Statistical Bioinformatics Webinar: Hu -- Optimising Tumor Mutation Burden Estimation from Targeted Panel Sequencing Data

Tumor mutation burden (TMB) has emerged as a predictive marker for responsiveness to
immune checkpoint blockade in multiple tumor types. As the gold standard, TMB is
quantitated from whole exome data, but in a clinical setting it is generally
approximated from targeted panel sequencing data. In this study, we systematically
evaluate parameters that could affect the panel-based TMB (pTMB) assessment including
panel size, gene content and local mutation determinants. By analysis simulated pTMB
across different independent cohorts, we found that panels that based on cancer genes
usually overestimate TMB, leading to misclassification of patients to receive improper
therapy. This might be caused by positive selection for mutations on cancer genes and
unlikely alleviate by removal of hotspots. To overcome this issue, we develop a
parsimonious model that is capable of optimising pTMB estimation, with improved
performance for patient stratification to clinical management. These findings may be
immediately applicable for guiding accurate TMB approximation based on targeted panel
sequencing data.