Journal of Clinical Oncology, Tempus-authored – Background: TMB is an emerging predictor of survival with immunotherapy. TMB is determined by taking the difference between somatic and germline datasets when tumor-normal pairs are available. In the case of commonly utilized tumor-only sequencing, additional steps are needed to estimate the somatic alterations. Computational tools have been developed that determine germline contribution based on sample copy state, purity estimates and occurrence of the variant in population databases. Given the potential bias of population datasets, we hypothesized that tumor-only filtering approaches may overestimate the actual TMB.
Methods: We assessed the TMB from 50 tumors in 10 diseases including all missense, indels, and frameshift variants with an allelic fraction (AF) ≥5% and Coverage ≥100X within the tumor. Tumor-only TMB was evaluated against the gold standard of matched germline subtracted TMB at three levels. Level 1 removed all the tumor-only variants with AF in the non-TCGA ExAC database ≥1%. Level 2 removed all variants observed in population databases simulating a naive approach of removing germline variation. Level 3 used an internal tumor-only pipeline for calculating TMB.
Results: There were significantly higher estimates of TMB with Level 1, Level 2 and Level 3 tumor-only filtering approaches than that determined by germline subtraction, resulting in significant bias. Whereas there was no correlation between TMB estimates and tumor-germline TMB for Level 1 filtering, there were improvements in correlations for Level 2 and Level 3.
Conclusions: The tumor-only approaches that filter variants in population databases overestimate TMB compared to that determined by germline subtraction. Despite improved correlations with more stringent filtering approaches, these falsely elevated estimates may result in the inappropriate categorization of tumor specimens and negatively impact clinical trial results and patient outcomes.
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Authors: Kaushal Parikh, Robert Huether, Kevin White, Derick Hoskinson, Alex A. Adjei, and Aaron S. Mansfield