Immunoscore Predicts Survival in Early-Stage Lung Adenocarcinoma Patients - Zhao, Zihuan; Zhao, Dan; Xia, Ji; Wang, Yi; Wang, Buhai (Frontiers in Oncology, 2020)
Background: The lung cancer staging system is insufficient for a comprehensive evaluation of patient prognosis. We constructed a novel immunoscore model to predict patients with high risk and poor survival. Method: Immunoscore was developed based on z-score transformed enrichment score of 11 immune-related gene sets of 109 immune risk genes. The immunoscore model was trained in lung adenocarcinoma cohort from The Cancer Genome Atlas (TCGA-LUAD) (n = 400), and validated in other two independent cohorts from Gene Expression Omnibus (GEO), GSE31210 (n = 219) and GSE68465 (n = 356). Meta-set (n = 975) was formed by combining all training and testing sets. Result: High immunoscore conferred worse prognosis in all sets. It was an independent prognostic factors in multivariate Cox analysis in training, testing and meta-set (hazard ratio (HR) = 2.96 (2.24 − 3.9), P < 0.001 in training set; HR = 1.99 (1,21 − 3.26), P = 0.006 in testing set 1; HR = 1.48 (1.69 − 2.39), P = 0.005 in testing set 2; HR = 2.01 (1.69 − 2.39), P < 0.001 in meta-set). Immunoscore-clinical prognostic signature (ICPS) was developed by integrating immunoscore and clinical characteristic, and had higher C-index than immunoscore or stage alone in all sets (0.72 (ICPS) vs 0.7 (immunoscore) or 0.59 (stage) in training set; 0.75 vs 0.72 or 0.7 in testing set 1; 0.65 vs 0.61 or 0.62 in testing set 2; 0.7 vs 0.66 or 0.64 in meta-set). Genome analysis revealed that immunoscore was positively correlated with tumor mutation burden (R = 0.22, P <0.001). Besides, high immunoscore was correlated with high proportion of carcinoma- associated fibroblasts (R = 0.32, P < 0.001) in tumor microenvironment but fewer CD8+ cells infiltration (R = -0.28, P < 0.001). Conclusion: The immunoscore and ICPS are potential biomarkers for evaluating patient survival. Further investigations are required to validate and improve their prediction accuracy.