Seifeldin Awad

All supplementary tables and figures.

Purpose:Computational pathology has emerged as an attractive option for improving risk stratification in prostate cancer, 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 prostate cancer. Experimental Design:We identified two cohorts (discovery and validation) with M0 prostate cancer (n = 490) who were treated with radical prostatectomy, with hematoxylin and eosin–stained whole-slide images (WSI) and data on distant metastasis (DM). We identified a third cohort from The Cancer Genome Atlas with M0 prostate cancer (n = 326) with prostatectomy WSIs and bulk sequencing. Immune cells were identified from WSIs using a deep learning method (CellViT), and spatially dense immune clusters were quantified using density-based spatial clustering of applications with noise (DBSCAN). CIBERSORTx was utilized for immune cell deconvolution and TRUST4 for immune receptor repertoire reconstruction. Results:Median follow-up was 12.6 years (discovery) and 8.1 years (validation). Fourteen percent (n = 37, discovery) and seventeen percent (n = 38, validation) had Gleason 8 to 10 disease. Immune cell abundance was not associated with DM. In discovery, increased immune cluster was associated with decreased risk of DM for Gleason 8 to 10 (adjusted hazard ratio, 0.42; 95% confidence interval, 0.19–0.93) but not Gleason 6 to 7 (1.26, 0.77–2.05; 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 to 10 but not 6 to 7, high-cluster samples were enriched for CD8+ T cells, activated memory CD4+ T cells, regulatory T cells (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 prostate cancer.

Purpose: Computational pathology has emerged as an attractive option for improving risk stratification in prostate cancer, 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 prostate cancer. Experimental Design: We identified two cohorts (discovery and validation) with M0 prostate cancer (n = 490) who were treated with radical prostatectomy, with hematoxylin and eosin–stained whole-slide images (WSI) and data on distant metastasis (DM). We identified a third cohort from The Cancer Genome Atlas with M0 prostate cancer (n = 326) with prostatectomy WSIs and bulk sequencing. Immune cells were identified from WSIs using a deep learning method (CellViT), and spatially dense immune clusters were quantified using density-based spatial clustering of applications with noise (DBSCAN). CIBERSORTx was utilized for immune cell deconvolution and TRUST4 for immune receptor repertoire reconstruction. Results: Median follow-up was 12.6 years (discovery) and 8.1 years (validation). Fourteen percent (n = 37, discovery) and seventeen percent (n = 38, validation) had Gleason 8 to 10 disease. Immune cell abundance was not associated with DM. In discovery, increased immune cluster was associated with decreased risk of DM for Gleason 8 to 10 (adjusted hazard ratio, 0.42; 95% confidence interval, 0.19–0.93) but not Gleason 6 to 7 (1.26, 0.77–2.05; 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 to 10 but not 6 to 7, high-cluster samples were enriched for CD8+ T cells, activated memory CD4+ T cells, regulatory T cells (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 prostate cancer.