at constitutional or germline
• 33:00risk alleles by sex.
• 33:01So if I can draw your attention
• 33:04to the table over here,
• 33:05these are the variants that we
• 33:07found to be significantly different
• 33:09males versus females.
• 33:10Males being the blue,
• 33:12an females being the red.
• 33:13So we did certainly find differences
• 33:15at the germline level,
• 33:17but we were also interested in
• 33:19looking at things at the tumor
• 33:21or their cinematic level.
• 33:220 Sex is a biologic variables I mentioned.
• 33:25This is a very hot topic.
• 33:27Now we obviously know there are biologic
• 33:30differences between males and females.
• 33:31There's also some thought as to
• 33:33whether there's variation in
• 33:35the prevalence of risk factors,
• 33:36and then also whether there's
• 33:38a difference in sort of a gene
• 33:40by environmental interaction.
• 33:41So, for example,
• 33:42and this has long been postulated,
• 33:44but it's really been pretty difficult
• 33:46to prove that males in particular are
• 33:49more likely to be exposed to work like
• 33:51toxins that might be associated with risk,
• 33:54and so that was one of the things
• 33:57we wanted to look at as well.
• 33:59And in part, why we divided our analysis.
• 34:02By sex.
• 34:03So there's two goals and what
• 34:05I'm going to talk about today.
• 34:08We wanted to look at the relative
• 34:10contribution and this is based on
• 34:12some of the work that I know you've
• 34:15already appreciated with Jeff Townsend,
• 34:18but we're applying it specifically
• 34:19now to glioma,
• 34:21but looking at the relative
• 34:22contribution of cancer cell lineages,
• 34:24proliferation and survival of
• 34:26single nucleotide mutations,
• 34:27and we divided our study subjects
• 34:29up by IDH mutation.
• 34:31And, as most of you know,
• 34:33IDH mutation is one of the
• 34:35key dividers into the.
• 34:37The higher in the lower grade tumors,
• 34:39and certainly a prognostic factor,
• 34:41as well as a factor in response to treatment.
• 34:44We also wanted to quantify,
• 34:45and this is something that is a
• 34:48little bit new to Epidemiology
• 34:49in terms of how we've tried to
• 34:52identify risk exposures.
• 34:53Typically we've done things like
• 34:55large case control studies where
• 34:57we look at large numbers of people
• 34:59that have the disease,
• 35:00compare them to large numbers
• 35:02of people without the disease,
• 35:04and look at things like questionnaire
• 35:06or work pic.
• 35:07Exposure and see if we can figure out
• 35:10differences between the cases and controls.
• 35:12So what we're doing now,
• 35:14and this is sort of an emerging
• 35:16field in cancer Epidemiology,
• 35:17is to look at the cosmic cancer
• 35:20mutational signatures in tumors and
• 35:22see if we can then backtrack match
• 35:24it to possible risk exposures,
• 35:26and one of the things we're hoping
• 35:28to do in the future is to go back to
• 35:31our cohorts and studies for which
• 35:33we collected good occupational data
• 35:35and see if we can match it up to.
• 35:39Mutational signatures So the methods
• 35:42I'll talk a little bit about.
• 35:44I am highlighting here.
• 35:46Jeff's paper that he had in J&CI
• 35:48two years ago,
• 35:49and I think you've seen some of
• 35:51these sorts of methods applied,
• 35:53in particular to actually head
• 35:55and neck cancer.
• 35:56So the Cancer Genome Atlas,
• 35:59and others, including the glioma
• 36:01Longitudinal Analysis Consortium,
• 36:03or Glass, which is led by roll.
• 36:06Their Hokage,
• 36:07Jackson Labs and which I'm also a member of.
• 36:11So these groups have identified
• 36:14the most common genetic changes in
• 36:17primary glioma tumors including TP 53,
• 36:20IDH,
• 36:20EGFR with the relative importance
• 36:22of these mutations and how they
• 36:25relate to tumorigenesis.
• 36:27Is not well known,
• 36:28so one of the things that we've
• 36:31been working on,
• 36:32and Jeff has been a leader in
• 36:35is defining this
• 36:36cancer affect size.
• 36:37So this metric of the relative
• 36:40overabundance of variance due to
• 36:42their contributions to survival,
• 36:43indivision versus what you're
• 36:45actually seeing in the tumor.
• 36:47So we're quantifying the cancer affect size.
• 36:50We're using single nucleotide mutations,
• 36:52and then we basically do a scaled
• 36:55selection coefficient for the for the
• 36:57different variants we look at it.
• 36:59By sex and by IDH subtype.
• 37:01And so we're trying to get a feel for
• 37:04whether this would help us explain
• 37:06any differences in the glioma,
• 37:08risk and outcome that we see by sex.
• 37:11And then we're going to move on to
• 37:14the cosmic mutations so I won't go
• 37:17into the gory statistical detail.
• 37:19This is drawn from Jeffs paper,
• 37:21but basically you're comparing
• 37:22expected to observed so expected
• 37:24number of synonymous mutations,
• 37:25and then we're looking at the
• 37:27rate at which the mutations.
• 37:29Actually occur.
• 37:30The data that we're using here,
• 37:33our whole exome sequencing data from a pretty
• 37:36good size data set in terms of glioma,
• 37:38so about 1100 and these are
• 37:40all adult patients.
• 37:41There's no pediatric patients
• 37:42in here and we drew it from the
• 37:45Cancer Genome Atlas study.
• 37:46And as I mentioned,
• 37:47I know some of you may be aware of
• 37:50what glasses, so it's an effort.
• 37:52As I mentioned led by role Verhaag,
• 37:54but which yell is also a member of
• 37:57looking at not only the initial tumors,
• 37:59but the humours overtime.
• 38:01So how do they change?
• 38:02In terms of their genetic makeup,
• 38:04when we do nothing to them when we
• 38:07do chemotherapy or we do radiation,
• 38:08or a combination of all the above and
• 38:11what changes do we see and what do
• 38:14we learn from that in terms of what
• 38:16we should or should not be doing?
• 38:18And then we also used a lot of data.
• 38:21All of this is readily available
• 38:23off the Internet,
• 38:24but we use tissue specific mutational
• 38:26covariance and this helped.
• 38:27Just figure out what sort of mutation
• 38:29rate calculations we should use.
• 38:31Gave us a little bit of information
• 38:33about replication timing.
• 38:34And some of the other datasets
• 38:36that are listed here.
• 38:37So here's some of the results.
• 38:39Just to take you through it a little bit.
• 38:42So I have a divided by tumor type
• 38:45and it's by seksan by mutation.
• 38:47So the wild type tumors who would
• 38:49be considered the higher grade are
• 38:52primarily the glioblastoma tumors are
• 38:54in the first 2 rows and the IDH mutant,
• 38:56which would more typically
• 38:58be the lower grade tumors.
• 39:00And then I have males versus females,
• 39:02males versus females,
• 39:03and then there's sort of a
• 39:05cancer affect size here.
• 39:07The blue is non coding region.
• 39:09And the red is coding so you
• 39:10can see the patterns are quite
• 39:12different for what might be called
• 39:15the low and the high grade,
• 39:17the IDH mutant tumors had few
• 39:19unique recurrent substitutions.
• 39:20All of them were in coding regions,
• 39:23whereas the wild type tumors,
• 39:24and obviously this is in part what
• 39:26makes them so hard to manage is
• 39:29they exhibited many substitutions,
• 39:30but they were primarily
• 39:32in non coding regions.
• 39:34So here's another picture.
• 39:35A little busy but divided once again,
• 39:38the IDH mutant or the lower grade
• 39:41tumors are presented first.
• 39:43The wild types are second,
• 39:45and there's female male, female, male,
• 39:47and So what we're looking at here is that.
• 39:51Items that top the list
• 39:53are the most important.
• 39:55The size of the circle that is attached
• 39:57to them measures the prevalence
• 39:59so there can be kind of this.
• 40:02Disconnect as to what is important
• 40:05and how frequently it occurs so
• 40:07we can see that in the low grades
• 40:10it's pretty much as expected.
• 40:12Previously reported mutations
• 40:13in IDH one and two TP 53.
• 40:15Some of the other classics were confirmed,
• 40:18but what's interesting is
• 40:20if we go here to the IDH.
• 40:22Wild type tumors the most important
• 40:25with respect to cancer affect
• 40:27gene is this low prevalence right?
• 40:29You can see that the circle
• 40:31that matches up to it is small,
• 40:34not large like we see for IDH.
• 40:37Is this B RAF V 600 E so we
• 40:40know that it's important.
• 40:42It turns out that it looks
• 40:44like it's the most important,
• 40:46but obviously it doesn't occur
• 40:48that frequently but interesting.
• 40:50What drives some of these gliomas here?
• 40:53The other thing we looked at is do
• 40:55males and females show the same pattern
• 40:57of what significantly overburdened,
• 40:59and there were a lot of similarities
• 41:02the way that we have this broken
• 41:04up here is each panel is a gene.
• 41:07The mutants come first in each
• 41:09panel and then within each panel
• 41:12we've got the females in the mails.
• 41:15So we did see some differences,
• 41:17although overall most the things the
• 41:19males and females showed were similar,
• 41:21but we did see differences in the P3K
• 41:24pathway, so an IDH mutant, tumors the PK.
• 41:27Three CA mutations were located in
• 41:30the helical domain for females,
• 41:32and the kinase domain for the males,
• 41:34and so that's appear.
• 41:36This panel here.
• 41:38OK,
• 41:38so it's the mutant and non mutant
• 41:40and then the variance of import
• 41:43also differed by sex for PK3R1
• 41:45and so that's interesting in part
• 41:48because we know that the way in
• 41:50which these areas are targeted by
• 41:52various chemotherapies does differ.
• 41:54We looked in the literature an we
• 41:57don't see too much reported honest.
• 41:59We did find a paper by Dan Cahill
• 42:02at all at mass general and although
• 42:05they didn't report it as such,
• 42:08they found something similar
• 42:09where the females tended to have.
• 42:11Variations in the he local domain and
• 42:15the males had them in the kinase domain,
• 42:19and so as I said,
• 42:21although both domains are
• 42:23involved with glioma Genesis,
• 42:25there is differential amounts of
• 42:28potentiated by these two regions.
• 42:30And obviously there's different
• 42:32sensitivity to various treatment
• 42:35types depending upon domain.
• 42:37So back to environmental exposure.
• 42:39We have searched and not just our
• 42:41group of many groups have searched
• 42:43long and hard for environmental and
• 42:46genetic risk factors for glioma.
• 42:48In terms of genetic risk factors,
• 42:50we have found small numbers of families
• 42:53with high risk but typically that does
• 42:55not relate to the general population
• 42:58and so no genetic risk factors
• 43:00really explain a large proportion of
• 43:02inherited risk and other than high dose
• 43:05radiation to which not many people.
• 43:07Thankfully are exposed.
• 43:08We really haven't found much of an
• 43:11Association between environmental
• 43:13risk factors in glioma risk.
• 43:15There has been reported a fairly
• 43:18consistent but low effect,
• 43:20an inverse Association with
• 43:22history of allergy.
• 43:23So the question comes,
• 43:25why haven't we found anything?
• 43:27Is it that there is no Association?
• 43:29Or is it basically statistical power
• 43:31that there's so few cases of glioma
• 43:34relative to other things we've looked at?
• 43:36For example,
• 43:37I started my work with breast
• 43:39cancer and even just using the
• 43:42state of Connecticut as a base,
• 43:44you would have enough cases
• 43:45for a large study for glioma.
• 43:48That is not true and also likely a
• 43:50lot of the exposures that we think
• 43:53are causing risk are themselves rare.
• 43:56So one of the things that people
• 43:58have been thinking about doing,
• 44:00is there another way to do this now?
• 44:03So now that we have these mutational
• 44:06signatures that the are listed in
• 44:09the Catalogue of Somatic mutations
• 44:11and cancer or cosmic can use that
• 44:13as a way to match up to exposure,
• 44:16particularly if you have previously
• 44:18obtained environmental or other
• 44:20exposure history in the patients.
• 44:21So we did that here with the
• 44:241100 cases that we mentioned,
• 44:26we group Jack Sonic.
• 44:27SNV and tried to match him
• 44:30up to what is in cosmic,
• 44:32and so you know that the cosmic
• 44:34catalog is rapidly changing.
• 44:36New things are always being added,
• 44:38but we looked at what existed at this point,
• 44:41and obviously they have previously found
• 44:43a match up over environmental exposure to
• 44:46signatures not only in head and neck cancer,
• 44:49but smoking and lung cancer,
• 44:50UV exposure,
• 44:51and so we looked at that for glioma.
• 44:55And so again,
• 44:56here's our slide here again,
• 44:58broken into IDH Mutant,
• 45:00which is the top row IDH,
• 45:03Wildtype bottom row and then females
• 45:06or first column mails or second
• 45:09column with each of these bar charts
• 45:12relates to is the proportion of our
• 45:15cases for whom the majority seem to be
• 45:18associated with a certain signature.
• 45:21And the overall news is a little
• 45:24bit depressing in the sense that
• 45:26the primary molecular signature
• 45:28identified was age related mutagenesis.
• 45:30Basically the older you get,
• 45:32the more at risk you are,
• 45:35but we did find one thing that
• 45:37was quite interesting,
• 45:39particularly in light of their such
• 45:41a positive risk factors identified
• 45:43for glioma and that was occupational
• 45:45exposure to something called Halo alkanes.
• 45:48Pretty much true across whether
• 45:50you are male or female.
• 45:52And whether you were IDH,
• 45:54mutant or not,
• 45:55we did find a little greater rate
• 45:58of the signature showing up in
• 46:00the males versus the females.
• 46:02But we certainly saw them in both
• 46:04and then we also saw which we
• 46:07haven't quite figured out how to
• 46:09explain yet. These UV light signatures an
• 46:12it's interesting because glioma has been
• 46:15associated in the number of instances
• 46:17with Melanoma and also with the B RAF.
• 46:20So we're trying to sort out whether that.
• 46:23Has anything to do with why we're
• 46:25just seeing some of those signatures?
• 46:27So hello, alkanes are basically used for
• 46:30many industrial and day-to-day purposes.
• 46:33Of interest there seen in refrigerants,
• 46:36fire extinguishers, flame retardants,
• 46:37and we thought this was very interesting
• 46:41because there's always sort of been
• 46:43this theory that in some of these.
• 46:46Occupations including for firemen
• 46:47and that has been reported that
• 46:50there is an increased risk of glioma,
• 46:52and so, whether or not this ties
• 46:55things together or not is unclear,
• 46:58so the signature was basically developed
• 47:00by looking at cholangio carcinoma in
• 47:02a group of workers that were exposed,
• 47:05known, exposed to hello Alkins in
• 47:07Japan and so essentially they had
• 47:10111 workers that were exposed.
• 47:1217 developed.
• 47:13Would you all know to be a pretty rare?
• 47:16Cancer,
• 47:16so it was quite unusual that this number
• 47:19of individuals was diagnosed with it.
• 47:22They all were working in printing
• 47:24companies and they all were known
• 47:26to have occupational exposure and so
• 47:28essentially what they did was they took
• 47:31the tumors from these individuals,
• 47:33looked at the molecular.
• 47:34Pattern and developed this signature.
• 47:37So that's essentially how that the
• 47:39signature was initially identified,
• 47:41and so that's what we're seeing.
• 47:44Basically in our data.
• 47:46So conclusions here that the majority
• 47:48of cancer causing mutations in these
• 47:51gliomas we're seeing primarily as
• 47:53a consequence of endogenous,
• 47:55rather than actual, exogenous exposures.
• 47:57We did think was interesting that
• 48:00different domains of jeans in the P3K
• 48:03pathway were different for males and females.
• 48:06For those of us that have searched
• 48:08long and hard for some of these
• 48:11risk factors for glioma,
• 48:13we are excited that at least potentially,
• 48:15there's a new means to try
• 48:17and identify even if rare,
• 48:19these environmental risk factors
• 48:21and it's sort of a whole new aspect
• 48:24of glioma that were looking at so
• 48:26some of our future directions.
• 48:28We're looking now to partner with
• 48:30colleagues who have worked with us
• 48:33both in the meningioma consortia man,
• 48:35the Glioma international Case
• 48:36Control Consortium.
• 48:37And we also in our international
• 48:39low grade glioma registry.
• 48:41Looking at cohorts in which we
• 48:43have a good occupational history.
• 48:46So the San Francisco Bay Area
• 48:48Adult Glioma study,
• 48:49which is led by Margaret Wrench
• 48:52and John Winky,
• 48:53they collected extremely detailed
• 48:55occupational history for their cohort,
• 48:57and they have all the tumors.
• 48:59So we're going to try and go back and
• 49:02Geno type those tumors and see if
• 49:05we can confirm these associations,
• 49:08which they found with firefighters.
• 49:10And glioma.
• 49:11And also they found it with painters as well.
• 49:14And so we are also collecting glioma
• 49:17patients with occupational histories
• 49:19and just sort of throwing it out to people.
• 49:22If you're aware of any firefighters
• 49:24or similar occupied individuals with
• 49:26glioma would love to try and get
• 49:29a cohort
• 49:29together. The other thing that was
• 49:32just sort of luck this past semester.
• 49:34So I teach over at the school,
• 49:37public health and everything has been remote.
• 49:40And so as I was meeting via zoom with
• 49:43one of my students for her final project,
• 49:46she revealed that she was actually
• 49:49the principle project director for
• 49:51the Firefighters Cancer Cohort study.
• 49:53So we're also hoping to parano.
• 49:55NIH is a big directive to try and
• 49:57look further at environmental
• 49:59exposures and cancer,
• 50:01so we're hoping that we can partner with
• 50:03some of these folks to look at individuals,
• 50:07either living or dead that
• 50:09may have undiagnosed.
• 50:10With glioma that we now have this exposure.
• 50:13So thank you all for your time.
• 50:16I wanted to also thank Jeff Townsend,
• 50:18Vinston, Canna Terra,
• 50:19who was a postdoc in Jeffs lab but now as an
• 50:23assistant professor of biology up the road,
• 50:26a little bit of Emmanuel
• 50:27College and I have to thank him.
• 50:30He made all the beautiful
• 50:32pictures an Steven Gaffney,
• 50:33who also works in Jeff Slab.
• 50:35Thank the various.
• 50:37Brain tumor associations,
• 50:38including the ABCA in the
• 50:40NBTS as well as Luglio Anna,
• 50:42a Dutch group called Stop Brain
• 50:44Tumor for their support and then
• 50:46also thank you for Doctor Rolled
• 50:49their hacking the Glass consortium
• 50:50who allowed us access to the data.
• 50:53So happy to take any questions.
• 50:57Elizabeth, thank you.
• 50:58That was a terrific summary of your
• 51:01work and obviously will open it
• 51:03up to questions on the chat line.
• 51:06But let me ask.
• 51:07I found it interesting the the
• 51:09observation I guess from Asia
• 51:11about the Association of Halo
• 51:13alkanes with cholangiocarcinoma.
• 51:15As you may know,
• 51:17there's there's a biologic
• 51:18difference between intrahepatic,
• 51:20an extra paddock cholangio,
• 51:22where extra panic actually gave
• 51:25IDH mutations but insured don't
• 51:27wear the cases that they found
• 51:30in Asia with a extra paddock.
• 51:32You
• 51:33know, I don't know the answer to that.
• 51:35I gave a similar talk at UCSF
• 51:37and they mentioned this as well,
• 51:39so we're trying to gain access
• 51:40to some of that information,
• 51:42but I don't know at present.
• 51:45And then. With regard to the finding of
• 51:48the potential differential in mutations
• 51:50within pick three CA by gender by sex,
• 51:53is there an understanding of
• 51:55why those two domains would be
• 51:56different between men and women?
• 51:58No, and you know,
• 51:59we started to look at that a
• 52:02little bit and we collaborate
• 52:03a bit with Dan Cahill.
• 52:05As I mentioned up at mass
• 52:07general so we don't know yet,
• 52:09but he's going to try to take a look
• 52:11into that he he presented the data
• 52:13but didn't note the differences,
• 52:15so he's going to try to take
• 52:17a look and see what that.
• 52:19Might entail.
• 52:21And then my last question,
• 52:23and this is gonna show my naivete
• 52:25and understanding brain tumors.
• 52:26But instead of the Natural
• 52:28History of the low grades.
• 52:30Is there an evolution of the
• 52:33semantic events such that they
• 52:36look more like high grades?
• 52:38So it depends.
• 52:39They generally remain quite different.
• 52:41The IDH mutation stays constant
• 52:43throughout and so that's sort of
• 52:45been one of the issues is what
• 52:47you show up to the party with
• 52:49tends to be what you stay within.
• 52:51That makes it a little bit
• 52:53different to manage them.
• 52:55We didn't found find in some of
• 52:57the glass consortium work that
• 52:58we've looked at that really things
• 53:00changed that much whether you
• 53:02gave them treatment or whether
• 53:04you didn't give them treatment.
• 53:05Is a little bit disheartening,
• 53:07but we're going to try to look a
• 53:10little bit further at that.
• 53:11Yeah, yeah, you know,
• 53:13judging by the way you describe
• 53:15for the presence of Halo alkanes,
• 53:17you could imagine they
• 53:19may be more ubiquitous.
• 53:21In our environment than we might
• 53:22otherwise appreciate given
• 53:23all the things they are in
• 53:25absolutely and it doesn't
• 53:26have to just relate to glioma.
• 53:28You know could relate to
• 53:29lots of different things so.
• 53:32Well, very interesting.
• 53:33You know we're just about out of time.
• 53:36An really appreciate.
• 53:37Oh actually, JoJo contest
• 53:39hasn't question, forgive me.
• 53:41So Joe's question is high dose
• 53:43radiation therapy delivered to
• 53:45pediatric patients can lead to glioma?
• 53:47Have you found evidence that medical
• 53:50imaging and radiation exposure in
• 53:52this setting is associated? So
• 53:54there's actually, and you probably
• 53:56even know of these two studies.
• 53:58There's a big cohort from Australia as
• 54:01well as a second cohort from England,
• 54:04and they did find that even exposure to
• 54:07head CT's at an early age in children
• 54:10was associated with the I mean.
• 54:12It's a very small increase in risk,
• 54:15but a definite increase in risk
• 54:17of both glioma and meningioma,
• 54:19and then anything we looked at
• 54:21we did find it was a fairly hotly
• 54:24contested topic we did find.
• 54:26And exposure to bite wings was associated
• 54:29with an increased risk of meningioma,
• 54:31but that's sort of exposure
• 54:33level in terms of dental X,
• 54:35Rays generally doesn't exist now.
• 54:37But yeah, in terms of head CT's,
• 54:39the two big cohorts from Australia,
• 54:41anyone to do suggest that,
• 54:43although you know,
• 54:44even though the risk is increased,
• 54:46the absolute numbers
• 54:47are small. And then Antonio
• 54:49Murray asks, is great talk.
• 54:51Have you looked at thyroid hormones,
• 54:53thyroid disease and differences
• 54:55between men and women?
• 54:57So we haven't but one thing that
• 55:00is very interesting, and it relates
• 55:02a little bit more to meningioma.
• 55:05Is a gene that we found,
• 55:07and this is a constitutional
• 55:10gene on chromosome 10.
• 55:11We've found to be associated with meningioma,
• 55:14breast, ovarian and also now
• 55:16thyroid tumors. Interest.
• 55:20Elizabeth, thank you.
• 55:21We are at the top of the hour.
• 55:24Appreciate both your talk and
• 55:25Barbara's really extending work.
• 55:26Thank you for sharing all of it with
• 55:29us and to everyone who joins us today.
• 55:31Thank you for taking the time
• 55:34to join grand rounds and we'll
• 55:36see you all again next week.
• 55:38Have a good day.