Aligning Tumor Mutational Burden (TMB) quantification across diagnostic platforms: Phase 2 of the Friends of Cancer Research TMB Harmonization Project.

ABSTRACT Background Tumor Mutational Burden (TMB) measurements aid in identifying patients who are likely to benefit from immunotherapy; however, there is empirical variability across panel assays and factors contributing to this variability have not been comprehensively investigated. Identifying sources of variability and the development and use of a calibration tool that can help facilitate comparability across different panel assays may aid in broader adoption of panels assays and development of clinical applications. Materials and methods Twenty-nine tumor samples and ten human-derived cell lines were processed and distributed to 16 laboratories; each used their own bioinformatics pipelines to calculate TMB and compare to whole exome results. Additionally, theoretical positive percent agreement (PPA) and negative percent agreement (NPA) of TMB were estimated. The impact of filtering pathogenic and germline variants on TMB estimates was assessed. Calibration curves specific to each panel assay were developed to facilitate translation of panel TMB values to whole exome sequencing (WES) TMB values. Results Panel sizes greater than 667Kb are necessary to maintain adequate PPA and NPA for calling TMB high versus TMB low across the range of cutoffs used in practice. Failure to filter out pathogenic variants when estimating panel TMB resulted in overestimating TMB relative to WES for all assays. Filtering out potential germline variants at >0% population minor allele frequency (pMAF) resulted in the strongest correlation to WES TMB. Application of a calibration approach derived from TCGA data, tailored to each panel assay, reduced the spread of panel TMB values around the WES TMB as reflected in lower root mean squared error (RMSE) for 26/29 (90%) of the clinical samples, although RMSE across samples at the laboratory level was less often reduced. Conclusions Estimation of TMB varies across different panels, with panel size, gene content, and bioinformatics pipelines contributing to empirical variability. Statistical calibration can achieve more consistent results across panels and allows for comparison of TMB values across various panel assays. To promote reproducibility and comparability across assays, a software tool was developed and made publicly available.