Background:Tumour mutational burden(TMB)has emerged as a predictive marker for responsiveness to immune checkpoint inhibitors(ICI)in multiple tumour types.It can be calculated from somatic mutations detected from whol...Background:Tumour mutational burden(TMB)has emerged as a predictive marker for responsiveness to immune checkpoint inhibitors(ICI)in multiple tumour types.It can be calculated from somatic mutations detected from whole exome or targeted panel sequencing data.As mutations are unevenly distributed across the cancer genome,the clinical implications from TMB calculated using different genomic regions are not clear.Methods: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 papers.TMB was calculated as the count of non-synonymous mutations and normalised by the size of genomic regions.Dirichlet method,linear regression and Poisson calibration models are used to unify TMB from different gene panels.Results:We found that panels based on cancer genes usually overestimate TMB compared to whole exome,potentially leading to misclassification of patients to receive ICI.The overestimation is caused by positive selection for mutations in cancer genes and cannot be completely addressed by the removal of mutational hotspots.We 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 treatment.We 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 TMB.Conclusion:Cancer gene-based panels usually overestimate TMB,and these findings will be valuable for unifying TMB calculations across cancer gene panels in clinical practice.展开更多
基金supported by the Research Grants Council,HK(grant number:17100920)seed funding from The University of Hong Kong.
文摘Background:Tumour mutational burden(TMB)has emerged as a predictive marker for responsiveness to immune checkpoint inhibitors(ICI)in multiple tumour types.It can be calculated from somatic mutations detected from whole exome or targeted panel sequencing data.As mutations are unevenly distributed across the cancer genome,the clinical implications from TMB calculated using different genomic regions are not clear.Methods: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 papers.TMB was calculated as the count of non-synonymous mutations and normalised by the size of genomic regions.Dirichlet method,linear regression and Poisson calibration models are used to unify TMB from different gene panels.Results:We found that panels based on cancer genes usually overestimate TMB compared to whole exome,potentially leading to misclassification of patients to receive ICI.The overestimation is caused by positive selection for mutations in cancer genes and cannot be completely addressed by the removal of mutational hotspots.We 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 treatment.We 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 TMB.Conclusion:Cancer gene-based panels usually overestimate TMB,and these findings will be valuable for unifying TMB calculations across cancer gene panels in clinical practice.
