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. 2021 Dec;32(12):1626-1636.
doi: 10.1016/j.annonc.2021.09.016. Epub 2021 Oct 1.

Aligning tumor mutational burden (TMB) quantification across diagnostic platforms: phase II of the Friends of Cancer Research TMB Harmonization Project

D M Vega  1 , L M Yee  2 , L M McShane  2 , P M Williams  3 , L Chen  3 , T Vilimas  3 , D Fabrizio  4 , V Funari  5 , J Newberg  4 , L K Bruce  5 , S-J Chen  6 , J Baden  7 , J Carl Barrett  8 , P Beer  9 , M Butler  10 , J-H Cheng  6 , J Conroy  11 , D Cyanam  12 , K Eyring  13 , E Garcia  14 , G Green  7 , V R Gregersen  15 , M D Hellmann  16 , L A Keefer  17 , L Lasiter  1 , A J Lazar  18 , M-C Li  2 , L E MacConaill  14 , K Meier  19 , H Mellert  20 , S Pabla  11 , A Pallavajjalla  21 , G Pestano  20 , R Salgado  9 , R Samara  15 , E S Sokol  4 , P Stafford  22 , J Budczies  23 , A Stenzinger  23 , W Tom  12 , K C Valkenburg  17 , X Z Wang  24 , V Weigman  25 , M Xie  8 , Q Xie  26 , A Zehir  16 , C Zhao  19 , Y Zhao  2 , M D Stewart  27 , J Allen  1 ; TMB Consortium
Affiliations
Free article

Aligning tumor mutational burden (TMB) quantification across diagnostic platforms: phase II of the Friends of Cancer Research TMB Harmonization Project

D M Vega et al. Ann Oncol. 2021 Dec.
Free article

Erratum in

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 can help facilitate comparability across different panel assays, which may aid in broader adoption of panel assays and development of clinical applications.

Materials and methods: Twenty-nine tumor samples and 10 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 >667 Kb are necessary to maintain adequate PPA and NPA for calling TMB high versus TMB low across the range of cut-offs 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 resulted in the strongest correlation to WES TMB. Application of a calibration approach derived from The Cancer Genome Atlas 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.

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.

Keywords: biomarker; cancer; immunotherapy; precision medicine; tumor mutational burden.

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Conflict of interest statement

Disclosure XZW is an employee of EMD Serono Research and Development Institute. JN, DF, and ESS are all employees of Foundation Medicine, and ESS is a shareholder in Roche. VF and LKB are employees of Neogenomics and stockholders in NeoGenomics Inc. S-JC and J-HC are employees of ACT Genomics and stockholder in ACT Genomics. JB is employed with BMS, shareholder in BMS, and a shareholder in Johnson & Johnson. JC and SP are employed by OmniSeq, Inc. and hold restricted stock in OmniSeq, Inc. DC and WT are employed with Thermo Fisher Scientific and stockholder in Thermo Fisher Scientific. KE is an employee of Intermountain Genome Diagnostics. GG is employed by BMS and a stockholder in BMS. VRG and R. Samara are employed with QIAGEN. LAK and KCV are employed with Personal Genome Diagnostics. PS is employed by Caris Life Sciences. AS serves on advisory boards and/or receives speech honoraria from AIGnostics, Bayer, Thermo Fisher, Illumina, Astra Zeneca, Novartis, Pfizer, Roche, Seattle Genetics, MSD, BMS, Takeda, Janssen, and Eli-Lily; and research funding from: Chugai and Bristol Myers Squibb. MB is employed by LGC SeraCare. VW is employed with Q Squared Solutions. JCB and MX are employed by AstraZeneca. JCB is employed and holds shares of AstraZeneca. KM and CZ are employees of Illumina Inc and stockholders in Illumina Inc. HM and GP are employees and shareholders in Biodesix Inc. MDH has stock and other ownership interests in Shattuck Labs, Immunai, and Arcus Biosciences; reports honoraria from AstraZeneca and Bristol Myers Squibb; has a consulting or advisory role with Bristol Myers Squibb, Merck, Genentech/Roche, AstraZeneca, Nektar, Syndax, Mirati Therapeutics, Shattuck Labs, Immunai, Blueprint Medicines, Achilles Therapeutics, and Arcus Biosciences; receives research funding from Bristol Myers Squibb (Inst); has patents, royalties, and other intellectual property [a patent has been filed by Memorial Sloan Kettering (PCT/US2015/062208) for the use of TMB for prediction of immunotherapy efficacy, which is licensed to Personal Genome Diagnostics]; and receives travel and accommodation expense reimbursement from AstraZeneca, Bristol Myers Squibb, and Eli Lilly. All other authors have declared no conflicts of interest.

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