Clinicogenomic Predictors of First-line Immune Checkpoint Inhibitor Outcomes in Non–Small Cell Lung Cancer: A Nationwide C CAT Cohort From Japan

Background The efficacy of immune checkpoint inhibitors (ICIs) in non-small cell lung cancer (NSCLC) varies, requiring predictive biomarkers. Methods We analyzed the nationwide Center for Cancer Genomics and Advanced Therapeutics (C-CAT) database and identified 1,629 patients with stage IV NSCLC who received first-line ICI-based therapy between 2019 and 2025. The primary endpoint was time to treatment failure (TTF); the secondary endpoint was overall survival (OS). Age-stratified Cox models were adjusted for covariates. Genomic covariates included KRAS, KEAP1 , and STK11 , with genes from exploratory screening. We tested KRAS – KEAP1 and KRAS – STK11 interactions. Sensitivity analyses included an FFPE-only subset with tumor mutational burden (TMB) and models adjusting for the ECOG status. Results BRAF mutation was independently associated with longer TTF (HR 0.77, 95% CI 0.59–1.00; p = 0.049), whereas KRAS, KEAP1 , and STK11 alone were not. A significant KRAS – KEAP1 interaction was associated with shorter TTF (HR 1.89, 95% CI 1.06–3.36; p = 0.032). In a four-level comparison versus wild-type, KRAS -only and KEAP1 -only were not significant, whereas KRAS – KEAP1 co-mutation was associated with higher risk of failure (HR 2.48, 95% CI 1.50–4.10; p < 0.001). Results were consistent across sensitivity analyses. KEAP1 and STK11 were associated with worse OS, while BRAF showed no OS advantage. Conclusions In first-line ICI–treated stage IV NSCLC, KRAS – KEAP1 co-mutation, not KRAS or KEAP1 alone, identifies patients at high risk of early failure, whereas BRAF was associated with longer TTF. These findings highlight the importance of co-mutation profiling and warrant prospective validation with integrative models incorporating PD-L1 and TMB. Micro-abstract Using nationwide genomic and clinical data from 1,629 patients with stage IV non-small cell lung cancer treated with first-line immune checkpoint inhibitor–based therapy, we found that KRAS–KEAP1 co-mutation, but not either gene alone, predicted early treatment failure. BRAF mutation was associated with longer time to treatment failure. These findings underscore the value of co-mutation profiling in optimizing immunotherapy strategies.