Abstract
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 clinical information. Our study demonstrates that genomic together with clinical features yielded a better reliable model predicting TMB-high status. A simplified model consisting of less than 20 genes and couples of clinical parameters were sought to be useful to provide TMB status with less cost and waiting time.
Highlights
Immune checkpoint inhibitors (ICIs) targeting programmed death 1 (PD-1) and programmed death ligand 1 (PD-L1) achieved great success improving clinical outcomes of patients with advanced non-small cell lung cancer (NSCLC)
499 FFPE tissues were collected from patients with clinically diagnosed NSCLC, including 470 lung adenocarcinomas (LUADs) and 29 lung squamous cell carcinomas (LUSCs)
Differentially mutated between Tumor mutation burden (TMB)-L and TMB-H patients (TMB-Low vs. TMBHigh) were EGFR (62 vs 42%, P < 0.001), EPHA3 (2 vs 13%, P < 0.001), FAT3 (4 vs 20%, P < 0.001), KEAP1 (1 vs 7%, P = 0.001), KMT2D (2 vs 10%, P < 0.001), LRRK2 (1 vs 10%, P < 0.001), NAV3 (1 vs 12%, P < 0.001), NF1 (2 vs 10%, P < 0.001), NFE2L2 (1 vs 11%, P < 0.001), PIK3CA (3.2 vs 12.1%, P < 0.001), PTPRD (1 vs 10%, P < 0.001), STK11 (1 vs 8%, P < 0.001), TP53 (20 vs 56%, P < 0.001), and TSHZ3 (1 vs 13%, P < 0.001) (Figure 2, Supplementary Table 1 and Supplementary Figure 3)
Summary
Immune checkpoint inhibitors (ICIs) targeting programmed death 1 (PD-1) and programmed death ligand 1 (PD-L1) achieved great success improving clinical outcomes of patients with advanced NSCLC. NSCLC tumors with elevated TMB and PD-L1 expression are associated with lympho-vascular invasion [11] It was reported in patients with advanced gastric cancer that clinicopathological (lymph node metastasis) and molecular characteristics (PIK3CA mutations) are associated with responders to nivolumab [12]. Targeted exome capture sequencing data of 499 NSCLC patients was analyzed in depth, and TMB was evaluated, which was defined as the total number of non-synonymous and indel somatic mutations present in a baseline tumor sample excluding known driver genes. In order to evaluate whether the histologic and genomic data could provide effective prediction of TMB, the receiver operating characteristic (ROC) curve analysis and area under the curve (AUC) were applied to evaluate the accuracy of TMB prediction model
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