Methodology for characterizing clinical differences & disparities for global populations to support disease area prioritization in industry oncology clinical development programs: Insights to inform scientifically driven evidence generation

Introduction Population-specific disparities in clinical research are well characterized-with individuals of European ancestry comprising the majority of genetic and clinical data globally. Disease course and treatment response can vary across individuals of different race/ethnicity and ancestral backgrounds. As the population continues to diversify and healthcare evolves toward personalized medicine, it9s essential that the biological differences among populations, and how these affect disease pathology, experience and outcomes, are investigated early and throughout the development process. Currently, there is no defined standard for characterizing population differences across diseases. Establishing a methodology to systematically assess and consider medically relevant population specific attributes for understudied populations is a critical enabler for the clinical research enterprise and supports greater inclusive clinical research. We established a methodology to assess and prioritize population specific attributes across disease areas (DA) and a framework to support hypothesis generation and population-driven clinical development considerations. Methods Data sources: NCI SEER, WHO Global Cancer Observatory, Global Health Data Exchange Burden of Disease, and published literature were used to assess population specific differences Attributes included: 1) Incidence and prevalence 2) Clinical outcomes, 3) molecular drivers, and 4) access factors. Population elements of race/ethnicity, genomic ancestry, and geographic origin were used to stratify outputs. A grid ranking framework was established based on relative prevalence and incidence and level of concordance or distinction of the above attributes across populations. Summary Methodology was established that included identification and analysis of key population-specific factors to rank DA9s within a grid system. The following diseases were characterized as having disproportionate prevalence as well as biologically plausible population specific differences. Breast Cancer Cervical Cancer Colorectal Cancer Gastric Cancer Hepatocellular Carcinoma Head & Neck SCC Multiple Myeloma NSCLC Prostate Cancer Population specific reports were developed and used to inform business integration process into evidence generation considerations including guidelines for assessments of population level pertinence to study hypothesis, response modification potential, and relevance of biomarker differences. Conclusion The established methodology and framework provides a process and standards to characterize biologically relevant population specific attributes for understudied global populations at the disease level. This approach will support the clinical development environment to systematically approach conduct of scientifically driven inclusion of representative patients in research, ultimately supporting greater inclusion of understudied patient populations. Citation Format: Keith Dawson, Dane Callow, Jimmy Ngueyn, Altovise T. Ewing, Ruma Bhagat, William Boyd, Caroline McCammond, Nicole Richie. Methodology for characterizing clinical differences & disparities for global populations to support disease area prioritization in industry oncology clinical development programs: Insights to inform scientifically driven evidence generation [abstract]. In: Proceedings of the AACR Virtual Conference: 14th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2021 Oct 6-8. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr PO-195.