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.

Development and Analytical Validation of a DNA Dual-Strand Approach for the US Food and Drug Administration-Approved Next-Generation Sequencing-Based Praxis Extended RAS Panel for Metastatic Colorectal Cancer Samples.

A next-generation sequencing method was developed that can distinguish single-stranded modifications from low-frequency somatic mutations present on both strands of DNA in formalin-fixed paraffin-embedded colorectal cancer samples. We applied this method for analytical validation of the Praxis Extended RAS Panel, a US Food and Drug Administration-approved companion diagnostic for panitumumab, on the Illumina MiSeqDx platform. With the use of the TruSeq amplicon workflow, both strands of DNA from the starting material were interrogated independently. Mutations were reported only if found on both strands; artifacts usually present on only one strand would not be reported. A total of 56 mutations were targeted within the KRAS and NRAS genes. A minimum read depth of 1800× per amplicon is required per sample but averaged >30,000× at maximum multiplexing levels. Analytical validation studies were performed to determine the simultaneous detection of mutations on both strands, reproducibility, assay detection level, precision of the assay across various factors, and the impact of interfering substances. In conclusion, this assay can clearly distinguish single-stranded artifacts from low-frequency mutations. Furthermore, the assay is accurate, precise, and reproducible, can achieve consistent detection of a mutation at 5% mutation frequency, exhibits minimal impact from tested interfering substances, and can simultaneously detect 56 mutations in a single run using 10 samples plus controls.