Investigating the Complex Biology of Drug Resistance for Clinical Impact

Transcript

• 00:00Good morning, everybody.
• 00:01Thank you for being here.
• 00:03Welcome to Grand Rounds.
• 00:06This is the this Grand Rounds is
• 00:09in a special location, obviously,
• 00:11because we are linked today to the
• 00:13first of what we hope will be a
• 00:16really successful series of annual
• 00:18translational science retreats
• 00:20meant to highlight the amazing
• 00:23resources that are present at Yale
• 00:26Cancer Centre for people who do
• 00:29translational science and also to
• 00:33highlight some of the amazing stories
• 00:35that that have come out of this work.
• 00:37And so no one better to to be our
• 00:41inaugural speaker than Doctor Katie Politi.
• 00:44Katie studied biology at the University of
• 00:47Pavia in Italy and then moved to New York,
• 00:50obtaining her PhD in genetics
• 00:53at Columbia University.
• 00:54She then joined Harold Varmus's
• 00:56lab at Memorial Sloan Kettering
• 00:58and began her life's work on the
• 01:01molecular basis of lung cancer.
• 01:04She continues this work at Yale,
• 01:05now as a professor in the Departments
• 01:07of Pathology and Internal Medicine
• 01:09in the section of Medical Oncology.
• 01:12Her laboratory is focused on studying
• 01:14the biology of lung cancer and
• 01:16uncovering mechanisms of resistance to
• 01:18targeted therapies and immunotherapies
• 01:20in in this disease.
• 01:22She's also got a keen knowledge of
• 01:26essentially every mutation that's
• 01:27ever been described in lung cancer.
• 01:29And I know that doctors often call
• 01:31her up and say what drug should I use.
• 01:34She Co leads the cancer signaling
• 01:36networks research program.
• 01:38She's the scientific director of
• 01:40the Center for Thoracic Cancers,
• 01:42Co Director of the Yale Sport in
• 01:44Lung Cancer and recently elected
• 01:46to the ACR Board of Directors.
• 01:48So we're really appreciative that
• 01:50you're going to kick us off today
• 01:53the the ID number there is to record
• 01:57your attendance and then we'll
• 01:59have questions both in the room
• 02:02and online when when we're done.
• 02:05Thank you.
• 02:10Thank you very much, Barbara,
• 02:12for that wonderful introduction
• 02:15and thank you very much for
• 02:17having me as a speaker today.
• 02:18It really always is, I think,
• 02:21very special to speak at one's own
• 02:24institution and then especially
• 02:26also associated with this first
• 02:28translational science retreat.
• 02:30So I'm really excited about this.
• 02:32And today what I'm going to do is
• 02:34I'm going to tell you about some of
• 02:37the work that we've been doing over
• 02:39the past few years in the laboratory.
• 02:46These are my disclosures.
• 02:50So we have a long standing interest
• 02:52in the lab on studying lung cancer.
• 02:55And as all of you know,
• 02:57there are several histological
• 02:58subtypes of lung cancer.
• 03:00But one of the things that we've learned
• 03:03over the past 20 or so years is that
• 03:05lung cancer is not one entity and that
• 03:08there are in addition to different
• 03:11histological subsets of the disease,
• 03:13there are also are a variety of laser
• 03:18pointer of molecular subsets and in
• 03:21particular in lung adenocarcinoma.
• 03:24Through various sequencing efforts,
• 03:26different mutations in genes that
• 03:30encode either receptor tyrosine
• 03:32kinases or downstream signaling
• 03:35components of receptor tyrosine
• 03:37kinase signaling pathways that
• 03:39regulate cell proliferation and cell
• 03:42survival have been identified as
• 03:44you can see here in this pie chart.
• 03:45And I think one of the things to
• 03:48really highlight is what we've
• 03:50learned over the years is that
• 03:52these mutations are in addition to
• 03:55being molecular to establishing
• 03:57molecular subsets of the disease.
• 03:59They really also are clinically
• 04:02relevant because different targeted
• 04:04agents have been developed that can
• 04:06you be used to block the activity
• 04:09of these mutated oncogenic drivers.
• 04:11And in particular and in the work
• 04:13that I'll tell you about today,
• 04:15for example,
• 04:15mutations were found 20 years ago
• 04:18now in Exxon's encoding the kinase
• 04:21domain of the epidermal growth factor
• 04:23receptor after in about 15 to 4050%
• 04:28of lung and nocarcinomas depending
• 04:31on which population you look at.
• 04:35And these are mutations that
• 04:39confer sensitivity to EGFR tyrosine
• 04:41kinase inhibitors.
• 04:42But there are many other
• 04:44targeted therapies as well.
• 04:45So you can have rearrangements in
• 04:49the anaplastic lymphoma kinase and
• 04:52targeted therapies that are effective
• 04:54in that and so on for a number of
• 04:57different oncogenic drivers and lung cancer.
• 05:00And so this has really transformed the field.
• 05:02And so if we look at this diagram here of
• 05:07approved FDA approvals for lung cancer in,
• 05:10in recent years,
• 05:11what you'll see is it really has
• 05:14been an explosion in FDA approvals,
• 05:16especially from the early 2000s in the
• 05:192000 and 10s and approvals now also
• 05:22in the first part of the twenty 20s.
• 05:25Most of these agents that were
• 05:27approved recently have been targeted
• 05:29agents and that really is linked to
• 05:31the discoveries of these molecular
• 05:33subsets of the disease.
• 05:34But also do I think one of the things
• 05:37that has been emerging also in the
• 05:39past 10 to 15 years really are the
• 05:42approvals of immunotherapies that
• 05:44we hear a lot about agents that
• 05:47are targeting immune checkpoints
• 05:48like the anti PD1,
• 05:50anti PDL ONE Access and CTLA 4.
• 05:53And so this has really been
• 05:55transformative in a lung cancer.
• 05:57And I'd like just like to point out
• 06:00how in recent analysis what we're
• 06:03seeing is that there's actually
• 06:05a decrease in mortality from lung
• 06:08cancer in recent years.
• 06:10And in the study published in the New
• 06:11England Journal of Medicine a few years ago,
• 06:13it was really shown that the
• 06:15decrease in mortality from lung
• 06:17cancer can't be accounted
• 06:19for just because of a decrease
• 06:21in incidence of the disease.
• 06:23But is likely reflects actually
• 06:26advances in the care and in the new
• 06:30therapeutics that have emerged,
• 06:32including in particular in the
• 06:33years that were studied in
• 06:35this paper for targeted agents.
• 06:37And so I think this is a really nice
• 06:41example of how what we've learned over
• 06:44the years from from the biology and
• 06:47from the genetic studies of tumors
• 06:50really is having a profound impact
• 06:52for patients with this disease.
• 06:55And of course I would be remiss if I
• 06:57didn't point out how immunotherapies
• 07:00have also been transformative.
• 07:02And I think the continued decrease
• 07:04in mortality that we are continuing
• 07:05to see is actually going to show how
• 07:08it isn't only the targeted therapies
• 07:10but also the immunotherapies that are
• 07:12really contributing to this decrease
• 07:15in and mortality from lung cancer.
• 07:17So if you know you look at this,
• 07:20there's really these advances
• 07:21have been tremendous.
• 07:22But what we do know is that both
• 07:25primary and acquired resistance
• 07:27to targeted therapies and to
• 07:30immunotherapies are common.
• 07:32And here you can see an example of
• 07:35scans from a patient with a tumors
• 07:38with AK Ras G12C mutation treated
• 07:41with AK Ras G12C inhibitor and
• 07:44you can see the tumor regresses
• 07:47but then comes back and you have
• 07:49this is acquired resistance.
• 07:51And here if we look at this plot
• 07:54taken from a review looking
• 07:57at studies of immunotherapies,
• 07:59you can see that across various
• 08:01different indications but including
• 08:03in lung cancer here that in clinical
• 08:06studies of immunotherapies,
• 08:07the response rates or to immune
• 08:09checkpoint inhibitors are not super high.
• 08:12We're not talking 7080% the way we're
• 08:14talking with some targeted therapies.
• 08:16Not only that,
• 08:17but also we see acquired resistance
• 08:19commonly emerging.
• 08:20So there's a lot of work that needs
• 08:22to be done to really understand and
• 08:25optimize treatments for both targeted
• 08:27agents and immunotherapies and to
• 08:30understand mechanisms of sensitivity
• 08:31and resistance to these agents.
• 08:33And So what do we do in my lab?
• 08:37And as part of the research program,
• 08:41we are really interested in understanding
• 08:45mechanistically biological processes
• 08:47that are involved in cancer.
• 08:49We like to integrate these with
• 08:53studying and addressing clinical
• 08:55challenges and investigating specimens
• 08:57and data from patients with cancer.
• 09:00And really the hope is that the work
• 09:02that we do collectively as a group,
• 09:04this is work that we do with many
• 09:07different people is to discover
• 09:09things that will discover findings
• 09:11that will lead to clinical trials and
• 09:14new therapeutic approaches to patients.
• 09:17Central to our research program is
• 09:20the use of biological specimens from
• 09:23patients and analysis of these specimens.
• 09:27And I think this slide is also going
• 09:29to be showed later in the day as an
• 09:31example of one of the resources that
• 09:33we have as part of the lung cancer
• 09:35program to really be able to collect
• 09:40and use specimens from patients.
• 09:42And this is just one of the examples
• 09:44of one of the resources I think
• 09:46you'll hear about a couple
• 09:48of others later on as well.
• 09:49But really an effort that started many,
• 09:51many years ago working initially
• 09:55with Scott Genger and Anna
• 09:58Wertz and Roy Herbst and many,
• 10:00many people in this room now with
• 10:03Sarah and many of all of the thoracic
• 10:06oncologists on the team and pathologists.
• 10:09Kurt for example,
• 10:11really working on collecting specimens
• 10:13from patients who have advanced
• 10:16lung cancer through treatment,
• 10:17especially at the time of resistance.
• 10:19So that then we can take these
• 10:21specimens and analyze them,
• 10:22generate patient derived models.
• 10:24And really these have contributed extensively
• 10:27to the work that I will tell you about today.
• 10:30And so I put a little cryovile here.
• 10:34And So what I'm going to do through the talk
• 10:37is when you see a cryovial on the slide,
• 10:40it actually is an example of data
• 10:44that we've been able to analyse and
• 10:46use because of the specimens that
• 10:48were collected through this approach.
• 10:50So you'll see that throughout the talk.
• 10:53So what what am I going to tell
• 10:56you about today.
• 10:56So I think as most of you know
• 11:00we have a long standing interest
• 11:02in studying the biology of EGF
• 11:04receptor driven lung cancer.
• 11:06And so when patients and really the
• 11:09focus that we've had at least in
• 11:13the in the past or until recently
• 11:14has really been and because of the
• 11:16sort of the clinical landscape has
• 11:18really been on advanced metastatic
• 11:20EGF receptor driven lung cancer.
• 11:23And so when patients are diagnosed
• 11:26with EGF receptor driven lung cancer,
• 11:29now they're mostly treated with tyrosine
• 11:33kinase inhibitors most recently and
• 11:35in the United States especially the
• 11:37tyrosine kinase inhibitor awesome.
• 11:39Merton if this is one of the newer
• 11:42generation of agents that has more
• 11:44activity on mutant EGFR compared
• 11:46to wild type.
• 11:48So hopefully decreasing its toxicity
• 11:50and has been shown to have superior
• 11:53progression free survival and overall
• 11:55survival compared to standard of
• 11:58care earlier generation tyrosine
• 12:00kinase inhibitors in this disease.
• 12:02And so this was an A really
• 12:05important advance in the field.
• 12:06However,
• 12:07what we do know is that still
• 12:09resistance or acquired resistance two
• 12:12asamertinib occurs almost inevitably
• 12:16and it actually isn't very commonly
• 12:20associated with on target EGFR mutations.
• 12:24And this is different from some of the
• 12:27earlier generations of tyrosine kinase
• 12:29inhibitors that instead where we saw
• 12:32commonly one most frequently observed
• 12:34on target EGF receptor mutation,
• 12:36the T79 TM mutation.
• 12:38But you see additional mechanisms of
• 12:41resistance met amplification for example,
• 12:44so a bypass signaling pathway
• 12:46being one of the more common.
• 12:48Then we see a histologic changes in
• 12:51the tumors that occur quite frequently,
• 12:53but then most of the mechanisms
• 12:55of resistance are really not known
• 12:57and poorly understood.
• 12:58And so one of the things that we've
• 13:01been interested from when as we
• 13:04think about these problems is really,
• 13:08really understanding these tough
• 13:10challenges like really understanding
• 13:12this part of the pie chart, right.
• 13:15What are these mechanisms of resistance,
• 13:17What is happening in these tumors
• 13:20where we don't really
• 13:22have a key genetic alteration that
• 13:24has changed that or clear process
• 13:27that is happening that we can target.
• 13:30And so just a couple of thoughts
• 13:33that sort of guide our thinking.
• 13:35Targeted agents are probably not sufficient.
• 13:38We need to discover new untapped
• 13:41vulnerabilities of oncogene driven lung
• 13:44cancers and then the tackling resistance
• 13:47requires new knowledge of the links between
• 13:50cancer cell plasticity and the tumor
• 13:53microenvironment and tumor heterogeneity.
• 13:55And so these are some of the and so I
• 13:57think of these that like the the not the
• 13:59low hanging fruit but the fruit really
• 14:00at the top of the tree that we're trying
• 14:04to really grasp and understand when we.
• 14:07And and really if we look at EGF receptor
• 14:10driven lung cancer and we think about it,
• 14:13one of the things that we know is
• 14:15that with with the targeted agents
• 14:17that I've told you about today is
• 14:20we do see this acquired resistance.
• 14:22But not only that.
• 14:23We also know that when we use the
• 14:25targeted agents they don't completely
• 14:27eradicate all the tumor cells and
• 14:30there's variability in the depth and
• 14:32duration of responses in patients.
• 14:34And you can see this really in this
• 14:36waterfall plot where there's some
• 14:38tumors that shrink dramatically
• 14:39and others that shrink less.
• 14:41And so we've been interested in the
• 14:44question of what accounts for this
• 14:47heterogeneity and disease progression and
• 14:49sensitivity to tyrosine kinase inhibitors.
• 14:52And so the first thing that I'm
• 14:54going to go through is some of the
• 14:56work that we've done to study how
• 14:58different EGF receptor mutations can
• 15:00actually have distinct properties.
• 15:02And so first of all,
• 15:06I've sort of told you about EGF
• 15:08receptor mutations and one could think,
• 15:09oh, we can lump them all together.
• 15:11But in reality,
• 15:13what we do know and what is becoming I
• 15:16think increasingly clear in recent years
• 15:19is that you have their different EGF
• 15:22receptor mutations and not only that,
• 15:24the different EGF receptor mutations have
• 15:28different properties both biological,
• 15:30biochemical and also in terms
• 15:32of TKI sensitivity.
• 15:34And so when we look at
• 15:36EGF receptor mutations,
• 15:37there are two major categories of mutations.
• 15:40There's the L858R point mutation and then
• 15:44there's a set of small in frame deletion,
• 15:46some of them more complex and Exxon 19.
• 15:50The most common of these is
• 15:52this E 746 to a 750 mutation.
• 15:54But then there are these other in
• 15:57Dells that are found at, you know,
• 15:59variable frequencies in these tumors,
• 16:00but they exist.
• 16:02And So what does it mean?
• 16:04Are all these mutations alike?
• 16:05Well,
• 16:06one of the things that we know is that
• 16:08even if you just broadly categorize
• 16:11the L858R mutations and the e.g FRXN 19
• 16:14deletion mutations and you look at the
• 16:17survival curves on ossumertinib from
• 16:19the trial of frontline osumertinib,
• 16:22you see that even just the
• 16:25Exxon 19 deletion mutations,
• 16:27the overall survival is about
• 16:2840 months in that study.
• 16:30But for the L858 Rs,
• 16:32it's about 33 months.
• 16:33And this is consistent over across
• 16:36different tyrosine kinase
• 16:37inhibitors that are used.
• 16:39So the L858R subset does worse with
• 16:43TKIS compared to the Exxon 19 subset.
• 16:46We also found several years ago in
• 16:50work that we did together with Sarah
• 16:53Goldberg and Mark Lemon is that that
• 16:56there's a small in frame deletion
• 16:59in a Proline insertion mutation and
• 17:02one of the Exxon 19 deletions that
• 17:05actually if you look at that mutation
• 17:07and you look in upon treatment with
• 17:09irlatinib this was a few years ago.
• 17:11So one of the early generation
• 17:14tyrosine kinase inhibitors that the
• 17:16progression free survival duration
• 17:17of a treatment overall survival were
• 17:20all worse for the for erlontinib in
• 17:22that subset compared to the more
• 17:25common Exxon 19 deletion mutation.
• 17:27And this along with some laboratory
• 17:30studies really piqued our interest in
• 17:32studying these differences a little bit more.
• 17:35And here you see the cryovile appear.
• 17:38This is also work that was Zenta Walther
• 17:42was really central to helping us
• 17:45identify these patients for this study.
• 17:48And so working with lots of different
• 17:52groups here we were able to show that
• 17:54this proline insertion for example what
• 17:57you see in Western blots is when you
• 18:00treat with tyrosine kinase inhibitors,
• 18:02it's less sensitive to various
• 18:05tyrosine kinase inhibitors compared
• 18:07to the canonical e.g.
• 18:09FRXN 19 deletion mutation.
• 18:11Not only that,
• 18:12when you actually go and look biochemically,
• 18:14and this is work that was spearheaded by a
• 18:17former student that Mark Lemon and I shared.
• 18:20Eris von Alderweil,
• 18:22von Rosenberg showing that this
• 18:24proline insertion mutation has AKM for
• 18:28ATP that is more more closer to the
• 18:31wild type in contrast to some of the
• 18:34other variants that instead are more
• 18:36sensitive to tyrosine kinase inhibitors.
• 18:38So really is that affinity of the
• 18:41kinase for ATP that is probably
• 18:43rendering it more resistant to
• 18:45these tyrosine kinase inhibitors.
• 18:46So really from the clinical observations,
• 18:49from some of the laboratory
• 18:51studies going to the biochemistry,
• 18:52we're really able to figure out what
• 18:55was happening with this variant.
• 18:57And this led to work that we did
• 19:00together with Mike Grant and Sarah
• 19:03Goldberg really putting together a multi
• 19:06institutional cohort of patients with e.g.
• 19:09Fr XL19 deletion mutations treated
• 19:11with asumertinib because we wanted to
• 19:13look at the tyrosine kinase inhibitor
• 19:15that was really clinically relevant
• 19:17for patients right now and that was
• 19:19being used to see what outcomes
• 19:21were for patients with this Proline
• 19:24insertion mutation with asumertinib.
• 19:26It's pretty rare.
• 19:27So you have to really work together and put
• 19:30together a cohort from various institutions.
• 19:33And so Mike and Sarah assembled
• 19:38this cohort including data from
• 19:40our Yale cohort and actually showed
• 19:43that in patients whose tumors have
• 19:46this proline insertion mutation
• 19:48treated with ossomatinib,
• 19:49you have worse progression free survival.
• 19:53Then if you look at the common e.g.
• 19:55Fr XM19 deletion mutation,
• 19:57the overall survival isn't quite
• 19:59statistically significant,
• 20:01but you can see that there is a trend
• 20:04in in in in worse outcomes there as well.
• 20:07And So what does this mean?
• 20:09What does this make us think?
• 20:11I think the message here is that
• 20:15not all mutations are the same.
• 20:17And now we have the tools and the drugs
• 20:20to better match mutations with therapies.
• 20:22We aren't the only ones who
• 20:24are thinking about this.
• 20:25There's some other work from
• 20:28Jacqueline Robichaud and John
• 20:30Haymack's group at MD Anderson,
• 20:32work from Christine Lovely at Vanderbilt,
• 20:35all really pointing in this direction.
• 20:37We need to know about the biology,
• 20:39the biochemistry of the mutations,
• 20:41and that can help us think about
• 20:44perhaps how to better optimize these
• 20:46therapies now that we have them.
• 20:48Another point, yeah,
• 20:50the structural and biochemical
• 20:51understanding of the effects of
• 20:53the mutation can guide predictions
• 20:55for TKI sensitivity and resistance.
• 20:57And of course,
• 20:58the other question that comes along
• 20:59is how do we translate to the
• 21:01clinic this to the clinic now what?
• 21:03What are the next steps that we can take?
• 21:05So we can test trials of like optimal TKI.
• 21:10So now we have all these reagents,
• 21:12we can test other agents and other
• 21:14drugs on these different variants
• 21:15and see if there's some that are more
• 21:18effective for specific mutational subsets.
• 21:20But then the other question is,
• 21:22are there other agents that we
• 21:24should be thinking about for certain
• 21:27subsets of the disease in combination
• 21:29with also Mertinib?
• 21:30And I think this will be a
• 21:32recurring theme throughout the talk.
• 21:33So for example, you know,
• 21:35should we be thinking about specific
• 21:37antibody drug conjugates or other
• 21:39approaches to target tumors with that
• 21:42don't do as well with monotherapy?
• 21:44Awesome.
• 21:45Or so after you know thinking
• 21:48about the different.
• 21:49So we talked about how different EGF
• 21:52receptor mutations themselves can
• 21:54have an impact and have distinct properties,
• 21:56but what about Co mutations?
• 21:57How can Co mutations influence tumor
• 22:01progression but also TKI sensitivity.
• 22:05And so many years ago now,
• 22:07I probably started working on this
• 22:09actually almost exactly 20 years ago
• 22:12when EGF receptor mutations were discovered.
• 22:14I think it was May 2004 that I started
• 22:18generating these mouse models.
• 22:20We generated genetically engineered
• 22:24mouse models of EGF receptor driven
• 22:27lung cancer in which we could express
• 22:30the EGF receptor mutants inducibly
• 22:32in the lung epithelium.
• 22:33And this was really these were really
• 22:35to be able to study the biology
• 22:37of the disease.
• 22:38And we've used these mice extensively
• 22:41over the years to study signaling by
• 22:44mutant EGF receptor discover resistance
• 22:46mutations to tarsine kinase inhibitors,
• 22:49identify therapeutic strategies to
• 22:51overcome or prevent and or prevent
• 22:54drug resistance and study the
• 22:56effects of targeted therapies on
• 22:58the immune microenvironment.
• 22:58And here you can see MRI images.
• 23:00We use MRI imaging for our mice to
• 23:04look at the lungs and see or you can
• 23:05see lungs full of tumors you treat
• 23:07them with a tyrosine kinase inhibitors,
• 23:09the tumors shrink and go away.
• 23:12Over time the tumors come back and
• 23:14we can study those resistant tumors.
• 23:16So a few years ago we decided to
• 23:20upgrade our our mouse model and
• 23:25use a slightly different system
• 23:27that would allow us then also to
• 23:29be able to modulate other genes.
• 23:31Because we know that EGF receptor mutations
• 23:33and lung cancer don't occur in a vacuum.
• 23:35There are other mutations in the tumors there
• 23:38and we wanted to be able to model that.
• 23:40So we decided to take this still
• 23:44this tetracycline inducible EGFR
• 23:46allele across it to another mouse.
• 23:50That in which using Cree recombinase
• 23:54you can then turn on expression of the
• 23:56reverse tetracycline transactivator
• 23:58which can bind the tetromotor in
• 24:00the presence of doxycycline and
• 24:02induce expression of EGF receptor.
• 24:04And we also crossed it to AP
• 24:0653 phloxed allele.
• 24:07But using this mouse what happens
• 24:10is we can deliver Cree recombinase,
• 24:12we deliver it with a Lantivirus
• 24:16into the lungs of mice,
• 24:18turn on mutated EGF receptor.
• 24:20Simultaneously we can delete P53.
• 24:23And here's some images,
• 24:24these are the lungs of mice.
• 24:26You can see the by MRI,
• 24:29you can see here by Histology and a a
• 24:32bigger magnification of the Histology.
• 24:35So we said OK,
• 24:37so we have this mouse model with now
• 24:40EGFR and mutants and P53 deficient tumors.
• 24:42The P53 deficient tumors are higher grade,
• 24:45they're nastier.
• 24:46I see Rob Homer here.
• 24:47He has helped us extensively over the
• 24:49years characterize and study these tumors.
• 24:52And so one of the questions that
• 24:54we had is well in addition to P53,
• 24:56what role do other mutations in
• 24:59EGF receptor play in EGF receptor
• 25:01driven lung cancer?
• 25:02How do they affect tumor progression?
• 25:04How do they affect TKI resistance
• 25:06and how do they affect the molecular
• 25:08properties and phenotypes of the tumors?
• 25:11And So what we did is we worked with
• 25:13a colleague at Stanford University,
• 25:15Monty Winslow,
• 25:15who had developed an approach in and
• 25:19used it in K Ras driven tumors to
• 25:21really be able to inactivate using CRISPR,
• 25:25CAS 9 technology,
• 25:26different tumor suppressor genes
• 25:29simultaneously in the lungs of mice.
• 25:32So not all of them in the same cell,
• 25:34but you can deliver this kind of
• 25:37pool of lentiviruses and in different
• 25:39cells you can then inactivate
• 25:41different tumor suppressor genes.
• 25:42And then you can use a computational
• 25:45approach that he developed called
• 25:47tumor barcode sequencing which
• 25:49based on various controls that are
• 25:51spiked in and based on barcode IDs.
• 25:54You can actually look and quantify
• 25:57the effect of inactivating that tumor
• 26:00suppressor gene on the number and
• 26:02size of tumors in in, in a screen.
• 26:05It's essentially a way of doing
• 26:06an in vivo screen.
• 26:07And so we applied,
• 26:09we took this pool of lentiviruses
• 26:13targeting different tumor suppressor genes
• 26:15that were frequently altered in lung cancer,
• 26:19not necessarily in EGF receptor driven
• 26:21lung cancer but in lung cancer and
• 26:22he had used it in the K Ras model
• 26:25previously and so we applied it to our e.g.
• 26:27FRL 850 at RP53 model and in particular
• 26:31we had also crossed the model that
• 26:33I just told you about with one
• 26:34that has an inducible CAS 9 Ileo.
• 26:36So these are experimental animals here.
• 26:39These are controls because
• 26:40they don't have CAS nine.
• 26:41You can't do CRISPR CAS 9 mediated genome
• 26:44editing when you don't have CAS 9:00.
• 26:46So we transduced the lungs of the mice,
• 26:50waited 11 weeks and then took the lungs
• 26:53of the mice and did tumor barcode
• 26:57sequencing in our control animals.
• 26:58When you look at the relative
• 27:00tumor size compared to controls,
• 27:01you don't really see any.
• 27:03The tumor suppressor gene
• 27:04inactivation doesn't have any effect,
• 27:06but that's because you don't have CAS 9,
• 27:08so you shouldn't see anything.
• 27:10So that was reassuring.
• 27:11What do we see in the mice with CAS 9?
• 27:13So one of the things that we saw is
• 27:15that when you inactivate APC from the
• 27:19wind signaling pathway RBM 10 and RB1,
• 27:23these three tumor suppressor
• 27:25genes when inactivated had the
• 27:28biggest effect on tumor growth.
• 27:30So the tumors grew faster when
• 27:32you were inactivating these tumor
• 27:34suppressor genes compared to controls.
• 27:37We also noticed interestingly
• 27:40that SET D2 and LKB 1,
• 27:43both of these putative tumor
• 27:45suppressor genes I'd say actually had
• 27:47a negative effect on tumor growth,
• 27:48which was quite interesting
• 27:50and is and I'll go,
• 27:51I'll tell you a little bit
• 27:52more about that in a minute,
• 27:53but it's a topic of interest,
• 27:55interesting work that we're doing.
• 27:56And then there were a number of
• 27:58tumor suppressor genes that really
• 28:00had no effect on tumor growth.
• 28:02We went ahead and we validated
• 28:04these using single SGRNAS.
• 28:06This is towards APC and this is
• 28:09to RBM 10 which is an RNA binding
• 28:12protein and a splicing factor.
• 28:14And you can see that when you
• 28:16inactivate them you see these bigger
• 28:19tumors and tumors progress faster
• 28:21than in the EGF receptor P53 model.
• 28:24So what does this mean though
• 28:26in the context of human cancer?
• 28:28And so if we,
• 28:31what we did at that time is we
• 28:34actually interrogated the ACR
• 28:35Project Genie database,
• 28:37which is a large data set that has a
• 28:39lot of mutational information that
• 28:41has been contributed to this data
• 28:44set from various institutions that
• 28:46are from their tumor sequencing
• 28:49efforts at their institutions.
• 28:52And when we look in this data set at e.g.
• 28:55F RP53 driven tumors and we look at
• 28:58the frequency with which there are
• 29:00alterations in this Co occurring
• 29:02tumor suppressor genes,
• 29:03you actually see that the top hits
• 29:06RBM 10 RB one and APC are where the
• 29:09top hits in our functional screen in mice.
• 29:13So we think that our screen in mice
• 29:15is actually telling us something
• 29:17about the functional relevance of
• 29:20these alterations in the human
• 29:21tumors and arid 1A didn't come out
• 29:23in our screen at 11 weeks,
• 29:26but we actually did another time
• 29:27point at 19 weeks and it popped up.
• 29:29So perhaps it's more important later
• 29:32in tumorigenesis And interestingly
• 29:34Genes SDK 11 is LKB one,
• 29:36it's really not frequently altered
• 29:38and that was the one that I showed
• 29:41you seemed to have a negative effect
• 29:43in our in vivo screen.
• 29:45So we've actually,
• 29:46this has been a really powerful
• 29:49system and we've actually been able
• 29:52to do broader screens with more
• 29:54genes and try to learn a little bit
• 29:57more about what genes are important
• 30:00for the progression of these tumors.
• 30:02And I'd just like to highlight
• 30:04an example of work that we
• 30:08did continuing this continuing
• 30:11this effort with D2G Oncology,
• 30:14a company that was founded Co
• 30:16founded by our collaborators
• 30:18Monty Winslow and Dmitry Petrov.
• 30:20And we work together on doing
• 30:23this screen of additional tumor
• 30:25suppressor genes in the context of
• 30:28EGFR tumors but also in the context
• 30:30of K Ras driven tumors for example.
• 30:32And you know I just like to go back to
• 30:36LKB one for example showing how this
• 30:38has a negative effect on EGFR driven tumors.
• 30:41It's not really a contributing,
• 30:45it doesn't really Co occur
• 30:47mutationally with EGFR driven tumors.
• 30:50So it seems to be like a synthetic
• 30:52lethality with these tumors.
• 30:54But it's an amazing contrast with what
• 30:56we see in Keras driven tumors where it
• 30:58is one of the major drivers of tumor growth.
• 31:01And so this is I think telling
• 31:03us and it's frequently mutated
• 31:04with Keras in human tumors.
• 31:06So we're really,
• 31:09we're really think that this is a
• 31:11cool system to be able to understand
• 31:14how Co occurring alterations
• 31:16impact the fitness of tumors.
• 31:18And Fran Exposito in the lab is
• 31:21really working a lot to understand
• 31:23this synthetic lethality and is
• 31:25doing experiments to knock it LKB
• 31:29one out and established EGF receptor
• 31:31tumors and see what happens and
• 31:33also to understand mechanistically
• 31:35what is happening in these tumors.
• 31:37So stay tuned for for data on
• 31:40these studies that I think will
• 31:42be really fascinating.
• 31:43And there are some other targets that
• 31:45we're studying along these lines as well.
• 31:48So I think a very powerful system.
• 31:50We've also used this approach not just
• 31:54to study mechanisms of tumor progression,
• 31:57but also use this type of approach
• 31:59to really understand what genes
• 32:01can modulate the sensitivity to
• 32:03tyrosine kinase inhibitors.
• 32:05So we did the same experiment and instead
• 32:09of just waiting and collecting the tumors,
• 32:12what we did is we also had an arm
• 32:14where we treated for two weeks with a
• 32:16tyrosine kinase inhibitor osumertinib.
• 32:18You see here the tumors go away
• 32:20or they're shrinking mostly.
• 32:21They're not completely going away at
• 32:23two weeks, but you do see a response.
• 32:25And so we did the same tumor bar
• 32:27code sequencing and what we found
• 32:29here is so this is the,
• 32:30this is the plot that I showed you earlier
• 32:33looking at what is affecting tumor growth.
• 32:36Well,
• 32:36when we add Asamertinib,
• 32:38one of the things that we saw is that
• 32:41keep 1 the tumor suppressor gene,
• 32:43keep one that really didn't have much
• 32:45of an effect on the growth of the
• 32:48tumors in the absence of drug now
• 32:50limits the sensitivity to Asamertinib.
• 32:52In other words,
• 32:54the tumors aren't shrinking as
• 32:56much as wild wild type
• 32:58or control tumors do
• 33:00when keep one is present.
• 33:02What do we think is happening here?
• 33:03Well, we know that keep one is important
• 33:07to sequester NRF 2 in the cytoplasm.
• 33:10When you knock out KEEP 1,
• 33:12NRF 2 can then go into the nucleus and
• 33:16activate antioxidant response elements and
• 33:18those gene expression programs that allow
• 33:22cells to really withstand oxidative stress.
• 33:24And when we take our mice and we just use
• 33:28an individual SGR and a targeting keep one,
• 33:31these are the control mice
• 33:32that don't have CAS nine,
• 33:34you use Asamertinib, the tumors go away,
• 33:37you don't really see anything
• 33:38left in the lungs.
• 33:39But if you have the experimental mice
• 33:41that have CAS 9 and you use the SGR and a
• 33:44targeting keep one treat with Asamertinib,
• 33:46you see tumors are still left over.
• 33:49And so again,
• 33:50what does that mean for patients?
• 33:52So at the time what we did is we
• 33:55worked with Jessica Hellier and Heather
• 33:58Wakeley at Stanford University who had a
• 34:01collection of data from patients with e.g.
• 34:04F RP53 driven lung cancer and looked at
• 34:07whether there were mutations in genes
• 34:09in the keep one access in these tumors.
• 34:12And you can see here in this blue line,
• 34:14the patients who had mutations in the
• 34:17keep One access in their tumors had
• 34:19a shorter time to treatment failure
• 34:22compared to controls suggesting that if
• 34:26you have alterations in this this program,
• 34:30this antioxidant response response program,
• 34:34you're going to have limited sensitivity
• 34:37to tyrosine kinase inhibitors.
• 34:40And so I think one of the things that
• 34:43we're really seeing emerging from this
• 34:46work looking at the tumor suppressor
• 34:49genes is that when you do have mutations
• 34:52or you have alterations that Co occur
• 34:55with EGF receptor and with EGF receptor
• 34:59P53 these can modulate both the growth
• 35:01and sensitivity to these agents.
• 35:04We we were interested in looking
• 35:06further and in work that Paul
• 35:09Stockhammer who was a resident is
• 35:12now a hospitalist here and is an
• 35:15incoming he monk fellow did recently.
• 35:19He looked at both our Yale internal data
• 35:24from our tissue collection program.
• 35:26You see the cryovial here,
• 35:29but also at the ACR project gene data set
• 35:32and looked at outcomes for patients on
• 35:38tyrosine kinase inhibitors whose tumors
• 35:41had different combinations of mutations.
• 35:44And I think the take away here is he
• 35:47was able to look at tumors that had
• 35:49mutations in a subset of tumor suppressor
• 35:52genes because tumors had been analyzed
• 35:54across a wide variety of different platforms.
• 35:57So we had to sort of focus in on the the,
• 36:01the common subset of tumor suppressor
• 36:03genes that were looked at across platforms.
• 36:05But essentially if tumors had both
• 36:10P53 mutations and a mutation,
• 36:12at least one of these tumor
• 36:13suppressor genes that he looked at,
• 36:15they had worse outcomes.
• 36:17These are EGFR mutant tumors even
• 36:19compared to mutations that just had
• 36:22TPF 3 mutations and were wild type for
• 36:25those different tumor suppressor genes.
• 36:28And So what does that mean?
• 36:29Again, I think we're identifying a subset
• 36:32of tumors where there may be a benefit
• 36:35from adding a different therapy or it
• 36:38should be at least be investigated from
• 36:40the get go because they are likely to
• 36:44have worse outcomes with monotherapy
• 36:46tyrosine kinase inhibitor treatment.
• 36:48And this is very relevant right now
• 36:50at least in the field of EGF receptor
• 36:52driven lung cancer because there are
• 36:54studies of chemotherapy plus asamartinib
• 36:56in the first line that are positive.
• 36:59But people are very reluctant to
• 37:01give that combination to everybody.
• 37:03If we can identify people who might
• 37:05benefit more or might need it more than
• 37:08that could be really helpful for deploying
• 37:10these different strategies in the clinic.
• 37:13And then I think another point is that
• 37:16we're really learning the Co mutations
• 37:19can affect therapeutic sensitivity
• 37:20and it isn't only in the context
• 37:23of EGFR tyrosine kinase inhibitors.
• 37:25This is happening in multiple contexts
• 37:28and with with multiple agents.
• 37:30So here an example,
• 37:31I'm just just giving you a few examples.
• 37:33There are many more in the literature.
• 37:35But if we look at keep one,
• 37:37going back to keep one, keep one,
• 37:39alterations seem to have been negative
• 37:43for response rates to Sotirasip
• 37:46in K Rash G12C driven lung cancer.
• 37:50Worse,
• 37:51you know higher local recurrence
• 37:54with chemo radiation in the context
• 37:58of immunotherapy LKB 1 mutations
• 38:01actually seem to be worse confer,
• 38:04you know be worse for or describe,
• 38:06define a word a subset that
• 38:09does worse with immunotherapy.
• 38:11And so in conclusion for this
• 38:14part of the talk,
• 38:16the nature of the oncogenic mutation and
• 38:18Co occurring mutations effects sensitivity
• 38:20to Tkis and mechanisms of resistance.
• 38:23We've developed a new generation of
• 38:26genetically engineered mouse models that
• 38:28can be used to study these complex genotypes.
• 38:31And I'd like to point out that
• 38:33really we have a lot of work that
• 38:35is happening now studying these
• 38:37individual different components.
• 38:39Mariana Do Carmos,
• 38:40an MD,
• 38:41PhD student in the lab.
• 38:42She's studying the role of RBM 10
• 38:46in EGF receptor driven lung cancer
• 38:49working with Luisa escobarahoyos lab.
• 38:51Because we really can
• 38:55join forces and Luisa is an
• 38:57expert in splicing and this is
• 38:59really important gene protein
• 39:01that is involved in in splicing.
• 39:03So we're doing that.
• 39:04I told you about Fran's work.
• 39:06We have Kita who's working on KMT 2D,
• 39:10which I didn't tell you about
• 39:12another potential target
• 39:13that came out of this screen.
• 39:14So really we can really study
• 39:16these different genotypes and
• 39:18understand the biology of these
• 39:20different complex genotypes,
• 39:21which is really exciting.
• 39:24We have found out that an activation of
• 39:27these different tumor suppressor genes
• 39:29can have different effects on both
• 39:31tumor growth including positive and
• 39:32negative effects and TKI sensitivity
• 39:34depending on the oncogenic context.
• 39:37We showed that keep one loss limits
• 39:40sensitivity to osmertinib in mice
• 39:42and in patients and think that
• 39:45this is really potentially a bad
• 39:47actor if there's Q1 alterations
• 39:50either at the genetic level or
• 39:52also alterations in the pathway.
• 39:54The pathway can be modulated
• 39:56in many different ways,
• 39:57and tumor suppressant gene mutations
• 39:59can be used to identify patients,
• 40:02subsets of patients who are likely
• 40:05to have worse outcomes and could
• 40:08be considered for additional
• 40:10therapeutic interventions.
• 40:11So in the last part of the talk,
• 40:15I'd like to tell you about some
• 40:18other work that we've been doing
• 40:21more recently to study non mutational
• 40:24mechanisms of resistance and I'd
• 40:26say also of persistence.
• 40:28So on tyrosine kinase inhibitors.
• 40:30And So what are some of the things
• 40:34that we're thinking about broadly
• 40:36in the lab when we think about this
• 40:38problem of this 50% of tumors that
• 40:40we don't what for which we don't
• 40:43know why a resistance emerges.
• 40:45So some of the things that we're
• 40:47really interested in in understanding
• 40:49and studying are how the tumor
• 40:52microenvironment effects resistance
• 40:53and persistence.
• 40:54And this is work that we're doing
• 40:57collaboratively,
• 40:57Jake Schillo in the lab doing
• 41:00collaboratively working with Don
• 41:02Nguyen's lab.
• 41:03We are studying lineage plasticity
• 41:06and tumor heterogeneity.
• 41:08And I'll tell you about an example
• 41:11of this that was just recently
• 41:13published this month and that comes
• 41:16out of work studying mechanisms
• 41:19of tumor persistence.
• 41:20And of course another area that
• 41:22we're really interested in is while
• 41:24we've we're talked a lot about genes
• 41:26and mutations and genetics here,
• 41:27but are there ways of reading out
• 41:30pathways and learning about how
• 41:32pathways are altered in tumours
• 41:34which might be an important way
• 41:37of understanding resistance
• 41:38and persistence as well.
• 41:40And so one of the non mutational
• 41:42mechanisms that we recently
• 41:44discovered and published on,
• 41:46I'm not going to tell you about that
• 41:48today because I don't really have
• 41:50time is that we identified a role
• 41:52for the ATP as of the SLY sniff
• 41:55complex in mediating resistance
• 41:57to tyrosine kinase inhibitors and
• 42:01SMARCA 4 is actually usually lost,
• 42:04you have loss of function mutations
• 42:06in tumors.
• 42:06One of the things that we found
• 42:08which was really interesting is that
• 42:11actually it seems to be important
• 42:13for the resistance phenotype because
• 42:15in resistant tumors it actually can
• 42:18promote accessibility of chromatin
• 42:20at both cell proliferation genes but
• 42:23also at genes it are NRF 2 low size
• 42:27so that allow activation of genes
• 42:30that are antioxidant genes with that.
• 42:32So it links to that keep one,
• 42:35keep one finding that we had in
• 42:37our tumor suppressor gene screen.
• 42:39So I'm not going to tell you about this,
• 42:41but I did want to highlight it
• 42:43as as one of the some of the work
• 42:46that we have done recently on non
• 42:48mutational mechanisms of resistance.
• 42:50What I really wanted to focus the last
• 42:53few minutes of the talk on is telling
• 42:55you about some work that we've been
• 42:57doing to study tolerance and persistence
• 43:00to tyrosine kinase inhibitors.
• 43:02And you saw this waterfall plot earlier.
• 43:06But one of the and one of the
• 43:08questions that that we've had and I
• 43:10think that is a prominent question
• 43:11in the field is why aren't all cells
• 43:14eradicated upon TKI treatment,
• 43:15right, Because if we could get rid
• 43:17of all of the cells from the get go,
• 43:19we wouldn't have the problem of acquired
• 43:21resistance. And here's some scans.
• 43:23You see the tumor and you see several
• 43:25months later the tumor is still there,
• 43:27there still is some residual tumor leftover.
• 43:30So what is the biology of residual disease?
• 43:33Well, we decided and this is work from
• 43:35a former graduate student in the lab,
• 43:38Boom Yao,
• 43:39who who is now in Arno Osher's lab
• 43:41as a post doc.
• 43:42And I think Boom Yao is here.
• 43:43I thought I saw him.
• 43:45And So what Bom Yao did is he took
• 43:48advantage again of our collection
• 43:50of specimens from patients.
• 43:52And he said, well,
• 43:53what happens if I implant these
• 43:56PDXS that we've generated,
• 43:58treat them with a tyrosine kinase inhibitor
• 44:00and then look at residual disease?
• 44:03We can harvest that.
• 44:04You know, we take it at a plateau, right?
• 44:07Once the tumors aren't shrinking anymore,
• 44:09that's what's left over.
• 44:10And can we we it's really hard to
• 44:12study residual disease in patients.
• 44:14We can't really easily do biopsies
• 44:16on treatment,
• 44:16but this is as a surrogate of that.
• 44:19And so here are some examples of
• 44:22the PDXS that Boom Yao studied.
• 44:25So he took these PDXS,
• 44:27treated them and then took what was
• 44:29leftover after four to six weeks
• 44:31of treatment when they plateaued.
• 44:33And you can see in all of the cases
• 44:36there was tumor leftover after treatment,
• 44:38varying amounts of tumor and in
• 44:40some very little,
• 44:41very small islands of tumor,
• 44:43but there was tumor leftover.
• 44:44And I'd like to highlight an example
• 44:46of one of the things that we found
• 44:48from one of these PDXS that we
• 44:50studied in a little more detail.
• 44:52We found that in one of them we
• 44:54saw up regulation of Ascl 1.
• 44:57ASCL one is a basic Helix loop
• 45:01Helix transcription factor.
• 45:02It has a role in neuronal differentiation
• 45:04and its expression actually identifies
• 45:06a subset of small cell lung cancer.
• 45:09So it was really up in the residual
• 45:12disease in this tumor and not only
• 45:14was it up at the transcriptional
• 45:17level and the signature was was
• 45:19enriched in the residual disease,
• 45:22but it's downstream targets rat BCL two
• 45:25and DLL three were also all turned on in
• 45:29the residual disease in in that tumor.
• 45:32Ossumertinib was working really well.
• 45:34You can see phospho EGFR is gone here.
• 45:37And so this was really interesting to
• 45:39us because we know that a subset of
• 45:42EGFR driven tumors when they're treated
• 45:44with osumertinib can actually undergo
• 45:48neuroendocrine differentiation and
• 45:49transformed to small cell lung cancer,
• 45:53a subset of which are ASCL 1 positive.
• 45:56And so this kind of piqued our interest.
• 45:59And so one of the first questions that
• 46:02we had was are these ASCL one cells
• 46:05present in the tumor pretreatment.
• 46:07And so when we looked and we did
• 46:09single cell RNA sequencing,
• 46:11we actually saw that the if you look at
• 46:15the pretreatment specimen here in blue,
• 46:17there is a subset of these cells that
• 46:20is present that is ASCL 1 positive.
• 46:22So we think that those cells
• 46:25were present beforehand.
• 46:26Whether other cells then turned it on,
• 46:29we can't really tell from the
• 46:30types of experiments that we did.
• 46:32But we do know that there was a
• 46:34population that was there pretreatment.
• 46:36And so our next question after that was
• 46:39well how is ASCL 1 conferring TKI tolerance,
• 46:42what is happening.
• 46:44And so we said OK,
• 46:46let's turn to our human EGF
• 46:48receptor driven cell lines and let's
• 46:50express ASCL one in these cells.
• 46:52And so one of the first things that we did,
• 46:54we expressed ASCL one in the cells and you
• 46:57can see here in this HCCA 27 cell line,
• 47:00we expressed it and we saw more colonies
• 47:02and you can see this quantified here
• 47:05after treatment with osmertinib
• 47:06compared to the empty vector control,
• 47:08we did this across in another cell line
• 47:11and we saw no effect of ASCL one expression.
• 47:14And so this was also interesting and we said,
• 47:17OK,
• 47:17so why does ASCL one having a
• 47:20phenotype has a phenotype in one
• 47:22cell line but not the other.
• 47:24We did gene expression profiling and what
• 47:26we saw is that in the permissive cells,
• 47:29these HCC 827 cells,
• 47:32you actually saw that ASCL one could
• 47:35lead to an EMT gene expression
• 47:38program was it had no effect at
• 47:41all in the PC-9 cell line.
• 47:43And we went on and we looked with ataxiq
• 47:47at chromatin accessibility at EMT genes
• 47:51and we see that upon ESAS CL1 expression,
• 47:54you do see changes in chromatin
• 47:57accessibility at both epithelial
• 47:59genes and mesenchymal genes when
• 48:02you put Ascl one into these HCC
• 48:06827 cells that are permissive,
• 48:08but you don't see any changes
• 48:09in the PC-9 cells.
• 48:11And So what do we think is happening?
• 48:14So we think that when you have,
• 48:18when you don't have ASCL 1,
• 48:20the TKI can work and you see death
• 48:22of the EGF receptor driven cells.
• 48:25If you have a permissive cellular
• 48:28context what happens is that
• 48:30you can have ASCL one can turn on
• 48:33or can lead to an EMT program and we
• 48:36know that that is associated with
• 48:38resistance to tyrosine kinase inhibitors.
• 48:41In a non permissive cellular
• 48:43context though that you don't have,
• 48:46you don't turn on this program so
• 48:48you don't have a difference in ASCL 1
• 48:51expressing versus non expressing cells.
• 48:53We also found that pre-existing
• 48:55cells with transcriptional features
• 48:57of drug tolerant cells are present
• 49:00in the untreated tumors.
• 49:01And I think one of the questions that
• 49:03we've we're really interested in is you
• 49:05know why are some cells permissive or not.
• 49:08I think this is sort of one of
• 49:10the major problems in cancer,
• 49:11one of the things that has been a
• 49:13mystery in cancer over all of the years.
• 49:14Why do you see certain phenotypes when
• 49:16you have certain settings and not others?
• 49:18And in the case of ASCL one,
• 49:20this is very reminiscent of
• 49:22reprogramming because it's known,
• 49:24for example,
• 49:25that you can put ASCL one into
• 49:29fibroblasts and reprogram them to neurons,
• 49:32but you put them when you put them
• 49:34in keratinocytes.
• 49:34You can't and this has been shown
• 49:36to be due to actually the chromatin
• 49:39landscape at Ascl,
• 49:40one target genes in the different cells.
• 49:42So could something like that be
• 49:44happening in the cancer cells as well?
• 49:46And one of the other questions of
• 49:48course that we have is since Ascl
• 49:50one is important for and neuronal
• 49:54differentiation,
• 49:55it's associated with neuroendocrine
• 49:57differentiation, Is it poising these cells?
• 49:59We didn't see any other, you know,
• 50:02neuroendocrine markers on,
• 50:03but is it poising the cells to
• 50:06undergo that type of change?
• 50:08And so,
• 50:10so some of the things that we're thinking
• 50:12about now and we have experiments ongoing,
• 50:15we have Mark Wiesehofer in the lab
• 50:17who's been thinking about this and
• 50:20working about on this in the context
• 50:22of both prostate cancer where very
• 50:25similar things happen and lung cancer.
• 50:27We're asking how does a chromatin
• 50:29state of a cancer cell affect
• 50:31responsiveness to therapy and plasticity.
• 50:34And so you can have these different cells,
• 50:35you add ASCL one and you can
• 50:37see different things happen in
• 50:38these different cells.
• 50:39And why is that happening?
• 50:41And is there something that we can
• 50:43learn from these cells that then
• 50:44we can apply to human tumors and
• 50:46could we use this information?
• 50:47I'm thinking far a little bit far ahead,
• 50:50but it's something that's in the back of the,
• 50:51my mind is can we predict how a tumor
• 50:54will evolve on treatment with this knowledge.
• 50:58So finally a couple of final thoughts.
• 51:02So what have I told you today,
• 51:04baseline mutations and Co mutations
• 51:06can affect disease progression,
• 51:08drug sensitivity and mechanisms
• 51:09of drug resistance and how can we
• 51:12incorporate this knowledge into
• 51:14clinical investigation and practice.
• 51:16This is something that we think about a lot.
• 51:19There's a vast heterogeneity and
• 51:21complexity of non mutational resistance
• 51:24and persistence mechanisms and
• 51:25we're working to identify them,
• 51:28establish when they are relevant
• 51:29for specific tumors and find
• 51:31vulnerabilities of these and be
• 51:33happy to talk more about these
• 51:35thoughts throughout the day.
• 51:36Today I there are a lot
• 51:39of people to acknowledge.
• 51:41Here are some pictures of lab
• 51:44members throughout the years.
• 51:46Here's a particularly fun one.
• 51:50This was a fundraising picture
• 51:52for a closer to free team that so
• 51:55I thought that was pretty cool.
• 51:57These are Halloween,
• 51:59one of our Halloween parties and
• 52:02other pictures from the we have the.
• 52:05All of the lab has contributed
• 52:06tremendously to all of these
• 52:08efforts over the years,
• 52:09and I'm so grateful to have
• 52:12the opportunity to work with
• 52:13so many talented people.
• 52:15There are lots of people to acknowledge
• 52:18who have contributed to this work
• 52:20in addition to members of the lab,
• 52:23so many collaborators outside of Yale,
• 52:27but in particular everybody here at Yale,
• 52:29which I, I, I really,
• 52:31I hope everybody is on this slide.
• 52:35It's one of the things that I was
• 52:37worried about but want to make
• 52:39sure that everybody is acknowledged
• 52:40here because of the tremendous
• 52:42contributions that makes it such
• 52:44an amazing place to work together.
• 52:47A couple of things that I'd like to say,
• 52:49we have a retreat too on thoracic cancers.
• 52:52On Monday, it's retreat season.
• 52:55It is at West Campus,
• 52:57so you're all invited to join us.
• 53:01We have a team that has been working.
• 53:04Sarah's in here, I think.
• 53:05Sarah Goldberg, Justin Blasberg.
• 53:07We have Glynis Arnold and Melody
• 53:10Noga MENA who's been working
• 53:12to organize this retreat.
• 53:14So we hope you can join us and then
• 53:18save the date for our annual lung
• 53:20cancer workshop on June 12th and 13th.
• 53:23It is also going to be at West
• 53:25Campus here and it's particularly
• 53:28special this year because we are
• 53:31going to be recognizing the 20th
• 53:33anniversary of the discovery of EGF
• 53:35receptor mutations and lung cancer,
• 53:37which has really transformed the field.
• 53:38It's near and dear front to my heart
• 53:41as you can imagine from the talk,
• 53:43but it's really going to be I think a
• 53:46spectacular event with lots of people
• 53:48coming from all over to mark this,
• 53:51this moment.
• 53:52And so we hope that you can
• 53:55participate in that too.
• 53:56Thank you very much and I'll
• 53:58be happy to take questions.
• 54:10Thank you so much, Katie.
• 54:11That was wonderful.
• 54:12Are there questions in the room?
• 54:16Maybe I'll start as a person who
• 54:18knows more about squamous cell
• 54:20cancers than adenocarcinomas.
• 54:22When you talk about P53 mutations,
• 54:25are they always the same
• 54:27in adenocarcinoma patients?
• 54:28And we spend a lot of time
• 54:30in the squamous world talking
• 54:31about disruptive mutations,
• 54:32gain of function mutations. Yeah,
• 54:36we have, I think there's a wide variety of
• 54:40P53 mutations that you see in lung cancer.
• 54:44So they're like different types and
• 54:47have you dissected out if they
• 54:49have different implications.
• 54:50We think the gain of function
• 54:52mutations don't lead to as much
• 54:53genomic instability for example. Yeah,
• 54:55those are things that we
• 54:57haven't studied that much.
• 54:58I think Paul had looked at the
• 55:01different mutations a little
• 55:02bit in terms of outcomes,
• 55:04Paul Stockhammer and I don't
• 55:05think he had found differences in
• 55:07terms of outcomes with Tkis with
• 55:09the different classes mutations.
• 55:12So is the polycommers suppressor
• 55:16name screen that your
• 55:19biggest hit at least in one
• 55:21of the assays was loss of RB,
• 55:24but it looks like in the in the cancers
• 55:27RB loss was relatively infrequent.
• 55:29Does it does that suggest or have
• 55:31you looked at whether there's other
• 55:33dysregulators of the RB pathway that
• 55:35are more common in lung cancer like
• 55:37the Cyclone CDK pathway and that's
• 55:39a potentially targetable approach?
• 55:42Yeah, that's a great question.
• 55:43So it's interesting because RB as you
• 55:47said RB one loss is one of the biggest
• 55:51drivers of tumor growth in our screen.
• 55:55It is also if you look at how frequently
• 55:59it Co occurs with EGFR and P53 mutations,
• 56:02it's one of the tumor suppressor genes
• 56:04that is most frequently Co altered.
• 56:06So none of them go really
• 56:08above the like 10% threshold.
• 56:12We do know, we haven't really looked at
• 56:15other ways in which the P50 in which the
• 56:17RB pathway could be altered in tumors.
• 56:19We haven't really looked at that.
• 56:21What we do know is that if
• 56:25you have tumors with e.g.
• 56:27F, RP53 and RB alterations,
• 56:31those are the ones that have the
• 56:33highest likelihood of undergoing
• 56:35that neuroendocrine differentiation.
• 56:37And so like 1/4 of those will undergo
• 56:39the neuroendocrine differentiation.
• 56:45Any other questions from.
• 56:47OK, Thank you again so very much. Thank you.