Schmidt, S.; Jarai, Z.; Larkeryd, A.; Ozer, B.; Karlsson, Q.; Campbell, J.; Merson, S.; Dennis, N.; Hazell, S.; Mitsopoulos, C.; Cooper, C.; ICGC-UK, T.; Consortium, T.; Russell, K.; Workman, P.; Eeles, R.; Al-Lazikani, B.

Despite advances in understanding and treating Prostate Cancer (PCa), there has been little effort to systematically map the biology distinguishing Early- (EOPCa) and Late- (LOPCa) onset PCa. Around 25% of EOPCa cases present with metastatic spread or aggressive disease with earlier metastatic development. Some available lines of therapy are extending treatment trajectories and prolonging lives. However, there remains a critical clinical need to identify new therapeutic targets for EOPCa where life expectancy necessitates safer, more targeted treatment options. To our knowledge, here we present the largest systematic analysis of molecular profiles in EOPCa versus LOPCa, employing machine-learning-enabled algorithms to identify distinguishing biology and druggable targets for each age group. Distinct stromal signatures are uncovered in EOPCa, which are used to propose therapeutic opportunities herein. Moreover, our analysis identifies 50 druggable targets, 11 of which we confirm in PCa cell line genetic/pharmacological perturbation data. These findings provide the first specific, testable hypotheses in EOPCa, offering avenues for experimental validation and potential therapeutic exploitation, and, more generally, shed light on the intricate and distinguished molecular profile of this aggressive, poorly understood disease.