Background: Black men experience higher incidence and mortality rates from prostate cancer, yet the molecular drivers of outcome disparities are not clearly defined. We performed a multi-omics analysis of localized PCa in the equal access Military Health System to identify ancestry-associated biological differences and prognostic markers.
Objectives: This study aims to address prostate cancer outcome disparities by defining ancestry-associated and shared molecular drivers in an equal-access Military Health System cohort using integrative proteogenomic analysis. By jointly analyzing genomic, proteomic, and phosphoproteomic data, we seek to identify biologically distinct molecular subtypes and prognostic features of localized prostate cancer. Finally, we aim to uncover early predictive signatures of aggressive disease and identify candidate therapeutic vulnerabilities through integrative multi-omic analyses.
Methods: We analyzed tumors from 112 patients (57 Black, 55 White) using whole genome sequencing, quantitative proteomics, and phosphoproteomics. To identify ancestry-associated differences, we performed comparative analysis using over-representation analysis for gene set enrichment and Multi-Omics Gene-Set Analysis (MOGSA) to determine the contribution of multi-omics features to these enrichments. Integrative clustering using iClusterBayes was applied to define molecular subtypes based on shared variation across multi-omics data. Germline regulation of the proteome was assessed via eQTL analyses. Prognostic significance was evaluated with Cox proportional hazards models (adjusted for age, PSA, and percent African ancestry); and ancestry-specific biomarker panels were validated using Kaplan–Meier and AUC analyses.
Results: Black patients displayed higher genomic variability, stronger androgen response, fatty-acid metabolism, and EMT. White patients were enriched for DNA repair gene deletions, MYC/E2F signaling, mTORC1 activity, and cell-cycle progression. Phosphoproteomics revealed ancestry-specific kinase dependencies, with CK2α and CHEK2 activity elevated in Black patients, and CDK1/2, PRKD1, and CAMK2D/B activity in White patients. eQTL analyses highlighted germline regulation of the proteome independent of CNAs. MOGSA revealed 56 pathways grouped into five ancestry-associated subtypes, contrasting White-enriched apoptosis/epigenetic pathways with Black-enriched structural and DNA repair responses. Multiomics integration with iClusterBayes defined three reproducible molecular subtypes, externally validated, providing a basis  for risk stratification with clinical potential.  Somatic and germline regulatory differences converged on steroid hormone signaling, metabolic reprogramming, PI3K/AKT/mTOR, and DNA damage response pathways, with ancestry-associated immune and stromal signals. Prognostic analyses identified ancestry-specific and shared alterations associated with PCa progression. Prognostic CNA and protein panels exceeded PSA and pathology models (AUC >0.8), with ancestry-specific and universal biomarkers validated across cohorts.
Conclusions/Implications for practice or policy: This integrative proteogenomics study of localized PCa defines ancestry-associated pathways, identifies reproducible molecular subtypes, and highlights germline regulatory influences on the tumor proteome. Ancestry-aware CNA, protein, and phosphoproteomic biomarker panels improve risk prediction and highlight potential therapeutic vulnerabilities, including kinase and metabolic dependencies. These findings provide a framework for refining ancestry-informed risk stratification and testing hypotheses of precision treatments to reduce outcome disparities.

APOLLO3 Study Design