Genomic Features and Response to Poly(ADP-ribose) Polymerase Inhibition in Metastatic Castration-Resistant Prostate Cancer

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PURPOSE: Poly(ADP-ribose) polymerase inhibitors (PARPis) are effective for the treatment of metastatic castration-resistant prostate cancer (mCRPC) harboring homologous recombination repair (HRR) gene alterations, but predictive biomarkers beyond individual gene alterations remain poorly defined. We aimed to identify genomic features associated with response to PARP inhibition in real-world data. MATERIALS AND METHODS: This is a single-center retrospective study of patients with mCRPC who received PARPi therapy. All patients underwent tumor genomic profiling using a single panel sequencing assay. Responses were defined as prostate-specific antigen (PSA50) (≥50% PSA decline from baseline) and objective radiographic response by RECIST 1.1. We explored the association of genomic features with response to therapy. RESULTS: Among 120 patients with mCRPC who received a PARPi, 54% had BRCA2 / 1 alterations and 16% had no HRR gene alterations. Other alterations include ATM (10%), CDK12 (10%), BARD1 (2%), CHEK2 (7%), and PALB2 (2%). Overall, 37% had PSA50 responses (95% CI, 27 to 47) and 43% had RECIST responses (95% CI, 27 to 61). Patients with BRCA2 alterations had the highest response rates (52% PSA50, 72% RECIST), and biallelic BRCA2 loss was associated with better outcomes. Panel-based mutational signatures (SigMA, SBS3 from DeepSig) were associated with BRCA -mutated status and overall response to PARPi. Six patients without known HRR gene alterations had responses; one was later found to harbor BRCA2 loss. Limitations include the small study size, retrospective design, and use of panel-based sequencing. CONCLUSION: Most PARPi responders had alterations in HRR genes, particularly BRCA2 , although some responders lacked known biomarkers associated with PARPi response. Our findings in this real-world data set support HRR gene testing for PARPi use in mCRPC and highlight the need for novel genome-wide biomarkers for patient selection.

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