Integrating Histologic and Genomic Characteristics to Predict Tumor Mutation Burden of Early-Stage Non-Small-Cell Lung Cancer

作者：Yuan Qiu, Liping Liu, Haihong Yang, Hanzhang Chen, Qiuhua Deng, Dakai Xiao, Yongping Lin, Changbin Zhu, Weiwei Li, Ди Шао, Wenxi Jiang, Kui Wu, Jianxing He · 发表于：Frontiers in Oncology · 年份：2021 · DOI：10.3389/fonc.2020.608989 · 被引用次数：35 · 研究领域：Lung Cancer Treatments and Mutations、Cancer Genomics and Diagnostics、Cancer Immunotherapy and Biomarkers

Tumor mutation burden (TMB) serves as an effective biomarker predicting efficacy of mono-immunotherapy for non-small cell lung cancer (NSCLC). Establishing a precise TMB predicting model is essential to select which populations are likely to respond to immunotherapy or prognosis and to maximize the benefits of treatment. In this study, available Formalin-fixed paraffin embedded tumor tissues were collected from 499 patients with NSCLC. Targeted sequencing of 636 cancer related genes was performed, and TMB was calculated. Distribution of TMB was significantly (p < 0.001) correlated with sex, clinical features (pathological/histological subtype, pathological stage, lymph node metastasis, and lympho-vascular invasion). It was also significantly (p < 0.001) associated with mutations in genes like TP53 , EGFR , PIK3CA , KRAS , EPHA3 , TSHZ3 , FAT3 , NAV3 , KEAP1 , NFE2L2 , PTPRD , LRRK2 , STK11 , NF1 , KMT2D , and GRIN2A . No significant correlations were found between TMB and age, neuro-invasion (p = 0.125), and tumor location (p = 0.696). Patients with KRAS p.G12 mutations and FAT3 missense mutations were associated (p < 0.001) with TMB. TP53 mutations also influence TMB distribution (P < 0.001). TMB was reversely related to EGFR mutations (P < 0.001) but did not differ by mutation types. According to multivariate logistic regression model, genomic parameters could effectively construct model predicting TMB, which may be improved by introducing...