BackgroundImmunological checkpoint blockade is effective in treating various malignancies. Identifying predictive biomarkers to assist patient selection for immunotherapy has become a priority in both clinical and research settings.MethodsMutations in patients who responded to immunotherapy were identified through next-generation sequencing. Relationships among protein kinase, DNA-activated, catalytic polypeptide (PRKDC) mutations, mutation load and microsatellite instability (MSI) were analyzed using datasets from The Cancer Genome Atlas. These relationships were validated by conducting an in vitro study and by using tissue samples from 34 patients with gastric cancer. The CT26 animal model was used to evaluate the role of PRKDC as a predictive biomarker and the efficacy of the DNA-PK inhibitor.ResultsFrom the published literature, we found that among patients whose tumors harboredPRKDCmutations, 75%, 53.8%, and 50% of those with lung cancer, melanoma, and renal cell carcinoma, respectively, responded to immunotherapy. Most of these mutations were truncating and located in functional domains or in a destabilizing PRKDC protein structure. Additional analysis showed that aPRKDCmutation was significantly associated with a high mutation load in cervical cancer, colon adenocarcinoma, head and neck squamous cell carcinoma, lung adenocarcinoma, gastric adenocarcinoma and endometrial cancer. Patients with gastric cancer or colon cancer harboringPRKDCmutations were also highly associated with MSI-high status. Finally, we found that knockout PRKDC or DNA-PK inhibitor (PRKDCencodes the catalytic subunit of DNA-dependent protein kinase) enhanced the efficacy of the anti-programmed cell death protein one pathway monoclonal antibody in the CT26 animal model.ConclusionsPRKDC is not only a predictive biomarker but also a drug target for immune checkpoint inhibitors.