Systemic inflammation biomarkers can identify high tumor mutation burden in lung adenocarcinoma.

backgroundTumor mutational burden (TMB) is a recognized biomarker for predicting immunotherapy efficacy in non-small cell lung cancer (NSCLC). Its assessment requires whole-exome sequencing (WES), but the high cost and stringent sample requirements of WES limit its clinical application. This study aims to assess the predictive value of accessible systemic inflammation markers for identifying high TMB lung cancer populations.

methodsWES was performed on tumor samples and paired peripheral blood from 72 lung adenocarcinoma patients. Genomic analysis identified mutation patterns across different TMB groups. Systemic inflammatory markers, including the neutrophil-to-lymphocyte ratio (NLR), derived neutrophil-to-lymphocyte ratio (dNLR), lymphocyte-to-monocyte ratio (LMR), and platelet to lymphocyte ratio (PLR), were collected. Generalized linear models and restricted cubic spline (RCS) plots were used to explore the predictive value of these markers for TMB. The Xgboost model assessed the importance of each variable for TMB prediction.

resultsAmong the 72 lung adenocarcinoma patients, missense mutations were the most common, with single nucleotide variants being the predominant mutation type. The most frequently mutated genes were EGFR (35%), TP53 (33%), and TTN (24%). Compared to the low TMB group, the high TMB group showed a higher proportion of C > A single nucleotide variants, along with significantly increased frequencies of TP53 (56% vs. 11%, p < 0.001) and TTN (42% vs. 6%, p < 0.001) mutations. Five de novo mutational signatures were extracted, each contributing differently across TMB strata. Multivariate generalized linear modeling indicated that higher TMB was significantly associated with elevated NLR (β = 0.272, 95% CI: 0.146-0.398), elevated PLR (β = 0.021, 95% CI: 0.012-0.030), and reduced LMR (β = -0.117, 95% CI: -0.212 to -0.028). Restricted cubic spline analyses further demonstrated non-linear associations between TMB and both NLR and PLR. The XGBoost model identified T stage, LMR and BMI as the most influential variables associated with TMB.

conclusionThis study reveals distinct mutational characteristics among different TMB groups in Chinese lung adenocarcinoma patients and demonstrates that systemic inflammatory markers can serve as preliminary indicators for identifying high TMB lung cancer populations.