Establishing guidelines to harmonize tumor mutational burden (TMB): in silico assessment of variation in TMB quantification across diagnostic platforms: phase I of the Friends of Cancer Research TMB Harmonization Project.

BACKGROUND

Tumor mutational burden (TMB), defined as the number of somatic mutations per megabase of interrogated genomic sequence, demonstrates predictive biomarker potential for the identification of patients with cancer most likely to respond to immune checkpoint inhibitors. TMB is optimally calculated by whole exome sequencing (WES), but next-generation sequencing targeted panels provide TMB estimates in a time-effective and cost-effective manner. However, differences in panel size and gene coverage, in addition to the underlying bioinformatics pipelines, are known drivers of variability in TMB estimates across laboratories. By directly comparing panel-based TMB estimates from participating laboratories, this study aims to characterize the theoretical variability of panel-based TMB estimates, and provides guidelines on TMB reporting, analytic validation requirements and reference standard alignment in order to maintain consistency of TMB estimation across platforms.

METHODS

Eleven laboratories used WES data from The Cancer Genome Atlas Multi-Center Mutation calling in Multiple Cancers (MC3) samples and calculated TMB from the subset of the exome restricted to the genes covered by their targeted panel using their own bioinformatics pipeline (panel TMB). A reference TMB value was calculated from the entire exome using a uniform bioinformatics pipeline all members agreed on (WES TMB). Linear regression analyses were performed to investigate the relationship between WES and panel TMB for all 32 cancer types combined and separately. Variability in panel TMB values at various WES TMB values was also quantified using 95% prediction limits.

RESULTS

Study results demonstrated that variability within and between panel TMB values increases as the WES TMB values increase. For each panel, prediction limits based on linear regression analyses that modeled panel TMB as a function of WES TMB were calculated and found to approximately capture the intended 95% of observed panel TMB values. Certain cancer types, such as uterine, bladder and colon cancers exhibited greater variability in panel TMB values, compared with lung and head and neck cancers.

CONCLUSIONS

Increasing uptake of TMB as a predictive biomarker in the clinic creates an urgent need to bring stakeholders together to agree on the harmonization of key aspects of panel-based TMB estimation, such as the standardization of TMB reporting, standardization of analytical validation studies and the alignment of panel-based TMB values with a reference standard. These harmonization efforts should improve consistency and reliability of panel TMB estimates and aid in clinical decision-making.

Informations de copyright

© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Déclaration de conflit d'intérêts

Competing interests: DF: employment with Foundation Medicine and stockholder in Roche. VF: employment with NeoGenomics Inc and stockholder in NeoGenomics Inc. S-JC: employment with ACT Genomics and stockholder in ACT Genomics. JRW: founder and owner of Resphira Biosciences and paid consultant to PGDx. PW: employment with Illumina and stockholder in Illumina. JB: employment with Bristol-Myers Squibb, shareholder in Bristol-Myers Squibb and shareholder in Johnson & Johnson. JCB: employment with AstraZeneca Pharmaceuticals and stocks in AstraZeneca Pharmaceuticals. RC: employment with Thermo Fisher Scientific. WSC: employment with Caris Life Sciences. JHC: employment with ACT Genomics. DC: employment with Thermo Fisher Scientific and stockholder in Thermo Fisher Scientific. JSD: employment with Personal Genome Diagnostics. VG: employment with QIAGEN. MH: received research funding from Bristol-Myers Squibb; is paid a consultant to Merck, Bristol-Myers Squibb, AstraZeneca, Genentech/Roche, Janssen, Nektar, Syndax, Mirati and Shattuck Labs; has received travel support/honoraria from AztraZeneca and BMS and a patent has been filed by MSK related to the use of tumor mutation burden to predict response to immunotherapy (PCT/US2015/062208), which has received licensing fees from PGDx. EH: employment with Guardant Health Inc and stockholder in Guardant Health Inc. YL: employment with Foundation Medicine while engaged in the research project (March 2019). Currently, an employee of Thrive Sciences, Inc and stockholder of Thrive Sciences, Inc. AP: employment with QIAGEN. KJQ: employment with Guardant Health Inc and stockholder in Guardant Health Inc. NR: NR and Memorial Sloan Kettering Cancer Center have a patent filing (PCT/US2015/062208) for the use of tumor mutation burden and HLA for prediction of immunotherapy efficacy, which is licensed to Personal Genome Diagnostics. NR is a founder, shareholder and serves on the scientific advisory board of Gritstone Oncology. NR has also consulted for AbbVie, AstraZeneca, BMS, EMD Sorono, Genentech, GSK, Janssen, Lilly, Merck, Novartis, Pfizer, Regeneron. HT: employment with Caris Life Sciences. CW: employment with Bristol-Myers Squibb, shareholder in Bristol-Myers Squibb ad shareholder in Johnson & Johnson. MX: employment with AstraZeneca Pharmaceuticals. CZ: employment with Illumina and stockholder in Illumina. AS: advisory board and/or speech honoraria from: Bayer, BMS, MSD, Novartis, AstraZeneca, Roche, Seattle Genomics, Illumina, Thermo Fisher, Takeda. Research funding from: Chugai, BMS.