Differences in the genomic landscape of advanced prostate cancer (aPC) patients (pts) with BRCA1 versus BRCA2 mutations as detected by machine learning analysis of the comprehensive genomic profile (CGP) of cell-free DNA (cfDNA).

Abstract

162 Background: PARP inhibitors (PARPi) provide significant clinical benefit for men with aPC with BRCA 1 and BRCA 2 mutations. However, in clinical trials, pts with BRCA1 mutations appeared to derive less benefit than pts with BRCA2 (De Bono et al., 2020). Probabilistic Graphical Models (PGMs) are artificial intelligence (AI) algorithms that capture multivariate, multi-level dependencies in complex patterns in large datasets while retaining human interpretability. We hypothesize that PGMs can reveal variants in BRCA1 and 2 that co-segregate with other known pathogenic variants and may explain the difference in response to PARPi therapy. Methods: Multilevel gene interdependencies between BRCA1 or BRCA2 were assessed using a Bayesian Network (BN) machine learning approach and Fisher’s exact test. CGP was performed by a validated cfDNA NGS panel that sequenced 74 clinically relevant cancer genes (Guardant360, Redwood City, CA). Only variants of known significance and those of unknown significance with a pathogenic REVEL score were included in the analysis. Results: Of 4671 men with aPC undergoing cfDNA CGP, 1248 men with somatic mutations in BRCA1, BRCA2, ATM, or combinations of the three were included in the analysis. The Bayesian network analysis demonstrated positive interdependencies between pathogenic variants in BRCA1 and 7 other genes. A positive interdependency between BRCA2 and 2 genes was present (table). ATM displayed negative interdependency with both BRCA 1 and 2. Conclusions: Our results demonstrate a decreased association of BRCA2 versus BRCA1 with known or predicted pathogenic variants at other loci. This may explain increased sensitivity of aPC with BRCA2 mutations to PARPi due to fewer concurrent resistance pathways. For example, alteration of ERBB2, which segregates strongly with BRCA1, is known to induce tumor progression and invasion in aPC and is associated with castration-resistance. These hypothesis-generating data reveal differential genomic signatures associated with BRCA1 as compared to BRCA2 and may inform development of future combinatorial treatment regimens for these pts. [Table: see text]