Tumor mutational burden (TMB) is an emerging predictive marker of response to immune checkpoint inhibitor therapies. We evaluated the correlation between clinical indicators and high-throughput sequencing results and TMB in lung adenocarcinoma patients, with the aim of finding simpler and more economical factors as surrogate markers for TMB. The medical records, next-generation sequencing data, and immunohistochemistry results of 340 lung adenocarcinoma patients who were admitted to the First Affiliated Hospital of Zhengzhou University between 2019 and 2020 were collected. The mutated genes were screened for, and the obtained mutated genes were subjected to functional enrichment analysis using R software. A protein-protein interaction (PPI) network was also constructed, and significant modules in the network were identified. Gene Ontology (GO) analyses were performed for the core genes. Univariate and multivariate correlation analyses were performed to judge the correlation between gene mutations and TMB. Genes with a junction mutation rate >1 were selected to construct PPI network and 13 high-connection core genes were screened. The results of GO enrichment analysis showed that the biological processes related to mutant core genes mainly included mitotic cell cycle and cell aging. Subsequently, ATM (p = 0.006) and PIK3CA (p = 0.008) mutation positivity were identified by univariate and multivariate correlation analysis, while TP53 (p = 0.003) and EGFR (p = 0.008) mutation negativity were significantly associated with elevated TMB. The results of this study demonstrate that ATM- and PIK3CA-positive and EGFR-negative mutation status are strongly associated with high levels of TMB and have the potential to be predictive biomarkers of response to immune checkpoint inhibitors in lung adenocarcinoma patients.
Keywords: immune checkpoint inhibitors; lung adenocarcinoma; tumor mutational burden (TMB).
© 2022 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.