CircRNA signature predicts immunotherapy response in advanced non-small cell lung cancer
Xin Li1,2, Shixiang Wang3, Yanru Cui4
1Department of Oncology, The Second Affiliated Hospital of Zunyi Medical University, Zunyi, P.R. China.
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.
Insights
A novel 11-circRNA signature (circRNA-Sig) effectively predicts atezolizumab efficacy in advanced non-small cell lung cancer (NSCLC). Low circRNA-Sig scores indicate a more responsive immune microenvironment, guiding personalized immunotherapy strategies.
Area of Science:
- Oncology
- Molecular Biology
- Immunotherapy
Background:
- Immune checkpoint inhibitors (ICIs) show limited response rates (10-30%) in advanced non-small cell lung cancer (NSCLC).
- Circular RNAs (circRNAs) are dysregulated in cancer and may predict ICI response.
Purpose of the Study:
- To identify a circRNA signature for predicting atezolizumab efficacy in NSCLC patients.
- To explore the underlying mechanisms of circRNA-mediated ICI response.
Main Methods:
- Analysis of circRNA expression profiles from 891 advanced NSCLC patients in the OAK and POPLAR trials.
- Development and validation of predictive models using an 11-circRNA signature (circRNA-Sig).
- Gene Set Enrichment Analysis and Gene Set Variation Analysis to investigate potential mechanisms.
Main Results:
- The circRNA-Sig model predicted atezolizumab efficacy with AUCs of 0.71 (OAK) and 0.67 (POPLAR).
- Low circRNA-Sig scores correlated with improved benefit from ICI over chemotherapy in NSCLC (HR=1.347, p=0.019).
- Low-scoring patients exhibited an activated tumor immune microenvironment with upregulated interferon-γ and IL-2/STAT5 pathways.
Conclusions:
- The validated circRNA-Sig offers a novel tool for stratifying NSCLC patients for atezolizumab therapy.
- This signature can enhance personalized treatment strategies by identifying patients likely to respond to immunotherapy.
- The findings suggest mechanistic links between circRNA expression and ICI sensitivity via immune microenvironment modulation.
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