Chromatin accessibility profiles of castration-resistant prostate cancers reveal novel subtypes and therapeutic vulnerabilities

Abstract

In castration-resistant prostate cancer (CRPC), the loss of androgen receptor (AR)-dependence due to lineage plasticity, which has become more prevalent, leads to clinically highly aggressive tumors with few therapeutic options and is mechanistically poorly defined. To identify the master transcription factors (TFs) of CRPC in a subtype-specific manner, we derived and collected 29 metastatic human prostate cancer organoids and cell lines, and generated ATAC-seq, RNA-seq and DNA sequencing data. We identified four subtypes and their master TFs using novel computational algorithms: AR-dependent; Wnt-dependent, driven by TCF; neuroendocrine, driven by ASCL1 and NEUROD1 and stem cell-like (SCL), driven by the AP-1 family. The transcriptomic signatures of these four subtypes enabled the classification of 370 patients. We find that AP-1 co-operates with the inhibitable YAP/TAZ/TEAD pathway in the SCL subtype, the second most common group of CRPC tumors after AR-dependent. Together, this molecular classification reveals new drug targets and can potentially guide therapeutic decisions.

Recapitulation of patient-specific 3D chromatin conformation using machine learning

“…To test our hypothesis that open chromatin regions with predicted target genes by DGTAC are active regulatory elements, we applied DGTAC on eleven cell lines for which we either generated ATAC-seq and RNA-seq data or used previously published datasets ( Figures S2 B–S2D; details for the dataset are listed in Table S2 ). 8 , 32 , 33 We observe that our predicted active enhancers (open peaks with target genes predicted) show a significantly higher H3K27ac signal compared with open peaks without target gene predicted ( Figure 2 A). This trend persists when comparing to active enhancers predicted by correlation-based methods ( Figure S2 E).…”