Purpose

Although numerous biology-driven subtypes have been described previously in metastatic castration-resistant prostate cancer (mCRPC), unsupervised molecular subtyping based on gene expression has been less studied, especially using large cohorts. Thus, we sought to identify the intrinsic molecular subtypes of mCRPC and assess molecular and clinical correlates in the largest combined cohort of mCRPC samples with gene expression data available to date.

Experimental design

We combined and batch-effect corrected gene expression data from four mCRPC cohorts from the Fred Hutchinson Cancer Research Center (N = 157), a small-cell neuroendocrine (NE) prostate cancer (SCNC)-enriched cohort from Weill Cornell Medicine (N = 49), and cohorts from the Stand Up 2 Cancer/Prostate Cancer Foundation East Coast Dream Team (N = 266) and the West Coast Dream Team (N = 162).

Results

Hierarchical clustering of RNA-sequencing data from these 634 mCRPC samples identified two distinct adenocarcinoma subtypes, one of which (adeno-immune) was characterized by higher gene expression of immune pathways, higher CIBERSORTx immune scores, diminished ASI benefit, and non-lymph node metastasis tropism compared with an adeno-classic subtype. We also identified two distinct subtypes with enrichment for an NE phenotype, including an NE-liver subgroup characterized by liver metastasis tropism, PTEN loss, and APC and SPOP mutations compared with an NE-classic subgroup.

Conclusions

Our results emphasize the heterogeneity of mCRPC beyond currently accepted molecular phenotypes, and suggest that future studies should consider incorporating transcriptome-wide profiling to better understand how these differences impact treatment responses and outcomes.

We are now in an era of molecular medicine, where specific DNA alterations can be used to identify patients who will respond to specific drugs. However, there are only a handful of clinically used predictive biomarkers in oncology. Herein, we describe an approach utilizing in vitro DNA and RNA sequencing and drug response data to create TreAtment Response Generalized Elastic-neT Signatures (TARGETS). We trained TARGETS drug response models using Elastic-Net regression in the publicly available Genomics of Drug Sensitivity in Cancer (GDSC) database. Models were then validated on additional in-vitro data from the Cancer Cell Line Encyclopedia (CCLE), and on clinical samples from The Cancer Genome Atlas (TCGA) and Stand Up to Cancer/Prostate Cancer Foundation West Coast Prostate Cancer Dream Team (WCDT). First, we demonstrated that all TARGETS models successfully predicted treatment response in the separate in-vitro CCLE treatment response dataset. Next, we evaluated all FDA-approved biomarker-based cancer drug indications in TCGA and demonstrated that TARGETS predictions were concordant with established clinical indications. Finally, we performed independent clinical validation in the WCDT and found that the TARGETS AR signaling inhibitors (ARSI) signature successfully predicted clinical treatment response in metastatic castration-resistant prostate cancer with a statistically significant interaction between the TARGETS score and PSA response (p = 0.0252). TARGETS represents a pan-cancer, platform-independent approach to predict response to oncologic therapies and could be used as a tool to better select patients for existing therapies as well as identify new indications for testing in prospective clinical trials.