Tumor mutational burden quantification from targeted gene panels: major advancements and challenges

Abstract

Tumor mutational burden (TMB), the total number of somatic coding mutations in a tumor, is emerging as a promising biomarker for immunotherapy response in cancer patients. TMB can be quantitated by a number of NGS-based sequencing technologies. Whole Exome Sequencing (WES) allows comprehensive measurement of TMB and is considered the gold standard. However, to date WES remains confined to research settings, due to high cost of the large genomic space sequenced. In the clinical setting, instead, targeted enrichment panels (gene panels) of various genomic sizes are emerging as the routine technology for TMB assessment. This stimulated the development of various methods for panel-based TMB quantification, and prompted the multiplication of studies assessing whether TMB can be confidently estimated from the smaller genomic space sampled by gene panels. In this review, we inventory the collection of available gene panels tested for this purpose, illustrating their technical specifications and describing their accuracy and clinical value in TMB assessment. Moreover, we highlight how various experimental, platform-related or methodological variables, as well as bioinformatic pipelines, influence panel-based TMB quantification. The lack of harmonization in panel-based TMB quantification, of adequate methods to convert TMB estimates across different panels and of robust predictive cutoffs, currently represents one of the main limitations to adopt TMB as a biomarker in clinical practice. This overview on the heterogeneous landscape of panel-based TMB quantification aims at providing a context to discuss common standards and illustrates the strong need of further validation and consolidation studies for the clinical interpretation of panel-based TMB values. Electronic supplementary material The online version of this article (10.1186/s40425-019-0647-4) contains supplementary material, which is available to authorized users.

Prognostic role of tumor mutation burden in hepatocellular carcinoma after radical hepatectomy

“…Tumor mutation burden (TMB) defined as the total number of mutations (such as point mutation, insertion or deletion mutation, frame‐shift mutation) per coding area in a tumor genome may range from a few to thousands of somatic mutations 4 . Somatic mutations may be associated with the production of neoantigens that would be recognized as nonself by the immune system 5 . Currently, TMB has emerged as a novel potential biomarker in immune checkpoint inhibitor (ICI) therapy 6–8 and is correlated with an objective response rate of ICIs in many tumor types 9 .…”

“…4 Somatic mutations may be associated with the production of neoantigens that would be recognized as nonself by the immune system. 5 Currently, TMB has Huayong Cai and Yu Zhang contributed equally to this study. emerged as a novel potential biomarker in immune checkpoint inhibitor (ICI) therapy [6][7][8] and is correlated with an objective response rate of ICIs in many tumor types.…”

The landscape of tumor mutational burden and its clinical significance in patients with lung cancer: a Multi-omics study with a meta-analysis

“…However, the TMB value detected by WES is higher than that detected by targeted NGS, which may be because the WES method contains more mutation sites. In clinical applications, targeted NGS is obviously more suitable for clinical work than WES because it saves testing resources and time [34]. At present, different types and quantities of gene panels are used for targeted NGS in different institutions, which results in various TMB benchmark values detected by various institutions.…”

The Multifaceted Nature of Tumor Microenvironment in Breast Carcinomas

“…The mutation rate is the key value defining TMB, calculated as the number of mutations per mega-base of a genome. This can be devised by whole exome sequencing (WES) data (cumbersome in routine diagnostic practice) or from more feasible targeted panels, with a longitudinal coverage higher than 1.3 Mb in size (reviewed in Fancello et al [59]). There is not a unique cut-off limit used to discern between high-TMB and low-TMB tumors: as pointed out by Samstein et al [60] the right TMB cut-off cannot be completely gene panel-, histology-and clinical question-agnostic.…”