Deep learning-based prognostic model for management of men with metastatic castration-resistant prostate cancer

The management of metastatic castration-resistant prostate cancer (mCRPC), especially the vulnerable patient population with diseases progressing on next-generation hormone therapies, remains a challenging clinical task. Recently, deep learning (DL) has revolutionized bioinformatics studies of cancer genomics given its unprecedented ability to characterize complex nature of high-dimensional genomics data. This project will test the hypothesis that a sophisticated DL model captures intricate patterns of mutations that are predictive of prognosis. The goal of this study is to develop a novel DL prognostic model for mCRPC progressing on next-generation hormone therapies. The model will comprehensively incorporate germline and liquid-biopsy mutation profiles, together with clinical variables and biomarkers, to make predictions. Achievement of the proposed study will have a huge impact on the management of this vulnerable patient population of mCRPC.