A novel tumor mutational burden estimation model as a predictive and prognostic biomarker in NSCLC patients
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RESEARCH ARTICLE
Open Access
A novel tumor mutational burden estimation model as a predictive and prognostic biomarker in NSCLC patients Yanhua Tian1,2†, Jiachen Xu1†, Qian Chu3†, Jianchun Duan1, Jianjun Zhang2, Hua Bai1, Zhenlin Yang4, Wenfeng Fang5, Liangliang Cai6, Rui Wan1, Kailun Fei1, Jie He4, Shugeng Gao4, Li Zhang5*, Zhijie Wang1* and Jie Wang1*
Abstract Background: Tumor mutational burden (TMB) has both prognostic value in resected non-small cell lung cancer (NSCLC) patients and predictive value for immunotherapy response. However, TMB evaluation by whole-exome sequencing (WES) is expensive and time-consuming, hampering its application in clinical practice. In our study, we aimed to construct a mutational burden estimation model, with a small set of genes, that could precisely estimate WES-TMB and, at the same time, has prognostic and predictive value for NSCLC patients. Methods: TMB estimation model was trained based on genomic data from 1056 NSCLC samples from The Cancer Genome Atlas (TCGA). Validation was performed using three independent cohorts, including Rizvi cohort and our own Asian cohorts, including 89 early-stage and n late-stage Asian NSCLC patients, respectively. TCGA data were obtained on September 3, 2018. The two Asian cohort studies were performed from September 1, 2018, to March 5, 2019. Pearson’s correlation coefficient was used to assess the performance of estimated TMB with WES-TMB. The Kaplan-Meier survival analysis was applied to evaluate the association of estimated TMB with disease-free survival (DFS), overall survival (OS), and response to anti-programmed death-1 (PD-1) and anti-programmed death-ligand 1 (PD-L1) therapy. Results: The estimation model, consisted of only 23 genes, correlated well with WES-TMB both in the training set of TCGA cohort and validation set of Rizvi cohort and our own Asian cohort. Estimated TMB by the 23-gene panel was significantly associated with DFS and OS in patients with early-stage NSCLC and could serve as a predictive biomarker for anti-PD-1 and anti-PD-L1 treatment response. (Continued on next page)
* Correspondence: [email protected]; [email protected]; [email protected] † Yanhua Tian, Jiachen Xu and Qian Chu contributed equally to this work. 5 State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, 651# East Dong Feng Road, Guangzhou 510060, Guangdong, China 1 State Key Laboratory of Molecular Oncology, Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Pan-jia-yuan South Lane, Chaoyang District, Beijing 100021, China Full list of author information is available at the end of the article © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you giv
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