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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\u201011) and an in\u2010house cohort who received ICIs, ICIs combined with chemotherapy, or chemotherapy alone are analyzed. Using bulk RNA\u2010seq transcriptomic data, an RNA\u2010based 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\u2010L1 expression and tumor mutation burden in predicting responses, particularly in identifying early progression risk during ICI monotherapy (HR: 0.15, 95% CI: 0.11\u20130.20). Tumor profile analysis reveals that LRP8 and HDAC4 are associated with immunotherapy outcomes. Additionally, scRNA\u2010seq analysis of NSCLC tumors indicates a higher prevalence of T cells and a reduced proportion of epithelial cells in samples with a high LIRA\u2010score. The deep learning model pinpointed critical high\u2010attention regions within whole\u2010slide 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.<\/jats:p>","DOI":"10.1002\/advs.202502037","type":"journal-article","created":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T18:29:38Z","timestamp":1761762578000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Predicting Immunotherapy Outcomes in NSCLC Using RNA and Pathology from Multicenter Clinical Trials"],"prefix":"10.1002","volume":"13","author":[{"given":"Zhaojun","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Oncology Nanfang Hospital Southern Medical University Guangzhou Guangdong 510515 P. R. 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