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.
Learn More About Their Work
Science Advances Magazine | Volume 7 | Issue 39 | 24 Sep 2021
Predicting and characterizing a cancer dependency map of tumors with deep learning
Tool: DeepDEP: deep learning of a cancer dependency map using cancer genomics
NIH/NCI R00 Award (PI: Chiu)
Deep learning of drug sensitivity and genetic dependency of pediatric cancer cells