Description: Background: Tumor mutational burden (TMB) is a promising biomarker for stratifying patient subpopulation who would benefit from immune checkpoint blockade (ICB) therapies. However, TMB measurements between different methods are poorly comparable. Furthermore, the effect of low tumor content on TMB estimation has never been systematically investigated. A standardized data analysis pipeline for TMB calculation and reporting for samples with variable tumor content is needed. Design: We built a linear model to correlate TMB estimated using whole exome sequencing (wesTMB) with targeted panel sequencing (psTMB). We further produced 12 series of reference DNA samples derived from tumor-normal paired cell lines with variable TMB in the parental tumor cell lines. Each tumor cell line was mixed with its matched normal at 0% (control), 1%, 2%, 5%, and 10% mass-to-mass ratio to mimic the variable tumor content in clinical setup. The reference standards were distributed to five vendors. This comparative test was intended to challenge both the quantitative coherence of TMB measurement by different vendors and the validity of our in-silico model. Results: Simulated psTMB and wesTMB of standard samples fitted the linear model trained by TCGA data. Dilution simulation demonstrated the decisive role of low-VAF variations to TMB detection in low tumor content samples. For 5% and 10% diluted reference standards, variants calling were reproducible and could be confirmed compared to parental cell lines. Futhermore, psTMB and wesTMB correlated closely for TMB-high patient stratification based on the linear model. Conclusions: We developed a linear model to establish the quantitative correlation between wesTMB and psTMB. A set of DNA reference standards was produced in aid to standardize TMB measurements in samples with low tumor content across different targeted sequencing panels. This study is a significant contribution aiming to harmonize TMB estimation and extend its future application in clinical samples with low tumor content.

Data type: Exome; Variation

Sample scope: Multiisolate

Relevance: Medical

Release date: 2021-01-22

Last updated: 2021-01-13

Data size: 15.03TB