CircRNA signature predicts immunotherapy response in advanced non-small cell lung cancer.

Background: Immune checkpoint inhibitors (ICIs) offer significant benefits for advanced non-small cell lung cancer (NSCLC) but yield objective response rates of only 10%-30% in unselected patients. Circular RNAs (circRNAs), implicated in cancer RNA dysregulation, may serve as biomarkers for ICI response. Objectives: Identify circRNA signature to predict atezolizumab efficacy of NSCLC. Design: This study analyzed circRNA expression profiles from 891 advanced NSCLC patients in the OAK and POPLAR clinical studies. Methods: Based on The Cancer CircRNA Immunome Atlas database, we identified circRNAs associated with the efficacy of immunotherapy in NSCLC patients. Then, we establish predictive models for immunotherapy efficacy using multiple methods and conduct performance verification. Finally, we performed Gene Set Enrichment Analysis and Gene Set Variation Analysis to explore potential mechanisms. Results: We identified an 11-circRNA signature, named circRNA-Sig, which predicted atezolizumab efficacy with an area under the curve of 0.71 in OAK and 0.67 in POPLAR. Survival analysis in OAK showed patients with low circRNA-Sig scores benefited more from ICI than chemotherapy (hazard ratio (HR) = 1.347; 95% confidence interval (CI): 1.049-1.730; p = 0.019), whereas those with high scores showed no significant difference (HR = 1.020; 95% CI: 0.796-1.307; p = 0.876). Enrichment analysis revealed that low-scoring patients exhibit an activated tumor immune microenvironment, with upregulated pathways in interferon-γ and IL-2/STAT5, which can activate immune cells such as CD8 + T cells and natural killer cells, suggesting mechanistic links to ICI sensitivity. Conclusion: This circRNA-Sig model, validated across two large cohorts, offers a novel, clinically actionable tool for stratifying NSCLC patients for atezolizumab therapy, potentially enhancing personalized treatment strategies.