Immune Spatial Organization Predicts Distant Metastasis Risk in Aggressive Localized Prostate Cancer

PURPOSE: Computational pathology has emerged as an attractive option for improving risk stratification in prostate cancer (PCa), but most approaches either lack interpretability or focus solely on tumor morphology. We aimed to identify an interpretable, immune microenvironment-derived computational pathology biomarker for PCa. PATIENT AND METHODS: We identified two cohorts (Discovery and Validation) with M0 PCa (n=490) who were treated with radical prostatectomy with H Pint=0.020), with similar results in Validation (Gleason 8-10 0.60, 0.37-0.98; Gleason 6-7 1.19, 0.74-1.91; Pint=0.043). In Gleason 8-10 but not 6-7, high-cluster samples were enriched for CD8+ T cells, activated memory CD4+ T cells, Tregs (P≤0.037), and clonal T cell populations (P≤0.039). CONCLUSIONS: These findings propose immune spatial clustering as a novel, interpretable computational pathology biomarker and provide insight into the unique immune features of high-grade PCa.