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

October 27, 2021

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

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  • 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.