Jett Crowdis

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:PARP inhibition has demonstrated efficacy in patients with platinum-sensitive pancreatic cancer with germline BRCA1/2 pathogenic variants (PV). Whether PARP inhibitors might be effective in a broader population of patients with pancreatic cancer remains under investigation.Patients and Methods:This multicenter, open-label phase II trial (NCT03601923) enrolled patients with advanced pancreatic cancers who harbored germline or somatic BRCA1, BRCA2, PALB2, ATM, and/or CHEK2 PVs. Patients with prior progression on platinum-based therapy were excluded. Patients were treated with niraparib 200 or 300 mg once daily, with their initial dose determined by weight and platelet count. The primary endpoint was 6-month progression-free survival (PFS).Results:Thirty-two patients [10 women (31%); median age: 67 years] were enrolled. Patients had a PV in at least one of the following genes: ATM (n = 14), BRCA2 (n = 10), PALB2 (n = 3), CHEK2 (n = 4), or BRCA1 (n = 2). The 6-month PFS was 25% [90% confidence interval (CI), 13%–41%] for the overall population, exceeding the preestablished threshold of 17%. The median PFS for the entire population was 2 months (95% CI, 1.4–3.8), and the objective response rate was 14% (95% CI, 4%–33%). All six patients with ≥6 months of PFS and evaluable tumor zygosity had biallelic inactivation of a DNA repair gene. Three patients with biallelic inactivation of ATM and no progression on prior chemotherapy received niraparib for more than 1 year.Conclusions:Niraparib demonstrated clinically meaningful benefit in a subset of patients. The prolonged PFS observed in patients with biallelic ATM loss warrants further investigation.

Supplementary Figure S1: CONSORT diagram indicating patient disposition.

Supplementary Figure S6: Genomic characterization of BRCA2 reversion mutations

Supplementary Figure S2: Kaplan-Meier estimates of overall survival (OS) in patients treated with niraparib across the entire trial population

Supplementary Figure S3: Outcomes based on chemotherapy sensitivity

Representativeness of Study Participants

DNA Repair Gene Inclusion Variant

Supplementary Figure S5: Outcomes based on cell-free DNA tumor fraction