Abstract
Even with the great advances in immunotherapy in recent years, the response rate to immune checkpoint inhibitor therapy for non-small cell lung cancer is only about 20%. We aimed to identify new features that would better predict which patients can benefit from an immune checkpoint blocker. This study is based on the publicly available gene expression data from The Cancer Genome Atlas lung cancer samples and the newly released mutation annotation data. We performed a comprehensive analysis by correlating patient cytolytic activity index, mutational signatures, and other immune characteristics in four stratified patient groups. The results cytolytic activity index are highly correlated with immune infiltration scores, T cell infiltration scores and TCR clonality scores in lung cancer. In addition, we observed that the mutational event signatures might play a more important role in predicting immunotherapy response in squamous cell carcinoma and two subgroups of adenocarcinomas. Our analysis illustrates the utility of integrating both tumor immune and genomic landscape for a better understanding of immune response in lung cancer.