Diversity in Androgen Receptor Action Among Treatment-naïve Prostate Cancers Is Reflected in Treatment Response Predictions and Molecular Subtypes

• Main Authors: Salma Ben-Salem, Qiang Hu, Yang Liu, Mohammed Alshalalfa, Xin Zhao, Irene Wang, Varadha Balaji Venkadakrishnan, Dhirodatta Senapati, Sangeeta Kumari, Deli Liu, Andrea Sboner, Christopher E. Barbieri, Felix Feng, Jean-Noel Billaud, Elai Davicioni, Song Liu, Hannelore V. Heemers
• Format: Article
• Language: English
• Published: Elsevier 2020-12-01
• Series: European Urology Open Science
• Subjects: Disease stratification; Treatment response; Hormonal therapy; Chemotherapy; Radiotherapy; Biomarker
• Online Access: http://www.sciencedirect.com/science/article/pii/S266616832035833X

Background: Metastatic prostate cancer (CaP) treatments are evolving rapidly but without evidence-based biomarkers to predict responses, and to maximize remissions and survival. Objective: To determine the activity of androgen receptor (AR), the target for default first-line systemic treatment, in localized treatment-naïve CaP and its association with clinical risk factors, molecular markers, CaP subtypes, and predictors of treatment response. Design, setting, and participants: We examined 452 bona fide AR target genes in clinical-grade expression profiles from 6532 such CaPs collected between 2013 and 2017 by US physicians ordering the Decipher RP test. Results were validated in three independent smaller cohorts (n = 73, 90, and 127) and clinical CaP AR ChIP-Seq data. Association with CaP differentiation and progression was analyzed in independent datasets. Outcome measurements and statistical analysis: Unsupervised clustering of CaPs based on AR target gene expression was aligned with clinical variables, differentiation scores, molecular subtypes, and predictors of response to hormonal therapy, radiotherapy, and chemotherapy. AR target gene sets were analyzed via Gene Set Enrichment Analysis for differentiation and treatment resistance, Ingenuity Pathway Analysis for associated biology, and Cistrome for genomic AR binding site (ARBS) composition. Results and limitations: Expression of eight AR target gene subsignatures gave rise to five CaP clusters, which were preferentially associated with CaP molecular subtypes, differentiation, and predictors of treatment response rather than with clinical variables. Subsignatures differed in contribution to CaP progression, luminal/basal differentiation, CaP biology, and ARBS composition. Validation in prospective trials and optimized quantitation are needed for clinical implementation. Conclusions: Measurement of AR activity patterns in treatment-naïve CaP may serve as a first branch of an evidence-based decision tree to optimize personalized treatment plans. Patient summary: Treatment options for metastatic prostate cancer are increasing without information needed to choose the right treatment for the right patient. We found variation in the behavior of the target for the default first-line therapy before treatment, which may help optimize treatment plans.