Tumor mutational burden (TMB) is a new biomarker for prediction of response to PD-(L)1 treatment. Comprehensive sequencing approaches (i.e., whole exome and whole genome sequencing) are ideally suited to measure TMB directly. However, as their applicability in routine diagnostics is currently limited by high costs, long turnaround times and poor availability of fresh tissue, targeted next-generation sequencing (NGS) of formalin-fixed and paraffin-embedded (FFPE) samples appears to be a more feasible and straightforward approach for TMB approximation, which can be seamlessly integrated in already existing diagnostic workflows and pipelines. In this work, we provide an overview of the clinical implications of TMB testing and highlight key parameters including pre-analysis, analysis and post-analytical steps that influence and shape TMB approximation by panel sequencing. Collectively, the data will not only serve as a field guide and state of the art knowledge source for molecular pathologists who consider implementation of TMB measurement in their lab, but also enable clinicians in understanding the specific parameters influencing TMB test results and reporting.

Tumor mutational burden (TMB) represents a new determinant of clinical benefit from immune checkpoint blockade that identifies responders independent of PD-L1 expression levels and is currently being explored in clinical trials. Although TMB can be measured directly by comprehensive genomic approaches such as whole-genome and exome sequencing, broad availability, short turnaround times, costs and amenability to formalin-fixed and paraffin-embedded tissue support the use of gene panel sequencing for approximating TMB in routine diagnostics. However, data on the parameters influencing panelbased TMB estimation are limited. Here, we report an extensive in silico analysis of the TCGA data set that simulates various panel sizes and compositions. We demonstrate that panel size is a critical parameter that influences confidence intervals (CIs) and cutoff values as well as important test parameters including sensitivity, specificity, and positive predictive value. Moreover, we evaluate the Illumina TSO500 panel, which will be made available for TMB estimation, and propose dynamic, entity-specific cutoff values based on current clinical trial data. Optimizing the cost-benefit ratio, our data suggest that panels between 1.5 and 3 Mbp are ideally suited to estimate TMB with small CIs, whereas smaller panels tend to deliver imprecise TMB estimates for low to moderate TMB (0-30 muts/Mbp), connected with insufficient separation of hypermutated tumors from non-hypermutated tumors.

Assessment of Tumor Mutational Burden (TMB) for response stratification of cancer patients treated with immune checkpoint inhibitors is emerging as a new biomarker. Commonly defined as the total number of exonic somatic mutations, TMB approximates the amount of neoantigens that potentially are recognized by the immune system. While whole exome sequencing (WES) is an unbiased approach to quantify TMB, implementation in diagnostics is hampered by tissue availability as well as time and cost constrains. Conversely, panel-based targeted sequencing is nowadays widely used in routine molecular diagnostics, but only very limited data are available on its performance for TMB estimation. Here, we evaluated three commercially available larger gene panels with covered genomic regions of 0.39 Megabase pairs (Mbp), 0.53 Mbp and 1.7 Mbp using i) in silico analysis of TCGA (The Cancer Genome Atlas) data and ii) wet-lab sequencing of a total of 92 formalin-fixed and paraffin-embedded (FFPE) cancer samples grouped in three independent cohorts (non-small cell lung cancer, NSCLC; colorectal cancer, CRC; and mixed cancer types) for which matching WES data were available. We observed a strong correlation of the panel data with WES mutation counts especially for the gene panel >1Mbp. Sensitivity and specificity related to TMB cutpoints for checkpoint inhibitor response in NSCLC determined by wet-lab experiments well reflected the in silico data. Additionally, we highlight potential pitfalls in bioinformatics pipelines and provide recommendations for variant filtering. In summary, our study is a valuable data source for researchers working in the field of immuno-oncology as well as for diagnostic laboratories planning TMB testing. What's new?Tumor mutational burden (TMB) is an emerging biomarker to predict response to immune checkpoint inhibitors. While TMB can be measured by whole exome sequencing (WES), costs, turn-around time, and tissue availability currently favor a panel sequencing approach using FFPE tissue for routine diagnostics. However, performance remains to be clarified. Our study is the first worldwide that analyses three major commercial gene panels by comparing TMB approximation across panels as well as against the technical reference standard WES. The data suggest that TMB approximation using gene panel sequencing of FFPE tumor tissue is feasible and can be implemented in routine diagnostics.