Panel sequencing 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. Simulations in two WES datasets of immunotherapy treated patients revealed that small gene panels were poor predictors of ICB response. Moreover, we noted substantial intratumoral variance of psTMB scores in the TRACERx 100 cohort and identified indel burden as independent marker complementing missense mutation burden.
A universal mathematical law describes accuracy limitations inherent to psTMB, which result in substantial misclassification rates. This scenario can be controlled by two measures: (i) a panel design that is based on the mathematical law described in this article: 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 substantial implications for panel design, TMB testing, clinical trials and patient management.