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Ann Oncol. 2019 Jul 3. pii: mdz205. doi: 10.1093/annonc/mdz205. [Epub ahead of print]

Optimizing panel-based tumor mutational burden (TMB) measurement.

Author information

Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany.
German Cancer Consortium (DKTK), Partner Site Heidelberg, Germany.
Cancer Evolution and Genome Instability Translational Cancer Therapeutics Laboratory, Francis Crick Institute, London, UK.
Department of Thoracic Oncology, Thoraxklinik at Heidelberg University Hospital, Heidelberg, Germany.
Translational Lung Research Center Heidelberg (TLRC-H), Heidelberg, Germany, Member of the German Center for Lung Research (DZL).
Department of Translational Oncology, National Center for Tumor Diseases (NCT) and German Cancer Research Center (DKFZ), Heidelberg, Germany.
Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV) and Lausanne University, Switzerland.



Panel-based estimates of Tumor Mutational Burden (psTMB) are increasingly replacing whole-exome-sequencing (WES) Tumor mutational burden as predictive biomarker of immune checkpoint blockade (ICB).


A mathematical law describing psTMB variability was derived using a random mutation model and complemented by the contributions of non-randomly mutated real-world cancer genomes and intratumoral heterogeneity through simulations in publicly available datasets.


The coefficient of variation (CV) of psTMB decreased inversely proportional with the square root of the panel size and the square root of the TMB level. In silico simulations of all major commercially available panels in the TCGA pan-cancer cohort confirmed the validity of this mathematical law and demonstrated that the CV was 35% for TMB=10 muts/Mbp for the largest panels of size 1.1-1.4 Mbp. Accordingly, misclassification rates (gold standard: WES) to separate "TMBhigh" from "TMBlow" using a cut-point of 199 mutations were 10-12% in TCGA-LUAD and 17-19% in TCGA-LUSC. A novel three-tier psTMB classification scheme which accounts for the likelihood of misclassification is proposed. Analysis of two independent datasets revealed that small gene panels were poor predictors of ICB response. Moreover, we noted significant intratumoral variance of psTMB scores in the TRACERx 100 cohort and identified indel burden in subsets of TMB high cases.


A universal mathematical law describes accuracy limitations inherent to psTMB, which result in significant misclassification rates. This scenario can be controlled by two measures: i) a panel design that is based on the mathematical law described herein: halving the CV requires a fourfold increase in panel size, ii) a novel three-tier TMB classification scheme. Moreover, inclusion of indel burden can complement TMB reports. This work has significant implications for panel design, TMB testing, clinical trials and patient management.


TMB; immuncheckpoint blockade; immune-oncology; panel sequencing; tumor mutational burden


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