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Purpose: Prostate cancer translational research has been hampered by the lack of comprehensive and tractable models that represent the genomic landscape of clinical disease. Metastatic castrate-resistant prostate cancer (mCRPC) patient-derived xenografts (PDXs) recapitulate the genetic and phenotypic diversity of the disease. We sought to establish a representative, preclinical platform of PDX-derived organoids that is experimentally facile for high-throughput and mechanistic analysis. Experimental Design: Using 20 models from the LuCaP mCRPC PDX cohort, including adenocarcinoma and neuroendocrine lineages, we systematically tested &gt;20 modifications to prostate organoid conditions. Organoids were evaluated for genomic and phenotypic stability and continued reliance on the AR signaling pathway. The utility of the platform as a genotype-dependent model of drug sensitivity was tested with olaparib and carboplatin. Results: All PDX models proliferated as organoids in culture. Greater than 50% could be continuously cultured long-term in modified conditions; however, none of the PDXs could be established long-term as organoids under previously reported conditions. In addition, the modified conditions improved the establishment of patient biopsies over current methods. The genomic heterogeneity of the PDXs was conserved in organoids. Lineage markers and transcriptomes were maintained between PDXs and organoids. Dependence on AR signaling was preserved in adenocarcinoma organoids, replicating a dominant characteristic of CRPC. Finally, we observed maximum cytotoxicity to the PARP inhibitor olaparib in BRCA2  -/- organoids, similar to responses observed in patients. Conclusions: The LuCaP PDX/organoid models provide an expansive, genetically characterized platform to investigate the mechanisms of pathogenesis as well as therapeutic responses and their molecular correlates in mCRPC. Clin Cancer Res; 24(17); 4332-45. ©2018 AACR .

A PDX/Organoid Biobank of Advanced Prostate Cancers Captures Genomic and Phenotypic Heterogeneity for Disease Modeling and Therapeutic Screening