Abstract C037: Polyethnic-1000: A New York-based initiative to advance cancer genomics through recruitment of diverse racial & ethnic populations
Authors: Balogun Onyinye , Davis Melissa , Mehallow Michelle , Robine Nicolas , Winterkorn Lara , Oschwald Dayna , Zody Michael , Aparicio Samuel , Maniatis Tom , Varmus Harold , Sawyers Charles , Tuveson David 
Published: January 2023

Recent advances in DNA sequencing technologies have revolutionized approaches to the prevention, early detection, diagnosis, and treatment of cancers. However, our current knowledge about tumor biology, cancer risk, and response to treatment is limited due to significant underrepresentation of non-European populations in genomic cancer research, including clinical trials. The vast majority of samples in publicly available genomic databases have been derived from patients of European descent. These inequities limit our understanding of cancer and the impact of ancestry on the various manifestations of this disease. Moreover, exclusion of minoritized populations may exacerbate health disparities and stymie their ability to equally benefit from participation in trials or the innovations that result from such studies. Through the Polyethnic-1000 (P1000) initiative, we sought to leverage the racial and ethnic diversity within New York City to conduct initial investigations that address the existing disparities in cancer genomics studies. We recruited healthcare facilities to join the P1000 Consortium with a particular focus on hospitals outside Manhattan that served a diverse racial and ethnic population. In Phase 1, clinical and scientific protocols were implemented to collect and process 176 archival tumor samples representing 39 cancer types from individuals who self-identified as “non-white.” This pilot study confirmed our ability to procure and analyze tissues using whole-exome and RNA sequencing. Importantly, we estimated each participant’s percentage of ancestry at the continental and subregional levels by applying ADMIXTURE software to our tumor samples using the reference populations from the 1000 Genomes Project. We observed that genetic information nearly always correlates at least partially with self-identified origins, but genetic classifications are more specific and allow the identification of mixed ancestry. We entered Phase 2 of the P1000 Initiative by launching a research program. The awards allowed investigators in the Consortium to compete for support of new or recently initiated projects designed to identify ancestry-associated genomic determinants in specific cancer types. We funded seven of the competing groups; the projects span eight primary cancers. All but one of the studies are focused on individuals of African ancestry; one addresses lung cancer in East Asian patients. With support from Illumina, we plan to perform tumor/normal whole genome sequencing and tumor RNASeq on 1000 cases. The resulting data set would be among the largest and most diverse collection of full-genome pairs available in oncology. It will be housed at the NYGC with initial access for all members of the P-1000 Consortium then for the entire research community after an embargo period. P1000 has established a framework to enhance interactions among our region’s academic and health centers to advance cancer genomics. These efforts should improve and widen the use of genomics for all, especially currently underserved racial and ethnic populations.

Citation Format: Onyinye Balogun, Melissa Davis, Michelle Mehallow, Nicolas Robine, Lara Winterkorn, Dayna Oschwald, Michael Zody, Samuel Aparicio, Tom Maniatis, Harold Varmus, Charles Sawyers, David Tuveson. Polyethnic-1000: A New York-based initiative to advance cancer genomics through recruitment of diverse racial & ethnic populations [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr C037.