Development and Application of Genetic Ancestry Reconstruction Methods to Study Diversity of Patient-Derived Models in the NCI PDXNet Consortium

Precision medicine holds great promise for improving cancer outcomes. Yet, there are large inequities in the demographics of patients from whom genomic data and models, including patient-derived xenografts (PDX), are developed and for whom treatments are optimized. In this study, we developed a gene...

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Bibliographic Details
Published in:Cancer research communications 2024-08, Vol.4 (8), p.2147-2152
Main Authors: Lott, Paul C, Chiu, Katherine, Quino, Juanita Elizabeth, Vang, April Pangia, Lloyd, Michael W, Srivastava, Anuj, Chuang, Jeffrey H, Carvajal-Carmona, Luis G
Format: Article
Language:English
Subjects:
Animals
Genomics - methods
Humans
Mice
National Cancer Institute (U.S.)
Neoplasms - epidemiology
Neoplasms - genetics
Precision Medicine
United States - epidemiology
Xenograft Model Antitumor Assays
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Summary:Precision medicine holds great promise for improving cancer outcomes. Yet, there are large inequities in the demographics of patients from whom genomic data and models, including patient-derived xenografts (PDX), are developed and for whom treatments are optimized. In this study, we developed a genetic ancestry pipeline for the Cancer Genomics Cloud, which we used to assess the diversity of models currently available in the National Cancer Institute-supported PDX Development and Trial Centers Research Network (PDXNet). We showed that there is an under-representation of models derived from patients of non-European ancestry, consistent with other cancer model resources. We discussed these findings in the context of disparities in cancer incidence and outcomes among demographic groups in the US, as well as power analyses for biomarker discovery, to highlight the immediate need for developing models from minority populations to address cancer health equity in precision medicine. Our analyses identified key priority disparity-associated cancer types for which new models should be developed. Understanding whether and how tumor genetic factors drive differences in outcomes among U.S. minority groups is critical to addressing cancer health disparities. Our findings suggest that many additional models will be necessary to understand the genome-driven sources of these disparities.
ISSN:2767-9764
2767-9764
DOI:10.1158/2767-9764.CRC-23-0417