"Health Outcomes Research: bridging emerging medical technologies and real-world cancer care" and "Gynecological Cancer: Translational Science and Pivotal Trials"

October 27, 2021

Information

Yale Cancer Center Grand Rounds | October 26, 2021

Presentations by: Dr. Michaela Dinan and Dr. Gloria Huang

ID7083

To CiteDCA Citation Guide

00:00Today we have two speakers and our
00:02first speaker is Michaela Dine-in,
00:05who's an associate professor
00:06Epidemiology and Co leader of the
00:09Yale Cancer Center Cancer Prevention
00:11and Control Research program.
00:13She joined us from Duke University last
00:15year and is a Health Sciences features
00:18researcher specializing in using
00:20epidemiological methodologies to study
00:22complex datasets with particular expertise
00:24and leveraging existing real-world
00:26datasets to examine cancer outcomes.
00:30Is also a leading researcher lean,
00:33and then I NCI funded study
00:36looking at health disparities
00:37in patients with kidney cancer.
00:39And so I think we'll hear
00:40about some of that today.
00:41So Michaela welcome and I
00:43have to have to unmute.
00:48Great, just pulling up my slides here.
00:53OK, looks like we're ready to rock and roll.
00:56Alright so thank you so much.
00:58Good afternoon everyone.
00:59I'm actually in Chicago right now and
01:02attending the Astro annual meeting.
01:04So technically it's still morning here,
01:07but either way I'm delighted to
01:09be speaking with you today so.
01:11Uhm, as was mentioned,
01:13I'm a health outcomes researcher by training
01:15and I can bucket my current research
01:17projects into three broad categories,
01:19including emerging technology in oncology,
01:21survivorship,
01:22and patient outcomes and molecular
01:25oncology outcomes research.
01:27But the running theme throughout
01:28these example projects is leveraging
01:30real-world data to answer questions
01:32about dissemination outcomes,
01:33costs and disparities,
01:34and how I think about answering
01:37these types of questions using
01:39real-world data resources.
01:40So what is the value added?
01:42Of health outcomes research and while
01:45RCT's are considered higher up in
01:47the food chain than cohort and case
01:49control studies in the traditional
01:51levels of evidence pyramid shown here,
01:53there are many types of questions
01:55that are not feasible to examine
01:57in the context of a trial,
01:58but that are feasible within health outcomes,
02:01study methodologies,
02:02and here are some examples of the
02:04types of questions we can answer about
02:07emerging diagnostics and therapeutics
02:09using real-world data resources.
02:11Randomized trials are required.
02:13Approval of a novel therapeutic agent,
02:15but approvals of diagnostics and
02:17other biomarkers are more complex
02:19and not always evaluated by ARC.
02:21Prior to their approval or
02:23coverage by insurance.
02:25However, even for therapeutic agents,
02:27initial approvals often arise from
02:30RCT comparisons with another single
02:32treatment which may be outdated
02:34by the time approvals received.
02:36In reality,
02:37more and more cancers.
02:39Have increasing numbers of possible
02:41treatment options and combinations
02:42and it's just not feasible to
02:44examine all possible treatment
02:45strategies in a head-to-head fashion,
02:47and oftentimes there's honestly
02:49not adequate financial incentives
02:51to support such trials.
02:52We also know that patients who participate
02:55in RCT's differ systematically from
02:57the average real world patient,
02:59where life and treatment is just
03:01a lot messier as compared to the
03:03highly curated patient population
03:05and controlled environment of an RCT.
03:06And this is an example study,
03:08not mine of a patient of patients
03:10with primary CNS lymphoma treated at
03:12the same institution who received the
03:15same treatment both on and off protocol,
03:17and the investigators showed that
03:19patients who were treated in the real
03:21world practice meaning off protocol.
03:23Or older,
03:23sicker had worse disease and had
03:25dramatically worse survival than
03:27the patients who were treated
03:28on the clinical trial.
03:30So here I have presented an
03:31overview of many different types
03:33of data that can be used to conduct
03:35real-world health outcomes research,
03:37and what I really want to drive home
03:38is that it's important to remind folks
03:40that there is no perfect single data set.
03:42But by leveraging the major strengths
03:44and weaknesses of different data,
03:46different types of datasets
03:47as they currently exist,
03:49or improving upon them,
03:50we can answer some pretty cool questions.
03:53So this is an example of
03:54a past fully completed
03:55study that I conducted in breast cancer,
03:57and this was a five year study
03:59that was funded by AHRQ.
04:00Where we were looking at adoption,
04:01chemotherapy, use and costs
04:03associated with Oncotype DX,
04:04in brand and breast cancer and a
04:07lot has changed in the subsequent
04:09years since this work was completed,
04:10but at the time in CC and guidelines.
04:12Recommended consideration of
04:13chemotherapy and all of early stage
04:16disease patients with primary tumors
04:18greater than one centimeter node.
04:19Negative ER positive disease,
04:21and patients characteristics that
04:22were consistent with chemotherapy.
04:24Candidacy and uncle Type DX was still
04:26relatively new to the scene at this time,
04:28and no one had looked at its
04:29use in real world population.
04:30Case studies.
04:31So let's consider the gaps in
04:33knowledge that existed at the time,
04:35so we know that randomized trials
04:37had confirmed the prognostic and
04:39predictive value of Oncotype DX,
04:40and there had been some single
04:42institution series that suggested
04:44that decreased chemotherapy was
04:45associated with archetype DX use.
04:48However,
04:48there hadn't been any nationally
04:50representative studies conducted.
04:51There were still questions about whether
04:53or not the adoption and diffusion of
04:56Archetype DX was being done equitably
04:57across different subgroups in the population,
05:00and there are questions about
05:01the impact that.
05:02Architect DX was having on chemotherapy,
05:04utilizations and costs.
05:04In the real world.
05:06And finally,
05:06there was limited data on patients
05:08who are 65 years and older.
05:09Because these were underrepresented
05:11in any of the child data.
05:14So in thinking about the types of
05:16questions about architects that
05:17I was interested in looking at,
05:19I chose to use the seer Medicare linked data,
05:21which combines the detailed clinical
05:23pathologic data from this year
05:25registry with the LOGITUDINAL
05:27claims from the Medicare data.
05:29So we use the Medicare claims portion
05:31of the SEER Medicare data to detect the
05:33use of Oncotype DX in our study population.
05:36Now,
05:36there was no specific CPT procedure
05:39code for Oncotype DX.
05:40In fact,
05:41the test is build using the CPT code 84999.
05:45Defined as unlisted chemistry procedure.
05:48However,
05:48using the knowledge that all Oncotype
05:50DX tests are processed by single
05:53provider in a single location,
05:55we were able to use an algorithm to
05:57detect the archetypes DX code in the
05:59Medicare claims data and confirm
06:01that all tests were performed by the
06:03same single provider from the same
06:05single location with 95% of these
06:08tests having identical payment of $3414.
06:11So this was considered a very
06:13creative approach at the time.
06:14Again, this was a while ago,
06:15and.
06:16And I believe ultimately,
06:17this creative approach is what
06:19got the study funded,
06:20but I've seen this approach recreated
06:22for other diagnostics many times signs.
06:23And this is just a side note to suggest
06:25that if you can think of novel ways to use
06:27data that have been around a long time,
06:29you can still make real
06:30contributions to the field.
06:31Interestingly,
06:32the Seer Medicare data now actually
06:34includes the Oncotype DX rescored
06:36data in the data set itself,
06:38but back then this data was
06:40not publicly available,
06:41so we were only able to detect
06:43receipt of testing at the time,
06:44but did not know what the test results.
06:46Actually were so we were able to show
06:48that archetype decks used in the real
06:50world increased over the study period,
06:52particularly with in the younger age
06:54group in the SEER Medicare data.
06:57And since the use of Oncotype DX was
06:58supposed to inform whether or not
07:00a patient received chemotherapy,
07:01we wanted to see how often the the
07:03use of diagnostic or sorry we wanted
07:05to see how the use of the diagnostic
07:08was impacting the use of chemotherapy.
07:10And here we can see that in patients
07:12who would traditionally be considered
07:13high risk due to their tumor size or stage,
07:15that chemotherapy.
07:16He's appeared to decline following
07:19the introduction of architect Deacs.
07:21So in multivariable analysis,
07:22we did not see an overall association
07:24between receipt of Archetype DX
07:26and receipt of chemo.
07:28However,
07:28we did see that patients with
07:30clinical markers of more aggressive
07:32disease such as tumor size,
07:34grade and NCCN,
07:35defined clinical pathologic risk had an
07:38increased likelihood of receiving chemo.
07:40The most nuanced and interesting finding,
07:43however,
07:43was that when we looked at the
07:46interaction between receipt of Oncotype
07:48DX and NCCN defined clinical risk,
07:50we saw that.
07:52Receipt of Oncotype DX was associated
07:54with decreased chemo in NCCN
07:56high risk patients and increased
07:59chemo and NCCN low risk patients.
08:01So at the time it was a foregone
08:03conclusion by many that the use of
08:05Oncotype DX would not only be cost effective,
08:08but also costs saving.
08:10However,
08:10there was a meta analysis of the
08:13ability of AC type DX to reduce costs,
08:15and it revealed that there was
08:17a wide range in the perceived
08:19benefit cost benefits of archetype
08:21deacs according to weather.
08:22A study had been funded by Genomic Health.
08:24The sponsor, which is those studies,
08:27are shown in blue on this graph.
08:29As opposed to other funding sources.
08:31So interestingly,
08:32the five studies that suggested
08:35Archetype DX was cost saving were
08:37all funded by genomic health.
08:39Ultimately,
08:39however,
08:40these were all modeling studies and we
08:42wanted to try to look at real-world data,
08:44so this is important,
08:45because when you look closely
08:46at these modeling studies,
08:4818 of them assume that T stage
08:51and tumor grade had no impact
08:53on chemotherapy decisions,
08:54which we clearly saw in the data
08:56I showed previously was not the
08:57case in our real-world data,
08:59and only five studies.
09:00Accounting for the fact that
09:01architect at DX testing might
09:03actually increase chemotherapy use
09:05in clinically low risk patients.
09:06So what did we find when we looked
09:08at costs associated with Oncotype
09:10DX in the real world setting?
09:12So the main takeaway lesson was that
09:14the impact of these tests depends
09:16strongly on the patient population
09:18and pretest likelihood that a patient
09:20was going to get chemotherapy anyway.
09:23So in patients who were
09:24planned for chemo or high
09:25risk patients, Oncotype DX can
09:28can reduce costs, chemo and costs.
09:31However, for lower intermediate patients,
09:33there is no evidence that Oncotype
09:35DX will reduce costs in actuality.
09:38And it's it's use is actually
09:40associated with higher non cancer costs,
09:42likely due to just general
09:45overall increased health care
09:46utilization in this population.
09:48And then finally using these same data,
09:50we were able to look at questions
09:52regarding what physician or provider
09:54characteristics were associated
09:55with the use of archetype DX and
09:58what we saw was that about 70% of
10:00patients who were receiving Oncotype
10:01DX had the Oncotype DX test ordered
10:04by their medical oncologists.
10:05But we were also able to look at
10:08factors physician characteristics
10:09that were associated with increased
10:11likelihood of receiving Oncotype
10:13DX and these were having been seen
10:14by a surgical oncologist having
10:16been seen having had your surgery
10:18at an academic Medical Center.
10:20Having been treated by a female medical
10:23oncologist and having been treated by
10:25a medical oncologist who was within
10:27five years of finishing their training.
10:29So I'm going to move on to my next example,
10:31which is from my current NCI funded
10:33R 01 where we are examining access
10:35and adherence to oral anti cancer
10:37agents and drivers of real world
10:40disparities in patients with metastatic
10:41renal cell carcinoma.
10:43As is the case in many cancers,
10:46the number of available therapies for kidney
10:49cancers have expanded dramatically over
10:51the past decade and a half and interestingly,
10:54ten of these therapy,
10:55ten of the therapies approved
10:56between 2005 and 2016.
10:58Of those 10.
11:00Seven of them were oral agents and we
11:02can use real world data to look at
11:04issues pertaining to patients ability
11:06to access and then stay adherent to
11:09these potentially lifesaving drugs.
11:10So once again,
11:12let's take a look at what what was
11:14known versus the knowledge gaps
11:15surrounding a a use in patients
11:17with kidney cancer at the time.
11:19So we know we knew that oral anti
11:21cancer agents and we know that they
11:23pose unique challenges to delivery and
11:25also there was clinical trial data
11:27that showed increased progression,
11:29free survival and overall survival
11:31for several different ways and
11:33typically always have shown to have a
11:36more favorable toxicity profile than
11:39traditional cytotoxic chemotherapies.
11:41However their continued.
11:42To be gaps in the knowledge
11:44around whether outcomes,
11:45what outcomes and toxicities looked like
11:48in older and comorbid patient populations,
11:50there were few head-to-head OA
11:53comparisons and there were additional
11:55unknown adherence barriers as well
11:57as impacts of out of cost out of
12:00pocket costs on adherence and how
12:02the impact of non what the impact
12:05of nonadherence had on outcomes
12:07for these patients.
12:08So for this study,
12:09we once again decided to leverage
12:10the strengths of the Seer,
12:12Medicare and the Medicare claims data,
12:14and in this case, Medicare Part D,
12:16which is includes prescription drug claims,
12:18was crucial for this study.
12:20But we also added an additional data
12:22source called the North Carolina Cypher
12:24data now North Carolina Cypher is an
12:26example of a state cancer registry
12:28that's been linked to claims data,
12:29and in this case it's the North
12:31Carolina Cancer Registry data that has
12:34been linked to Medicare, Medicaid,
12:35and Blue Cross Blue Shield data.
12:37So you can see here.
12:39That strengths include the same
12:40detailed clinical pathologic data that's
12:42contained in the SEER Medicare data set.
12:44But for patients of all ages,
12:46we receive Medicare is limited to
12:48those who are 65 years and older and
12:50with unsafe Cypher has patients with
12:52different types of insurance coverage.
12:54Where senior Medicare is limited,
12:55obviously, to just the Medicare population.
12:59So here I show the seer Medicare rates
13:01of utilization of oral anti cancer
13:03agents in patients with renal cell
13:05carcinoma and we also reproduce this
13:07data in the North Carolina cypher
13:09data where we saw highly similar
13:12trajectories and rates of OH agents.
13:14We found that roughly 1/3 of patients
13:16were receiving an oral anti cancer
13:17agent at all within a year of being
13:19diagnosed with advanced disease and
13:21that the majority of these patients
13:23were initially treated with sunitinib.
13:25A multivariable analysis of CR Medicare
13:28factors associated with utilization
13:29did not show evidence of differential
13:32receipt of oral therapies by patient race,
13:34ethnicity, or socioeconomic status.
13:36However,
13:37we did see decreased utilization
13:39in patients who were unmarried,
13:40older, or that lived in the South.
13:43So one of the strengths of the
13:45North Carolina cipher data is that
13:46it includes adults of all ages
13:47as well as private insurance.
13:49As I've already mentioned before,
13:51we adjusted for age.
13:52There were large differences in utilization
13:54by private versus Medicare insurance.
13:57However, in multivariable adjusted analysis,
13:59we saw that there was no difference
14:01in the utilization by insurance.
14:02Instead,
14:03this was likely driven entirely by age,
14:05with older patients being less
14:06likely to receive therapy.
14:08We also observed that frailty and
14:10having multiple kohram abilities
14:12were both associated with.
14:14Decrease to a utilization.
14:15And lastly we looked at patients with
14:17all stages of kidney cancer and saw
14:20that patients who were diagnosed with
14:22stage one disease but that experienced
14:24progression to metastatic disease were
14:26less likely to utilize Inoue within
14:28a year of metastatic disease diagnosis,
14:31and this is likely due to slower
14:33growing disease with a less urgent
14:36need to treat immediately.
14:37Come for oral anti cancer agents.
14:39However,
14:39it's important to remember that
14:41in addition to utilization,
14:42there's also the concept of adherence
14:45or the percentage of time a patient
14:47was taking their anti cancer drug.
14:49We know that in general,
14:50adherence to oral medications is often
14:53far from 100% due to any number of
14:55reasons such as side effects or costs.
14:58We looked at adherence in both the
15:00Seer Medicare and the Cypher cohorts
15:02and we observed slightly higher
15:04rates of adherence within the North
15:07Carolina cypher patient population.
15:09As compared to the CR Medicare cohort,
15:11we think this is largely due to the
15:13difference in age between the cohorts.
15:15As both cohorts showed evidence
15:16of either older patients or
15:18those with Medicare insurance
15:20having lower adherence rates.
15:21North Carolina Cypher was somewhat limited
15:23in power due to the smaller sample sizes,
15:26and it did not examine adherence
15:28by by different agents in
15:30the multivariable analysis.
15:31However, there was evidence of substantially
15:34lower adherence to soften it in both cohorts.
15:36We saw a strong impact of poverty on
15:39adherence within the SEER Medicare data,
15:41but not the North Carolina cypher data.
15:43And although it is unclear why,
15:44we hypothesize that older patients
15:46living on a fixed income may be more
15:50sensitive to financial stressors.
15:51Consistent with this,
15:52we saw that OAS,
15:54with out of pocket costs over $200,
15:57were associated with decreased adherence
15:59within the SEER Medicare cohort.
16:02So these real world datasets also
16:03allow you to look at survival.
16:05And here is a three month landmark survival
16:08curve of all 'cause mortality for a pass.
16:10Open abusers by whether
16:12they received the trial.
16:13Recommended dose of 800 milligrams of
16:16pheasant per day in the three months
16:19following a a initiation for the
16:21patients getting the prescribed dose
16:23for the first three months of treatment,
16:24we saw superior outcomes and survival
16:26was assessed beginning at three
16:28months post postoperative initiation.
16:30In order to avoid introducing.
16:32Immortal time bias in the analysis.
16:35So I think it's incredibly critical to
16:37acknowledge that a key limitation of
16:38all these data sets is that the patient
16:41perspective and the patient voice is missing.
16:43I also feel it's incredibly important to
16:45do our best to include this perspective,
16:47even when working exclusively
16:49with secondary data,
16:50and one way that we address this
16:52for the renal cell carcinoma.
16:53A study was by partnering with patient
16:56advocacy groups who helped us identify
16:58questions that were most important to them.
17:01So,
17:01for example,
17:02these patients and their families,
17:04they wanted to know how often providers
17:05were switching their medications.
17:07Which is something we hadn't
17:08planned on examining,
17:09but we were absolutely capable of
17:11examining in our real-world data set.
17:13So we looked at the request of the patients,
17:16and we found that while only 6%
17:18of RCC patients switched aways
17:20within 90 days of diagnosis,
17:23that number increased to 20% of RCC patients,
17:26switched to their always
17:27within one year of diagnosis.
17:30So now I'd like to move on to an example
17:32of current future work that I'm doing.
17:34So I was recently awarded in American
17:36Cancer Society 5 year Research Scholar
17:38Grant and this grant will be developing
17:40algorithms to inform risk stratified
17:42care for long term cancer survivors.
17:44So this figure was modified from a
17:46paper by Effinger and McCabe which
17:48shows at the top the current model,
17:51care for cancer survivors,
17:52which is more of a one size
17:54fits all approach.
17:55Once the patient is diagnosed
17:57with their cancer,
17:58their care is transferred to an oncologist
18:00for an indefinite period of time.
18:02Little to no ongoing participation
18:04from the PCP.
18:05The bottom shows the proposed
18:07shared practice model care based
18:09on risk stratification,
18:10which helps to inform
18:11the point in time when a
18:12cancer survivors care might be
18:14appropriately transferred back to you or
18:16shared with the primary care physician
18:18with the idea being that the new
18:20model represents both a more efficient
18:22and better quality model of care.
18:24So this figure is from a study
18:26where McConnell and colleagues used
18:28National Cancer Registry data from
18:29the UK and Northern Ireland tourist
18:32stratify patients with twenty of
18:33the most common cancers into three
18:36groups based on overall survival at
18:38one in five years from diagnosis.
18:40And this is just to demonstrate that
18:43crude risk categorization is possible
18:44and is currently being used to
18:46inform treatment in other countries.
18:48So the authors noted that important
18:49caveats of this analysis included
18:51the absence of treatment information
18:52which was not available, and.
18:54That their data was unable to assess
18:56treatment related complications,
18:58both of which I propose to improve
19:00upon in our models for this ACS grant.
19:02So once again,
19:03we return to existing currently
19:05existing knowledge gaps,
19:07which real-world data and outcome
19:08methodologies can help to address,
19:10so we know that Uncle logic and
19:12noncaloric risks vary substantially by
19:14cancer stage and treatment and cancer type.
19:17We also know that cancer site
19:19and stage alone can provide broad
19:21uncle logic risk categories.
19:22However, non uncle logic disease.
19:26Risks have been defined qualitatively,
19:28but not quantitatively,
19:30and cancer survivors.
19:32And we do not know how Uncle Logic
19:35and on non uncle logic risks compare
19:38or compete within cancer survivors.
19:41And there's also a need to estimate
19:44these risks at the point of care.
19:46So we will once again use this year
19:48Medicare and the North Carolina cipher data.
19:50But the new data set addition to
19:52this project will be incorporating
19:54data from the Veterans Health system
19:56and the overarching plan is to use
19:59inputs that are available from
20:00all three of these datasets,
20:02such as cancer or specific variables
20:04like site and stage treatment.
20:06Personal characteristics like age
20:08and gender and race and ethnicity,
20:10and then aging related concerns like
20:13comorbidities and functional status
20:15to develop risk prediction models.
20:17In breast, breast,
20:18prostate and colorectal cancers.
20:20To predict both ankle logic and
20:22non oncologic events,
20:23for which long term cancer
20:25survivors are at increased risk.
20:27So these risk algorithm algorithms will
20:29separate long term cancer survivors into low,
20:31medium and high risk categories to
20:34help inform discussions between
20:35survivors and physicians about their
20:37optimal care going forward and
20:39ultimately the final product will be
20:41a freely available web calculator in
20:43which patients and or physicians can
20:45input their individual information
20:47to help categorize their individual
20:49risk and inform pathways of care.
20:52So next on the horizon for me is
20:54tackling additional unmet needs of
20:56traditional health services research
20:58through novel data linkages and I'm
21:00developing studies that will include
21:02actual physical tumor samples so
21:04that we can run genomic sequence
21:06analysis on them and then link that
21:08additional biologic information
21:10to both tumor registry data and
21:13longitudinal claims data.
21:14So there are a couple existing
21:16resources which
21:16I have already tapped into to get this
21:18work off the ground and the first of which
21:21is the SEER residual tissue repository,
21:22which is a program that used to be funded
21:25by NCI to maintain physical tumor samples
21:27for patients contained in the SEER
21:30registry for three participating sites,
21:32which were Iowa, Hawaii and Los Angeles, CA.
21:34So like I said, the program
21:38consists of pathologic specimens.
21:40These are old specimens were
21:42collected between 1992 and 2006.
21:44I've already.
21:45Mention the participating see registries,
21:47but they do allow the ability to
21:50physically analyze tumor samples and So
21:52what I did was we recently completed a
21:55proof of concept study on a very small
21:58breast cancer cohort to demonstrate
22:00the process for combining the sear,
22:02the Medicare,
22:03and the genomic or biologic data obtained
22:05from running gene expression analysis
22:07on the tumor samples themselves.
22:09So unfortunately,
22:10LA did not participate in this pilot study
22:12due to an inability to procure large enough.
22:15Funds to cover their participation
22:16costs and this left us with two
22:19very distinct and racially and
22:21ethnically homogeneous populations
22:22which were not was not ideal.
22:24We would have liked it to have
22:25been much more representative,
22:26but it did allow us to proceed with the
22:29proof of concept study and here is a brief
22:32summary of some of our major findings,
22:34so this publication is in press and
22:36will be published in two days in JAMA
22:38Network and I'm happy to share that
22:39publication with folks to go through
22:41in more detail once it's published.
22:43But you can see that our major findings.
22:46Really show how we were able to
22:48leverage the different aspects of
22:50these three different data linkages.
22:52The three different datasets that
22:53we linked together so we were able
22:55to show from the Medicare claims
22:57data that symptomatic detection of
22:58breast cancer was associated with
23:00a higher mortality hazards ratio
23:02as from the SEER registry data.
23:04We were able to show that.
23:07Low levels of high school graduation
23:09rates were associated with a higher
23:11mortality mortality hazard ratio and
23:13then from the tumor samples and the
23:15genetic analysis that we conducted on these,
23:18we were able to show that androgen
23:20receptor macrophage set of toxicity and T.
23:22Rex signaling were all associated
23:24with reduced mortality.
23:25But the key thing that I want to
23:27highlight here is that factors
23:29related to socioeconomic status and
23:31screening access remained associated
23:33with mortality even after adjusting
23:34for clinical and genomic factors.
23:38So what does the future look like
23:40for this work?
23:40Well,
23:41I'm getting ready to submit a
23:42narrow one which would leverage the
23:45sear virtual tissue repository and
23:47proposes the first in kind linkage
23:48ever of the tumor samples with ceron,
23:51Medicare longitudinal claims.
23:53So the server consists of
23:55seven participating.
23:56See registry, so we're up to 7 from 3,
23:59and the pathologic specimen location
24:01is known for the most recent 10 years.
24:04So this is, this is the the oldest.
24:06The tissue samples are ten years old.
24:09But the collection is ongoing,
24:10so these are recent tissues.
24:12And once again we must physically
24:13request and fund the acquisition
24:15of the pathologic specimens from
24:17the pathology labs storing them.
24:18But what are we proposing to do? So?
24:20We're calling this a retro genomic approach,
24:23which we are defining as a combination
24:25of population level cohort studies
24:27followed by retrospective retrospective
24:29selection of patient cases in
24:32which to pursue genomic analysis,
24:33and this allows us to bypass a common
24:35weakness of traditional trials where
24:37patients are assigned to specific.
24:39Groups and then we wait to see what
24:41outcomes they have and this approach
24:43we can use the Medicare claims data
24:45to cherry pick specific outcomes of
24:47interest and then go and pull the tumor
24:49samples for the patients who experience
24:51these outcomes in the real world
24:53and study which treatment patterns,
24:55SES factors,
24:56or clinical pathologic characteristics
24:58appear to be driving those outcomes.
25:00And in the case of RRCC proposal,
25:02that we're getting ready to
25:04submit in February, February,
25:05we're going to look at two rare
25:07events experienced by patients
25:08related to amino therapy.
25:10Namely,
25:10severe IO toxicities and durable responders,
25:13so we're calling this project
25:15the virtual siert issue,
25:16registry Genomics and Medicare cohort,
25:18or a Verge cohort.
25:19And as I mentioned,
25:20our first application to go in
25:22will be in renal cell carcinoma
25:23since this study will be following
25:25on the heels of my current R 01,
25:27but our intention always has been and
25:28remains to have several different bridge
25:30cohorts across different disease sites.
25:32Answering all types of
25:34different clinical questions.
25:35So in summary,
25:36there are many questions relevant
25:38to cancer care that can be
25:40informed and enhanced by real
25:41World Health services research.
25:43Many questions cannot be feasibly or
25:46ethically addressed by clinical trials alone,
25:49and novel linkages may pave
25:50the way to novel opportunities
25:52in health services research.
25:53There are several datasets that
25:55are available for research in
25:57real world outcomes data and
25:59each data has its own strengths,
26:01weaknesses,
26:02and nuances that you need to know how
26:04to work with in order to get the best.
26:06And most accurate data and then
26:08the incorporation of genomics
26:09and biology into health service
26:11research is on the horizon.
26:13With that,
26:13I want to thank the team members
26:15who participated in all the various
26:16studies that I that I presented today.
26:18All of the work I do is team based
26:20science and I couldn't do it without
26:22the clinical collaborators and the
26:24support staff who are helping me with
26:25this work. Thank you for your time.
26:29Thank you Michaela.
26:30Very interesting work.
26:32If there are any questions, I I guess
26:34what we do is we type them into the chat.
26:38While we're waiting now to question.
26:40I I thought the most interesting thing
26:43he showed was the effect of ZIP code.
26:47The five fold increase in mortality.
26:49Yes, 'cause of course in within
26:51the ZIP code there are many people.
26:52There's a range of educational levels,
26:55so if you if you just actually broke it down.
26:59Are you able to break it down by actual,
27:01whether or not a patient
27:02has graduated or not?
27:03'cause I would assume then that
27:05difference would be much greater.
27:06Yeah, I mean,
27:07so obviously that would be ideal.
27:09That's just that's just a limitation
27:10of this year Medicare data,
27:11so the the SES data is in this
27:15available in their Medicare data,
27:17and I could talk a whole another
27:18half hour about this.
27:19Is zipcode level information,
27:21so it's not ideal,
27:22but it does give you a sense of you.
27:24You get zipcode level information
27:26about high school graduation,
27:27zipcode level, information about poverty.
27:29Uhm, about, uh,
27:32like the racial or ethnic makeup of
27:35a neighborhood somebody lives in.
27:37So obviously it's a proxy.
27:38It's not ideal,
27:39but it's it's better than what's
27:40in a lot of other datasets,
27:42so it's still
27:43despite those very very striking difference.
27:46We have a question from Laos.
27:48Yes, Titan, congratulations
27:50is clearly very exciting.
27:51What you described I,
27:52I wonder who is your year
27:54collaborator Co investigator for the
27:56genomic analysts piece of Euro one who
28:00will actually do the the sequencing
28:02and data analysts and
28:03linking to the clinical data.
28:04Yeah, so we're still working
28:05through the details of that,
28:06but we've been talking to all the
28:08various cores and thinking about
28:10exactly what what we want to do
28:12in terms of the genomic analysis.
28:15Obviously there's a couple things
28:17that are going to weigh in.
28:18This is a big study.
28:19It like I said, it's going to involve.
28:21It's all ecipes from all of the six
28:23registries I mentioned are all on board.
28:26We're going to have,
28:27so that'll be six sites,
28:29and so a lot of this unfortunately
28:31is gonna be driven by what we
28:33can afford in terms of, you know.
28:34So we're going to start with a
28:36very focused analysis and then
28:37from there you know.
28:38I'm hoping to build on that with
28:40either administrative supplements
28:41or other funding mechanisms to
28:43build out and expand on that,
28:44so that's still that that specific pieces
28:46build still being in development right now,
28:48but we're.
28:49Talking with all the Yale course.
28:51There's a lot to follow up
28:53with you because you know,
28:54I I couldn't write the Yale Genetics
28:55Genomics program and you may know
28:57that we have a similar large
28:58initiative that's run by like Murray.
29:01The generations project,
29:03and I think there is a lot of synergy
29:04that you could you could leverage.
29:06Yeah, it'd be great to talk,
29:07and we're still we're still
29:08developing that specific piece.
29:09I would love to talk about it more.
29:12Thanks Flash any other questions or comments?
29:19How the work is obviously critically
29:22dependent on how good the datasets are.
29:26Which you have not a lot of control over
29:28other than select which ones to use.
29:29I mean for example other VA.
29:32How does that compare to see?
29:33Or how does that compare to Medicare?
29:34Or are there systematic differences?
29:37Yeah, so great question.
29:39Again, I have a whole other talk just
29:42talking specifically about these.
29:44Uhm, so you know it.
29:46It's all about like I said,
29:47like knowing the datasets well knowing
29:48what their strengths or weaknesses are
29:50and knowing how to leverage them so
29:52specifically for the wrist ratification
29:53grant that I'm talking about where
29:54we're going to be using serum,
29:55Medicare cipher and the VA data,
29:57we're specifically focusing on the
29:59variables of interest on things
30:00that we know we can get out of.
30:02Each of those three datasets, right?
30:03So because we want to be able to
30:05like develop and then validate
30:07the risk prediction algorithms.
30:08I mean, I, I said it from the beginning.
30:10There's no perfect data set.
30:11There are things that are
30:12really strong about this year.
30:13Medicare data.
30:14It is probably the most widely used
30:17real-world data set for oncology.
30:18Specific research is an
30:20incredibly strong data set,
30:21but the two big limitations that
30:23everyone can tell you right off the
30:24top of their head is that it's limited
30:26to those who are 65 years and older.
30:29It's Medicare only,
30:29and then the other limitation is
30:31there's a pretty significant lag
30:32with the data because it relies on a
30:35linkage that's done every two years at NCI,
30:36so it's usually about three
30:38to four years behind, right?
30:39So if you're trying to look
30:41at emerging technologies,
30:41it can be a little bit of a nuisance.
30:43So from the current R 01.
30:45Using Seer Medicare data.
30:48Actually getting ready to purchase
30:49a cohort of the Medicare 100% data.
30:51So the limitation to that data set
30:53is going to be that it doesn't have
30:55the seer registry information,
30:56so we're not going to know things
30:59like stage or like other clinical
31:01pathologic variables.
31:02However,
31:02the whole you know we're trying
31:04to fill in the gaps that we know
31:06exist from the previous work that
31:07we did with the other datasets,
31:09which is the lag that we saw in in this era.
31:11Medicare data and the North
31:13Carolina cipher data,
31:13so we can't look at O as in the
31:15context of current immunotherapy,
31:17which we know is playing a huge role.
31:19In a renal cell carcinoma
31:21treatment right now,
31:22so the Medicare claims data,
31:24while it will have different gaps,
31:26is going to allow us to look at other
31:29questions alongside of what we've
31:30already done to look at how aydelette
31:33OAA utilization and adherence looks
31:36in the context of amino therapies.
31:39So it's just about figuring out,
31:40like it's just about acknowledging
31:42where the limitations exist,
31:43and then figuring out a way to
31:45kind of fill that information in.
31:48Terrific,
31:48thank you very much.
31:49Very interesting talk.
31:50We need to move on to our second
31:53speaker who's Gloria Wong and Gloria
31:56is a social professor of OBGYN
31:59and reproductive sciences here,
32:00and she specialized in the
32:02treatment and prevention of ovarian,
32:04uterine, and cervical cancers.
32:05She's a board certified gynecological
32:08oncologist who performs minimally
32:10invasive surgery and her research
32:11interests are in Dimitriou,
32:13SIS associated and ovarian cancer
32:14in the prevention and treatment
32:16of endometrial cancer recurrence.
32:18So Gloria, the floor is yours.
32:22Hey, thank you so much for the
32:24introduction and I really enjoyed
32:25the first talk and learns a lot.
32:28So let me just see if I can
32:30bring up my slides here.
32:36Can you see those? Yes,
32:38could you put in presentation? Yes perfect
32:42great alright. Well today I wanted
32:44to talk about a couple of topics
32:47on near and dear to my heart,
32:49which is translational science and
32:52pivotal trials and gynecological cancer.
32:57I have my disclosures on file with the
33:00CME office, none of which are related
33:02to the content of this presentation.
33:07In this talk, I want to first give a
33:10epidemia brief overview of the epidemiology
33:13and current trends in GYN cancer.
33:16Challenges and successes in the
33:18field of GYN Cancer Research,
33:21including highlighting some
33:23recent practice changing trials
33:25and example of how translational
33:29science in my personal experience,
33:31can be a driver for clinical trial
33:34development and team science,
33:36and then also just touch briefly
33:39on some resources available
33:41for translational research.
33:45And these are the learning objectives.
33:52Endometrial cancer has been
33:54increasing in both incidence and
33:56mortality in the United States.
33:58Currently, the lifetime risk of developing
34:01under mutual cancer is about one in
34:0332 and over 800,000 women in the US
34:06are living with endometrial cancer.
34:08Ovarian cancer mortality has slightly
34:11declined in recent years and currently
34:14the lifetime risk of developing
34:17ovarian cancer is about one in 83
34:20and over 200,000 women in EU S R.
34:22Living with ovarian cancer. In EU.
34:25S. Thanks to HPV vaccination
34:27and cervical screening.
34:29The cervical cancer rate has declined
34:32over the past decades to about 167 women.
34:36However, there are significant
34:39disparities related to access
34:42of care and affecting outcomes.
34:46She whined.
34:47Cancers arise from the reproductive
34:49tract organs, including the ovary,
34:52fallopian tube, uterus,
34:53cervix, ***** and vagina,
34:55and these organs are remarkable in
34:58their ability to respond rapidly
35:00to endocrine signals, produce sex,
35:03hormones and their remarkable capacity
35:05for proliferation, regeneration,
35:07and morphological changes,
35:08and some of these do relate to
35:11underlying risk factors and protective
35:13factors for GY and cancers.
35:15Full fearing cancer,
35:17there's a correlation with increased
35:20lifetime ambulatory cycles,
35:21whereas oral contraceptive use,
35:24pregnancy and risk,
35:25and breastfeeding decrease risk.
35:29A MWe now that.
35:32Term line genetic testing has
35:34become much more widespread and may,
35:36you know, be available to the general public.
35:39It is available now for out of
35:42pocket cost for you know about $250
35:46to determine if one carries a BRCA
35:49one or two mutation and for those
35:53patients risk reducing surgery is
35:55highly protective for women at average risk.
35:58There is a benefit to
36:01opportunistic salpingectomy so,
36:02uhm,
36:03a surgical removal of the flippin
36:06tubes at the time of other pelvic
36:10surgery for benign indications.
36:13Endometrial cancer is linked to the
36:18rising obesity rate unopposed estrogen
36:21as well as hereditary factors,
36:24and we know that use of progestin
36:27containing oral contraceptive
36:28pills or progestin IUD can offer
36:31protection as well as risk reducing
36:33surgery for patients at higher risk.
36:36And cervical cancer can be really
36:41eliminated with widespread implementation
36:43of HPV vaccination and cervical screening,
36:46which currently consists mainly of
36:49liquid cytology and high risk HPV detection.
36:54We are still facing notable challenges
36:56in the fields of GI and cancer,
36:59and I'm going to focus today on and a
37:02mutual cancer which has an increasing
37:04incidence and mortality rate as well
37:07as substantial racial disparity in outcomes.
37:11However,
37:11this is buffeted by recent successes
37:14and pivotal trials in GI and cancer
37:17in just in the past 18 months alone,
37:20we've seen new first line maintenance
37:23therapy options for ovarian cancer.
37:25New indications for immunotherapy,
37:27including for mismatch repair,
37:30proficient at a mutual cancer,
37:32as well as new first line and second
37:34line standard of care for cervical cancer.
37:36So really quite amazing how many.
37:40Pivotal trials have resulted in
37:43the recent 18 to 24 months leading
37:46to practice changing.
37:50Approaches,
37:51so in 2000 end of 2019 the results
37:56of Primon Paolo one were published
37:58in the New England Journal,
38:01leading to the approval of two different
38:04options for first line maintenance
38:06therapy of epithelial ovarian cancer.
38:08Fallopian tube for primary piratini,
38:10oh cancer. Following complete or
38:12partial response to first line platinum
38:15based chemotherapy, the new rap rib.
38:19Demonstrated a significant improvement
38:22in progression free survival in both
38:25the overall intent to treat population
38:28and the homologous recombination
38:30deficient population with a hazard risk
38:34of 0.43 in progression free survival.
38:39Come with clear divergance of the
38:43progression free survival curves.
38:46Similarly, Palo one which tested elapp rib
38:50and bevacizumab for first line maintenance,
38:54showed remarkable
38:55improvement and progression.
38:57Free survival on the upper
38:59left in the bracket.
39:01Mutated population hazard ratio
39:04of 0.31 and on the lower right.
39:07Patients without a BRAC mutation.
39:09But with a molecular test demonstrating.
39:13Homologous recombination deficiency
39:16as tested by genomic instability also
39:20showed a progression free survival
39:23benefit with a hazard ratio of 0.4.
39:35And outcomes for patients who,
39:37unfortunately often prevent present
39:40with advanced stage ovarian cancer,
39:42and we know that upon recurrence
39:46becomes more difficult to treat and
39:49more likely to be chemo resistant.
39:54In mutual cancer, just to review some
39:57of our recent exciting new options.
40:00And this has been really a big deal
40:04because actually progress has been
40:07quite slow and endometrial cancer.
40:10Progestin therapy Megace was approved.
40:14You know, many decades ago for
40:16palliative treatment of enemy,
40:18enemy, troll, and breast cancer.
40:21However, really many decades
40:23elapsed without any new trials,
40:26new indicate indicated therapies for
40:29endometrial cancer of a big benefit
40:32for our patients without mutual cancer,
40:35with seen with the accelerated
40:38approval of pembrolizumab.
40:40For a minute, solid tumors that were
40:44mismatch repair deficient as about
40:4720% of endometrial cancers are,
40:50or microsatellite instability
40:52high or more recently,
40:54with the addition of the accelerated
40:57approval for the tumor mutation burden high.
41:00Uhm?
41:00Tumors more recently this.
41:04Here we have an additional
41:07option for mismatch repair
41:09deficient and demetral cancer,
41:12just Starla Mob,
41:13which received accelerated approval
41:16in August and then most recently
41:20the keynote 775 updated results were
41:24presented at ESMO following previous
41:27presentation at SGO showing combination.
41:31Of pembrolizumab and lymphatic nib.
41:35Showing actually with this combination.
41:38In proficient mismatch repair.
41:41Proficient endometrial cancers.
41:43Uhm,
41:44an improvement in overall survival,
41:46leading to regular approval of
41:49this combination for patients with
41:51endometrial cancer that is not
41:53MSI high that is mismatch repair
41:57proficient and have disease progression
41:59following prior systemic therapy.
42:05Next, I want to move into how we,
42:09as clinicians scientists, participate.
42:11And a example for trial in
42:15progress that I'd like to share.
42:18So I have a couple of different
42:20projects moving into clinical trials.
42:23This one that's currently in
42:26enrolling in clinical trial.
42:29And emerged from what began as a
42:32collaborative team science project,
42:34funded by a narrow one and then
42:37another trial, which I'm in the
42:41process of moving towards the clinic,
42:43which is which I won't talk about today,
42:47which was based on translational
42:48science done in my lab.
42:50Supported by DoD grant.
42:52For this study,
42:54which began quite a long time ago,
42:56UM, I collaborated with, UM,
43:01Epidemia Cancer epidemiology experts,
43:02and we wanted to ask the
43:05question of what could,
43:07what we know about the development
43:10of endometrial cancer and how
43:12obesity is a major risk factor
43:15for Type 1 endometrial cancer
43:17which has been increasing steadily
43:20and underlies the primary.
43:22Increase in the endometrial cancer
43:25incidence as shown here in this graph.
43:30See. A man is dorceau tick tick
43:34tick lining rate of hysterectomy
43:37is another contributing factor.
43:39Uh, what was known?
43:41And for many studies,
43:42including prospective study of
43:44the Women's Health Initiative,
43:46that some of the underlying biological
43:49mechanisms linking obesity to endometrial
43:52cancer include increased estrogen
43:55levels increased by availability of
43:59estrogens and insulin resistance.
44:01Uhm, and the question that we asked was,
44:04do these factors that underlie the
44:07development of endometrial cancer.
44:10Do they play a role in the
44:13recurrence and progression of women
44:16diagnosed with endometrial cancer?
44:18For this study,
44:20we utilized the tissue by
44:22repository of the GOT 210 study.
44:25This is a study that was over
44:2960 sites around the USFRGOG
44:32Gynaecologic oncology group sites,
44:34now under the auspices of NRG
44:38Oncology and enrolled patients who
44:41were undergoing standard surgical
44:43care for endometrial cancer and
44:45prospective specimen banking was.
44:48Performed and sent to a
44:51centralized tissue bank,
44:52the jioji tissue bank.
44:55And and prospective epidemiological
44:58surveys and outcomes.
45:00Treatment and outcomes data was
45:02obtained in order to facilitate
45:04translational research,
45:06including a variety of molecular
45:09and genetic genomic assays
45:12and data integration.
45:18So we proposed a study
45:21within this G210 cohort,
45:24which we obtained funding for,
45:27and this focused on the patients
45:29who had endometrioid Histology,
45:31and we investigated the sex hormone
45:34and insulin insulin like growth factor,
45:38signaling pathways implicated in the
45:41development of endometrial cancer,
45:43to determine if these factors.
45:45More related to the recurrence or
45:48progression of higher risk and a
45:51Metroid under mutual cancers and
45:53this study included over 800 women,
45:55of whom 35% experienced a recurrence
45:58in a follow-up of over five years.
46:06Or the, UM, the methods?
46:10The models were adjusted for known
46:12clinical risk factors of recurrence,
46:15including age, stage and grade,
46:17which were all significant risk factors
46:21for recurrence and just to summarize,
46:24some of the interesting findings
46:27which we presented at an ASCO
46:30plenary and we published this
46:33year in cancer epidemiol AMPDCEP.
46:35We found that circulating estradiol is
46:38positively associated with recurrence risk,
46:41independent of other factors,
46:44and in addition,
46:45a particular tissue biomarker that I
46:48was interested in based on some of my
46:51laboratory research that phosphorylated
46:53expression of insulin receptor,
46:55IGF one receptor was also independently
47:00associated with recurrence risk.
47:02And this is an example of immunohistochemical
47:06staining for the phosphorylated
47:09activated form of the receptor.
47:12Because of the, you know,
47:14large number of patients we did utilize
47:17high throughput approaches for this study,
47:19which included construction
47:21of tissue microarrays and.
47:25And in real time PCR.
47:29So the translational impact of these
47:31findings is that we identified
47:33novel sex hormone and insulin,
47:35IGF axis tissue and circulating
47:37biomarkers of recurrence in a
47:39prospective study of high stage enemy
47:42troydan mutual cancer and this led to.
47:46A motivation to test strategies to
47:49target these pathways for prevention
47:52and treatment of endometrial cancer
47:54and endometrial cancer recurrence.
48:01Come in my lab.
48:03We looked at different potential
48:07therapies for treating and demetral
48:10cancer that could be superior to
48:13the previously used strategies.
48:15So the most commonly used strategies
48:19in in the past have been protesting
48:23agents aromat ACE inhibitors or
48:26combination tamoxifen and megace,
48:29and all of those.
48:31Resulted in really modest
48:33efficacy with progression.
48:35Free survivals even in the first
48:37line setting of around three months,
48:40so this indicated a need for more effective.
48:44Effective approaches for endocrine
48:46therapy and we found both in cell
48:50line models demonstrate we found that
48:53combination cyclin D kinase CDK 46
49:00inhibition with AROMATISSE inhibitors
49:02was potently synergistic and endometrial
49:05cancer cell lines and and this is.
49:09Something that it's been very
49:12successfully implemented.
49:13Of course,
49:15in estrogen receptor positive breast cancer.
49:18And this just shows in vivo data of
49:21showing on the Y axis the tumor volumes
49:25of the endometrial cancer xenograft.
49:28And this was a RB wild type.
49:31As expected,
49:32we found that RB mutant mutual
49:35cancers are not responsive to this
49:37combination and you could see
49:40in the red that the combination
49:43therapy was significantly superior
49:45to either agent alone and.
49:48Both and much was really able to
49:51inhibit growth of this aggressive
49:54endometrial cancer xenografted and this
49:57is work we presented at the AACR meeting.
50:01And this led me to initiate a collaboration
50:04guided by valuable input from,
50:07you know my division colleagues here at Yale,
50:10who of course are leading clinical
50:14researchers as well as colleagues
50:17and in breast cancer like Doctor
50:22Pusztai and my colleague Dr Santine,
50:26incorporating their input,
50:27I was able to successfully submit a concept.
50:30For a clinical trial for two to be
50:36supported by Lilly and in collaboration with.
50:41Leading clinical trialists in June
50:43ecology and the in the Jioji group,
50:46which is our major cooperative
50:49group for research.
50:51We we actually were able to successfully
50:56propose and activate an investigator
51:00initiated trial which is GOG 3039,
51:03a phase two study of abemaciclib
51:05in combination with lectures on
51:08advanced recurrent or metastatic
51:11endometrioid in Dimitriou cancer.
51:13This is a phase two single arm trial
51:15to evaluate the efficacy of this
51:17drug combination for endometrioid and
51:19imaginal cancer with dosing based
51:21on the current FDA approval for
51:23combination therapy and breast cancer.
51:27The study endpoints is to evaluate
51:30the efficacy and in addition,
51:32the translational research component,
51:34which is all being done here at Yale.
51:39We are. Collecting longitudinally
51:44whole whole blood for cell free DNA as
51:49well as FFP of the tissue samples for
51:53exploratory analysis and identification
51:55of novel biomarkers of response.
51:58And how does this trial the JIOJI 3039 trial
52:02fit into the rapidly evolving landscape
52:05of treatment for endometrial cancer?
52:07Well surgery, hysterectomy,
52:09removal of the tubes and ovaries,
52:12and nodal valuation is still the
52:14cornerstone of patients presenting
52:16with resectable ended mutual cancer.
52:18Following surgery,
52:19low end and intermediate risk
52:22patients are managed with observation,
52:25while high intermediate risk
52:27patients standard of care.
52:29Some receive radiation therapy or
52:31vaginal breakey therapy with the
52:33potential benefit of the additional
52:35of pembrolizumab for mismatch repair.
52:38Deficient patients being evaluated
52:40in this trial we have open here,
52:44which is the Gio 24 high risk higher
52:50risk patients following surgery
52:52who are fully respected.
52:54Admin therapy includes chemotherapy,
52:57usually tax on carboplatin.
53:00With a mentor,
53:02village individualized radio
53:03radiation therapy,
53:05often including pelvic radiation,
53:06if there's pelvic nodal involvement
53:08and whether or not pember Lism AB is
53:11going to offer additional benefit
53:13to reduce the risk of distant
53:16Mets in these higher risk women is
53:19being evaluated in keynote.
53:21E 21 and what about first line therapy
53:24for advanced patients measurable disease,
53:28metastatic disease,
53:30or recurrent disease?
53:32So the standard of care currently is
53:36chemotherapy with GOG 209 showing tax sale,
53:40CARBO doublet therapy as to double as
53:44adopted from ovarian cancer is seems to
53:47be more tolerable than triplet therapy.
53:50So that's become the standard of care
53:52and whether or not pember lism AB.
53:55Will improve outcomes in these
53:57patients who have a very high
53:59risk of progression and recurrence
54:02is being evaluated in giot, oh.
54:05Eighteen also actively enrolling and
54:08in this patient population where NCCN.
54:11Guidelines also described hormonal
54:13therapy as an option.
54:16Would definitely consider Geo G39 for
54:19these patients who would be eligible.
54:25And what about in the second line setting?
54:28Currently we have standard of
54:30care options for patients who
54:32progressed on previous chemo and
54:35those include for mismatch repair,
54:37deficient pembrolizumab or just Starla mad.
54:41And then for the MMR proficient,
54:43we saw that pembrolizumab and inland
54:47vatnik combination performed better
54:49than physicians choice of second line
54:52chemo in the GY and art portfolio.
54:55We have a number of biomarker
54:58driven therapies being evaluated
55:00in a phase two setting,
55:02and these are led by Doctor Santine,
55:04a fully receptor alpha targeting
55:07antibody drug conjugate,
55:09as well as a trope 2 targeting anti
55:13antibody drug conjugate and certainly
55:16for endometrioid endometrial cancer
55:19would would would recommend consideration
55:23of GOG 39 for these patients.
55:26So patients are eligible for GOG 3039
55:31with up to two prior systemic regimens,
55:34one of which could have been chemo,
55:36one of which could have been immunotherapy.
55:39And we actually have activated over 20
55:43sites of the 25 selected sites and have
55:48really been having rapid accrual with the.
55:53Current rate of accrual
55:55exceeding our expectation of one,
55:57and it's currently one to two
55:59patients per week.
56:00For this trial, which,
56:02if it goes to second stage,
56:04would enroll a maximum of 52 patients.
56:08I just wanted to briefly touch on
56:10that since this is relatively new.
56:12Is this NCTM navigator or clinical
56:15trial specimen resource and it's
56:18available for validation of
56:20hypotheses following already completed
56:23exploratory and pilot studies,
56:25and this includes a very vast
56:27number of specimens,
56:28including a lot of the specimens that were
56:31transferred over from the jioji tissue bank,
56:34and there is a workflow available.
56:38For exploring what specimens are
56:42available and submitting for
56:45for access to these specimens,
56:47for for addressing research questions
56:50that may require large number of
56:53samples that are collected in a
56:56very rigorous way and then,
56:59how do we fund translational research
57:02in the area of some declining support?
57:06One of the mechanisms.
57:08Which has been super valuable for
57:11supporting translational support.
57:14Is this poor mechanism,
57:16which of course yellows been very
57:18successful and has spores and head and neck,
57:21lung, and skin cancer.
57:23There are very few GYN funded spores,
57:26currently only one and ended meet
57:28real one in cervical,
57:305 in ovarian and there's one new.
57:33Sporen that focuses on health disparities
57:37and endometrial Varian cancer.
57:40So I hope I've relate some of my
57:43enthusiasm for team science and
57:45its essential ingredient for
57:47translational science and conduct of
57:49clinical trials for gene cancers,
57:52which are relatively rare
57:54cancers and really way for having
57:57exciting and meaningful impact.
58:00And I hope I've,
58:01I hope to yell at people who are
58:03interested in collaborating with.
58:05Contact me in my emails listed here.
58:10Thank you Gloria. Very interesting,
58:12very exciting to see the progress
58:14that's been made and all these
58:16trials that are underway.
58:17They're underway, people can please.
58:21Type your questions into the chat.
58:23While we're waiting, you might want
58:24to talk to Roy Herbst if you haven't.
58:27He's sort of taking the lead on
58:29trying to organize new spores and
58:31has quite a bit of experience,
58:32so he might be someone to talk to.
58:34Be great to have this poor in this
58:36in this area in the Piola trial.
58:38It it it was comparing bracket positive.
58:41Projecting negative patients.
58:42Was that bracket one or two or or both?
58:45Did they they stratify that?
58:49So in the data that was
58:52published in the paper,
58:54at least not in the main manuscript.
58:56I don't recall seeing a stratification
59:01of the Braca one versus bracket two.
59:05They did show the hazard ratios and
59:09PFS curves for a few different groups,
59:14and that included the bracket
59:17tumor mutation positive.
59:19The bracca tumor mutation,
59:21positive and HRD positive and
59:24then the bracket to mutation.
59:27Negative or wild type and HRD
59:32positive and then for so.
59:35The UM for that trial, the, UM,
59:39the benefit was seen in the Braca positive
59:44Braca mutated or the HRD positive,
59:49which in that trial was
59:51determined by the myriad.
59:53My choice HRD thing.
59:55Uhm and there was not a clinical
60:00benefit in the HR proficient.
01:00:03Braka wildtype group.
01:00:05But that's an interesting question
01:00:08about if there are differences
01:00:10between Bracha one or two.
01:00:13Uhm, mutated,
01:00:14which I'm not sure I'll
01:00:16look into that though.
01:00:17OK, alright, good. There any other
01:00:21questions from the audience?
01:00:26If not, will thank you Gloria.
01:00:28It was very interesting and also Michaela.
01:00:29I thought we had a terrific series today
01:00:32and we'll see you all next week, bye. I.