Comprehensive genomic analysis with immunological parameters to obtain the biomarkers for immune checkpoint inhibitors (ICIs) in non-small cell lung cancer (NSCLC).

Abstract

107 Background: Several kinds of predictors of ICIs efficacy in NSCLC have been analyzed. PD-L1 expression of tumor, frequency of tumor infiltrating lymphocytes (TIL) and microsatellite instability are cited as potential predictors of treatment efficacy of ICIs, but these still seem to be immature. Lately, mutation load in tumor, presumably involving to neo-antigens in tumor tissue, is also thought to be one of more probable predictors for ICIs. However, the hypothesis is still limited and it is very hard to analyze the mutation load routinely in clinic. We here describe more definitive and easily detecting factors by using Next generation sequencing (NGS) and by analyzing conventional immunological and molecular parameters. Methods: 94 patients with NSCLC who undergone resection in our institution were subjected for this study. Mutation load in tumor samples was analyzed by whole exome sequencing using NGS. The major gene alteration was also analyzed by NGS. TILs and PD-L1 expression in tumor were immunohistochemically assessed by using CD8 and SP142 (anti-PD-L1 antibody). Results: The median value of mutation load was 54 genes (10-363). The factors; squamous cell carcinoma, EGFR-mutation negative, and TP53 alteration positive indicated close connection with higher mutation load. PD-L1 expression, TILs, gender, age, and etc. were not associated with mutation load. Multiple regression analysis shows that EGFR-mutation negative and TP53 alteration positive contribute to higher mutation load (p = 0.018, p = 0.015). Conclusions: Mutation load in tumor could be associated with EGFR-mutation and TP53 alteration status. These profiles could potentially be more simple biomarkers in treating ICIs.