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. Issue 1 (26th March 2020)

• Record Type: Journal Article
• Authors: Merino, Diana M; McShane, Lisa M; Fabrizio, David; Funari, Vincent; Chen, Shu-Jen; White, James R; Wenz, Paul; Baden, Jonathan; Barrett, J Carl; Chaudhary, Ruchi; Chen, Li; Chen, Wangjuh (Sting); Cheng, Jen-Hao; Cyanam, Dinesh; Dickey, Jennifer S; Gupta, Vikas; Hellmann, Matthew; Helman, Elena; Li, Yali; Maas, Joerg; Papin, Arnaud; Patidar, Rajesh; Quinn, Katie J; Rizvi, Naiyer; Tae, Hongseok; Ward, Christine; Xie, Mingchao; Zehir, Ahmet; Zhao, Chen; Dietel, Manfred; Stenzinger, Albrecht; Stewart, Mark; Allen, Jeff;
• Is Part Of: Journal for immunotherapy of cancer. Volume 8:Issue 1(2020)
• Publication Date: 2020-03-26
• Subjects: TMB -- tumor mutational burden -- biomarker -- harmonization -- immunotherapies -- immune checkpoint inhibitors; Cancer -- Immunotherapy -- Periodicals; Cancer -- Immunological aspects -- Periodicals; Tumors -- Immunological aspects -- Periodicals; Immunotherapy -- Periodicals; 616.99406105
• DOI: 10.1136/jitc-2019-000147
• Languages: English
• ISSNs: 2051-1426

Abstract

Abstract : 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.