Tumor mutational burden (TMB) is an emerging biomarker that correlates with response to immunotherapeutic agents, such as checkpoint inhibitors. Recent studies indicate that a high mutation load increases the likelihood that immunogenic neoantigens expressed by tumor cells may induce a response to immunotherapy.  However, TMB estimation and reporting can be heavily influenced by differing working processes across clinical and research laboratories; primarily, the choice of assay, platform, and how the assay is implemented and interpreted. 

In the first half of the webinar, we introduce the QIAseq Tumor Mutational Burden (TMB) Panel, a comprehensive next-generation sequencing (NGS) assay that targets the full coding region of 486 genes implicated in the pathogenesis of solid tumors and can be boosted to add 27 microsatellite instability (MSI) markers. From optimized primer design based on the robust UMI-enabled single primer extension (SPE) chemistry, which reduces false positives and eliminates duplicate reads, and now coupled with the CLC Genomics Workbench, the QIAseq Tumor Mutation Burden Panel ensures TMB estimation consistent with best practices as reported by the Friends of Cancer Research (Genes, Chromosomes & Cancer, 2019). With QIAGEN Clinical Insight (QCI™) – Interpret, insights for variants detected, based on private and publicly available data sources, are provided, enabling a better understanding of these variants and their role in tumor initiation, growth, spread and recurrence.

In the second half we present a scalable bioinformatics solution for the interpretation of somatic mutations. Cancer laboratories need rapid and reliable interpretation of identified genomic alterations. One of the challenges they face is producing standardized, reproducible interpretation and reporting of the most current and actionable information. QIAGEN Clinical Insight (QCI™) Interpret enables labs to deliver evidence-based, actionable insights and reporting of variants and TMB and MSI scores from tumor genomic profiles. We will discuss the benefits of automating guidelines (AMP/ASCO/CAP and ACMG/AMP) for vetting somatic cancer alterations for biological and actionable relevance while providing the evidence behind each automated classification via direct links to the source, facilitating insights that improve clinical trial matching.

Learning Objectives: 

1. Learn how the coupled solution of UMI-enabled single primer extension (SPE) and the well renowned CLC algorithms for secondary analysis enables the coverage and high confidence detection of variants, even those at low frequency (as low as 0.4%) while minimizing impact of PCR duplicates and false positives which could inflate TMB scores
2. Learn how the QIAGEN Clinical Insight (QCI™)- Interpret clinical decision support platform can enable rapid and consistent reporting of variant pathogenicity and actionability, resulting in better clinical trial matching.