Mitsopoulos, C. | Search bioRxiv

bioRxiv2025

Distinct druggable biological processes in early-onset prostate cancer

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. One Sentence SummaryMachine learning-enabled algorithms were utilized to identify distinguishing biology and associated druggable targets for early-onset prostate cancers.

cancer biology↗

bioRxiv2024

Probabilistic graph-based model uncovers previously unseen druggable vulnerabilities in major solid cancers

Over half cancer patients lack safe, effective, targeted therapies despite abundant molecular profiling data. Statistically recurrent cancer drivers have provided fertile ground for drug discovery where they exist. But in rare, complex, and heterogeneous cancers, strong driver signals are elusive. Moreover, therapeutically exploitable molecular vulnerabilities extend beyond classical drivers. Here we describe a novel, integrative, generalizable graph-based, cooperativity-led Markov chain model, A3D3as MVP (Adaptive AI-Augmented Drug Discovery and Development Molecular Vulnerability Picker), to identify and prioritize key druggable molecular vulnerabilities in cancer. The algorithm exploits cooperativity of weak signals within a cancer molecular network to enhance the signal of true molecular vulnerabilities. We apply A3D3as MVP to 19 solid cancer types and demonstrate that it outperforms standard approaches for target hypothesis generation by >3-fold as benchmarked against cell line genetic perturbation and drug screening data. Importantly, we demonstrate its ability to identify non-driver druggable vulnerabilities and highlight 43 novel or emergent druggable targets for these tumors.

cancer biology↗