Background: AA have substantially higher prostate cancer incidence rates, are diagnosed at a younger age and with a more advanced stage as compared to Ca. However, after adjusting for known prognostic factors, AA have an increased overall survival. We hypothesized that these differences might be due to the underlying changes in the genomic landscape which can be revealed by liquid biopsy. Methods: Real world comprehensive genomic profiling of ctDNA from aPCa patients from two institutions. The first ctDNA results as reported by Guardant 360 panel (Redwood City, CA) were included. Association between genetic mutation and gene were tested using Barnard’s test. To account for multiple testing, we used Benjamini-Hochberg’s False Discovery Rate adjustment across all tests to determine thresholds for false discovery rates. Same analysis was performed using a Bayesian Network Machine learning approach. Results: Overall, 361 patients with aPCa (81 AA and 280 Ca) were included in the analysis. Pathogenic genomic alterations were found in 87.0% of the cases, more frequently TP53 (42.4%), AR (34.1%), PIK3CA (13.9%), BRAF (12.7%), NF1 (10.8%) and MYC (10.0%). Targetable alterations of interest included DNA repair genes [BRCA 2 (7.8%), BRCA 1 (4.4%), ATM (6.4%), CDK12 (2.2%)], PIK3CA/mTOR/AKT (19.1%), PTEN (3.3%) and NTRK (1.9%). MSI-high was found in 4 patients. AA as compared to Ca had a significantly higher prevalence of CDK12 (20.7% vs. 3.8%, p=0.016) and GNA11 mutations (3.7% vs. 0.4%, p=0.0225). BayesNet analysis also supported these results (table). Conclusions: In this dataset, liquid biopsy of ctDNA was useful for genetic characterization of aPCa and reveal differences in the molecular phenotype of AA and Ca in aPCa with potential clinical implications. These findings support ongoing research on the clinical utility of non-invasive genotyping and therapeutic response monitoring with a focus on AA population.

AA – African American; Ca – Caucasians; BH-FDR - Benjamini-Hochberg’s False Discovery Rate; tests and percentages account for panel differences in the number of genes tested.