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Assessment of tumor mutation burden calculation from gene panel sequencing data

Authors Xu Z, Dai J, Wang D, Lu H, Dai H, Ye H, Gu J, Chen S, Huang B

Received 30 November 2018

Accepted for publication 13 March 2019

Published 6 May 2019 Volume 2019:12 Pages 3401—3409


Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 2

Editor who approved publication: Dr Sanjeev Srivastava

Zhenwu Xu,1 Jiawei Dai,2 Dandan Wang,3 Hui Lu,2 Heng Dai,3,4 Hao Ye,3,4 Jianlei Gu,2 Shengjia Chen,1 Bingding Huang3,4

1Department of Thoracic Medical Oncology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, Fujian, People’s Republic of China; 2SJTU-Yale Joint Center for Biostatistics, Department of Bioinformatics and Biostatistics, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, People’s Republic of China; 3Research and Development, Sinotech Genomics Inc, Shanghai 21000, People’s Republic of China; 4Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Science, Shenzhen, Guangdong, People’s Republic of China

Background: High tumor mutation burden (TMB) is an emerging selection biomarker for immune checkpoint blockade in tumors such as melanoma and non-small cell lung cancer. TMB is typically calculated from whole genome sequencing or whole exome sequencing (WES) data. Recently, clinical trials showed that TMB can also be estimated from targeted sequencing of a panel of only a few hundred genes of interest, which can be performed at a high depth for clinical applications. 
Materials and methods: In this study, we systematically investigated the distribution of TMB and preferences at the gene and mutation level, as well as the correlation between TMB calculated by WES and panel sequencing data using somatic mutation data from 15 cancer types from The Cancer Genome Atlas (TCGA). 
Results: We proposed a pan-cancer TMB panel and demonstrated that it had a higher correlation with WES than other panels. Our panel could serve as a reference data-set for TMB-oriented panel design to identify patients for immunotherapy.

Keywords: PD-1/PD-L1 blockade, Gene panel, hotspot mutation

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