Integrative Molecular Analyses of the MD Anderson Prostate Cancer Patient-derived Xenograft (MDA PCa PDX) Series

Abstract

Abstract Purpose: Develop and deploy a robust discovery platform that encompasses heterogeneity, clinical annotation, and molecular characterization and overcomes the limited availability of prostate cancer models. This initiative builds on the rich MD Anderson (MDA) prostate cancer (PCa) patient-derived xenograft (PDX) resource to complement existing publicly available databases by addressing gaps in clinically annotated models reflecting the heterogeneity of potentially lethal and lethal prostate cancer. Experimental Design: We performed whole-genome, targeted, and RNA sequencing in representative samples of the same tumor from 44 PDXs derived from 38 patients linked to donor tumor metadata and corresponding organoids. The cohort includes models derived from different morphologic groups, disease states, and involved organ sites (including circulating tumor cells), as well as paired samples representing heterogeneity or stages before and after therapy. Results: The cohort recapitulates clinically reported alterations in prostate cancer genes, providing a data resource for clinical and molecular interrogation of suitable experimental models. Paired samples displayed conserved molecular alteration profiles, suggesting the relevance of other regulatory mechanisms (e.g., epigenomic) influenced by the microenvironment and/or treatment. Transcriptomically, models were grouped on the basis of morphologic classification. DNA damage response–associated mechanisms emerged as differentially regulated between adenocarcinoma and neuroendocrine prostate cancer in a cross-interrogation of PDX/patient datasets. Conclusions: We addressed the gap in clinically relevant prostate cancer models through comprehensive molecular characterization of MDA PCa PDXs, providing a discovery platform that integrates with patient data and benchmarked to therapeutically relevant consensus clinical groupings. This unique resource supports robust hypothesis generation and testing from basic, translational, and clinical perspectives.