我国学者开发出环状RNA模型，预测肺癌患者的免疫治疗响应

Core Insights - Lung cancer is the most common malignant tumor globally and the leading cause of cancer-related deaths, with non-small cell lung cancer (NSCLC) accounting for over 85% of cases. Despite advancements in clinical management, the 5-year overall survival rate for NSCLC patients has only increased from 15% to 25% [2] - Immune checkpoint inhibitors (ICIs), such as PD-1 and PD-L1 inhibitors, have transformed the treatment landscape for NSCLC. However, the objective response rate (ORR) for unselected NSCLC patients receiving ICI treatment is only 10%-30%, with some patients experiencing accelerated disease progression or early death [2] - A new study identified a circRNA signature (circRNA-Sig) consisting of 11 circRNAs that can predict the response to immunotherapy in advanced NSCLC, potentially guiding clinical treatment [3][8] Summary by Sections CircRNA and Cancer - CircRNA is associated with dysregulated RNA expression in cancer and has potential as a biomarker for predicting responses to ICIs [3] Research Findings - The research team analyzed circRNA expression profiles from 891 advanced NSCLC patients in the OAK and POPLAR clinical trials, identifying significantly differentially expressed circRNAs [4] - A predictive model was constructed using machine learning, which was validated and revealed key circRNAs that may influence the efficacy of NSCLC immunotherapy [4] CircRNA-Sig Model - The circRNA-Sig model demonstrated an area under the curve (AUC) of 0.71 in the OAK trial and 0.67 in the POPLAR trial for predicting the efficacy of atezolizumab [5] - Survival analysis indicated that patients with low circRNA-Sig scores benefited significantly more from ICI treatment compared to chemotherapy (HR=1.347), while high-score patients showed no significant difference [5] - Enrichment analysis suggested that low-score patients exhibited an activated tumor immune microenvironment, indicating a mechanistic link between circRNA and ICI treatment sensitivity [5] Clinical Application - The circRNA-Sig model, validated across two large clinical trial cohorts, offers a new stratification tool for NSCLC patients undergoing atezolizumab treatment, enhancing personalized treatment strategies [8]