Developing and Validating Prognostic Models for Black women with Endometrial Cancer following Hysterectomy
Project Number1K99CA297013-01
Contact PI/Project LeaderPEERI, NOAH CHARLES
Awardee OrganizationSLOAN-KETTERING INST CAN RESEARCH
Description
Abstract Text
PROJECT SUMMARY
Endometrial cancer (EC) incidence and mortality are rising rapidly in the U.S. Black women are experiencing
the sharpest increase, with incidence and mortality rising 46% and 29%, respectively, from 1999-2015. While
the 5-year survival for EC overall is 80%, for Black patients it is only 63%. Black women are more commonly
diagnosed with aggressive histologic subtypes (non-endometrioid; ~35% vs ~17% non-Black) not amenable to
surgical resection alone. Primary staging and treatment for EC is surgical hysterectomy with bilateral salpingo-
oophorectomy; several adjuvant therapies allow for the tailoring of post-surgical treatment, requiring careful
balancing of prognosis/recurrence risks with adverse effects of therapy to improve outcomes and avoid
mistreatment. However, there are no prognostic tools to inform discussion of adjuvant therapies for Black
women diagnosed with EC. This Early K99/R00 application leverages years of NIH-funding and collaboration
to develop and externally validate prognostic tools for Black women with EC post-hysterectomy. This research
will inform discussion of tailored adjuvant therapies in clinical settings and is foundational for Dr. Peeri to
achieve research independence focused on creating clinical decision-making tools inclusive of minority
populations to reduce cancer health disparities. Our specific aims are to: 1A) build a robust prognostic tool
tailored for Black EC patients using readily available clinical and epidemiologic factors to predict survival 3- and
5- years post-hysterectomy, and to externally validate this tool, 1B) compare model performance for Black
patients with an established tool developed primarily in White women, 1C) develop a web-based survival tool
for clinician and patient use; 2A) assess the utility of adding existing TCGA molecular subtypes to our
prognostic tool for Black women and externally validate this tool, and 2B) use an established clinical tool in our
population as a point of reference for evaluating the performance of our model; 3) Build biomarker-based
prognostic tools to predict overall survival at 3- and 5-years post-hysterectomy in 3A) Black women, expanding
upon the prognostic tool developed in Aim 1, and 3B) White women, expanding on an established tool. The
training goals are to 1) receive training in rigorous prognostic model development and validation methods, 2)
receive training in cancer genomics including advanced methods for whole-exome sequence data, 3) expand
knowledge of biomarkers of clinical significance and machine-learning methods for model development, 4) lay
a foundation to support an independent research career in cancer health disparities and cancer survivorship.
Ultimately, this proposal will advance the career of a young investigator with a strong research background
from postdoctoral fellow to independence.
Public Health Relevance Statement
PROJECT NARRATIVE
The work performed under this Early K99/R00 Career Development Award will yield prognostic tools for Black
women diagnosed with endometrial cancer post-hysterectomy. These tools will inform patient-provider
discussions of tailored adjuvant therapies to balance the risks and benefits of treatment and ultimately improve
survival outcomes and quality of life for Black women, who suffer the worst outcomes.
NIH Spending Category
No NIH Spending Category available.
Project Terms
Adjuvant TherapyAgeBenefits and RisksBilateralBiological MarkersBlack raceC-reactive proteinCancer PatientCancer SurvivorshipCareer MobilityCaringClassificationClinicClinicalClinical DataCollaborationsCollectionDataDevelopmentDiagnosisEndometrial CarcinomaEndometrioid TumorEpidemiologic FactorsEquilibriumEtiologyExcisionFoundationsFundingGoalsHandHistologicHistologyHysterectomyIncidenceInsulin-Like Growth Factor IInvadedK-Series Research Career ProgramsKnowledgeLouisianaMachine LearningMalignant Breast NeoplasmMalignant NeoplasmsMeasuresMemorial Sloan-Kettering Cancer CenterMethodsMinority GroupsModelingMolecularNCI Center for Cancer ResearchOnline SystemsOperative Surgical ProceduresOutcomePatient CarePatientsPerformancePopulationPostdoctoral FellowPrognosisPrognostic MarkerProviderPublishingQuality of lifeRecurrenceResearchResearch PersonnelRiskSalpingo-OophorectomyStagingThe Cancer Genome AtlasTrainingTumor SubtypeUnited States National Institutes of HealthValidationWhite WomenWomanWorkblack patientblack womencancer genomicscancer health disparitycareerclinical biomarkersclinical decision-makingclinically significantcohortdata integrationepidemiologic dataexomeexperiencefollow-upimprovedimproved outcomeinterestlymph nodesmachine learning methodmaltreatmentmodel developmentmolecular subtypesmortalityoutcome predictionpost-doctoral trainingpredictive toolsprognostic modelprognostic significanceprognostic toolprognostic valueprognosticationracial diversitystatisticssuccesssurvival outcomesurvival predictiontherapy adverse effecttooltumortumor exomewomen's diagnosis
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