Predicting Immunotherapy Outcomes in NSCLC Using RNA and Pathology from Multicenter Clinical Trials

作者：Zhaojun Wang, Yiran Fang, Xiatong Huang, Guichuang Ma, Qianqian Mao, Xiansheng Lu, Guangda Rong, Yunfang Yu, Yuanyuan Wang, Zhenhua Huang, Huiying Sun, Jiani Wu, Wenchao Gu, Na Huang, Jianhua Wu, Rui Zhou, Xiaoxiang Rong, Siting Zheng, Shao-wei Li, Gaofeng Wang, Lingling Wang, Wenjun Qiu, Luyang Jiang, Peng Luo, Yonggang Liu, Jianping Bin, Yulin Liao, Min Shi, Zuqiang Wu, Ji-Guang Wang, Wangjun Liao, Gang Chen, Dongqiang Zeng · 发表于：Advanced Science · 年份：2025 · DOI：10.1002/advs.202502037 · 被引用次数：1 · 研究领域：Cancer Immunotherapy and Biomarkers、Ferroptosis and cancer prognosis、Radiomics and Machine Learning in Medical Imaging

Immune checkpoint inhibitors (ICIs) are widely used to treat advanced non-small cell lung cancer (NSCLC). However, it remains crucial to identify patients who are unlikely to benefit from immunotherapy and to explore potential combination treatment strategies. In this study, 1127 advanced NSCLC patients from multicenter randomized clinical trials (OAK, POPLAR, ORIENT-11) and an in-house cohort who received ICIs, ICIs combined with chemotherapy, or chemotherapy alone are analyzed. Using bulk RNA-seq transcriptomic data, an RNA-based model, named the Lung Cancer Immunotherapy Response Assessment (LIRA), is developed, utilizing interaction analysis and a random forest algorithm to predict immunotherapy outcomes. LIRA outperforms PD-L1 expression and tumor mutation burden in predicting responses, particularly in identifying early progression risk during ICI monotherapy (HR: 0.15, 95% CI: 0.11-0.20). Tumor profile analysis reveals that LRP8 and HDAC4 are associated with immunotherapy outcomes. Additionally, scRNA-seq analysis of NSCLC tumors indicates a higher prevalence of T cells and a reduced proportion of epithelial cells in samples with a high LIRA-score. The deep learning model pinpointed critical high-attention regions within whole-slide images that contributed decisively to the LIRA predictions. In summary, these results demonstrate that LIRA enables independent risk stratification of NSCLC patients and provides insights into potential resistance mechanisms.