Tumour mutational burden is overestimated by target cancer gene panels

作     者：Hu Fang Johanna Bertl Xiaoqiang Zhu Tai Chung Lam Song Wu David J.H.Shih Jason W.H.Wong 

作者机构：School of Biomedical SciencesLi Ka Shing Faculty of MedicineThe University of Hong KongHong KongChina South China HospitalHealth Science CenterShenzhen UniversityShenzhenChina Department of MathematicsAarhus UniversityAarhusDenmark Department of Clinical OncologyLi Ka Shing Faculty of MedicineThe University of Hong KongHong KongChina 

出 版 物：《Journal of the National Cancer Center》 (癌症科学进展（英文）)

年 卷 期：2023年第3卷第1期

页      面：56-64页

学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

基　　金：supported by the Research Grants Council,HK(grant number:17100920) seed funding from The University of Hong Kong 

主　　题：Tumour mutational burden Immune checkpoint inhibitor Immune response Biomarker 

摘      要：Background:Tumour mutational burden(TMB)has emerged as a predictive marker for responsiveness to immune checkpoint inhibitors(ICI)in multiple tumour *** can be calculated from somatic mutations detected from whole exome or targeted panel sequencing *** mutations are unevenly distributed across the cancer genome,the clinical implications from TMB calculated using different genomic regions are not ***:Pan-cancer data of 10,179 samples were collected from The Cancer Genome Atlas cohort and 6,831 cancer patients with either ICI or non-ICI treatment outcomes were derived from published *** was calculated as the count of non-synonymous mutations and normalised by the size of genomic *** method,linear regression and Poisson calibration models are used to unify TMB from different gene ***:We found that panels based on cancer genes usually overestimate TMB compared to whole exome,potentially leading to misclassification of patients to receive *** overestimation is caused by positive selection for mutations in cancer genes and cannot be completely addressed by the removal of mutational *** compared different approaches to address this discrepancy and developed a generalised statistical model capable of interconverting TMB derived from whole exome and different panel sequencing data,enabling TMB correction for patient stratification for ICI *** show that in a cohort of lung cancer patients treated with ICI,when using a TMB cutoffof 10 mut/Mb,our corrected TMB outperforms the original panel-based ***:Cancer gene-based panels usually overestimate TMB,and these findings will be valuable for unifying TMB calculations across cancer gene panels in clinical practice.