Abstract 5093: A predictive biomarker classififier in NSCLC treated with immunotherapy

Abstract Background: Our Study have been focusing on investigating the immune components to increase immunotherapy efficacy and to assist patient stratification for immunotherapy NSCLC patients, we explored predict property of the mutations characteristics with immunotherapy survival benefit and Tumor Immune Microenvironment(TME)characteristics. Methods: A training cohort of 344 immunotherapy NSCLC were obtained from MSK database with Clinical and biological data. intersecting Candidate genes related to survival(p_value < 0.05)with immunity-related genes (IRGs), following an independent cohort of 75 patients with immunotherapy NSCLC was assessed. Applying the same patients RNA-Seq data calculated TME by CIBERSORT from TCGA database. Results: The result divided the patients into two groups: 54 samples in the MT group ( two or more of the 18 candidate genes mutated) and 290 samples in the WT group (less two mutations in the 18 genes).The TMB status frequency in WT_H/L and MT_H/L cohort were 11.9% vs 3.8% and 21.5% vs 62.8%(MT_H vs WT_H, P < 0.01 ; MT_L vs WT_L, P = 0.046 ). Discovery training cohort showed a high Overall Survial for patients with MT group which was 15.7% and 84.3% for WT group( P_value < 0.0001). The genes mutation signature degree of most immune cells (such as CD4\8+ T cells and M1 macrophages \natural killer ) were higher in the MT group. Conclusions: Our study offers genes mutation biomarker for OS prediction and potential addition development of tumor exemption, to be served as a convenient tool for identifying high relapse individuals with immunotherapy NSCLC patients. Citation Format: Shixin Zhang, Xintong Wang. A predictive biomarker classififier in NSCLC treated with immunotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5093.