Immune Based Prognostic Biomarkers for Multiple Anticancer Therapies in Lung Adenocarcinoma

Lung adenocarcinoma is the leading cause of cancer deaths in the United States. This subtype of lung cancer shows an average five-year survival rate of 15-17%, which is mainly due to late diagnosis and specific prognostic evaluation for therapy recommendation. There is an urgent need for developing reliable prognostic biomarkers to predict the success of the therapy and devise effective treatment strategies. In this study, we analyze gene expression data using RNA-seq from patients suffering from lung adenocarcinoma, who were subjected to one of four different anticancer therapies and demonstrated one of two-response phenotype post-therapy. We used gene set enrichment analysis to predict the outcome of multiple anticancer therapies based on host immune response. Results indicate the gene expression based study offers an avenue to short list putative-biomarkers for each anticancer therapy, which can be used for effective diagnostics and personalized treatment to improve therapeutic outcome in lung adenocarcinoma patients.