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Measurement of tumor mutational burden (TMB) in routine molecular diagnostics: in silico and real‐life analysis of three larger gene panels
Author(s) -
Endris Volker,
Buchhalter Ivo,
Allgäuer Michael,
Rempel Eugen,
Lier Amelie,
Volckmar AnnaLena,
Kirchner Martina,
Winterfeld Moritz,
Leichsenring Jonas,
Neumann Olaf,
Penzel Roland,
Weichert Wilko,
Glimm Hanno,
Fröhling Stefan,
Winter Hauke,
Herth Felix,
Thomas Michael,
Schirmacher Peter,
Budczies Jan,
Stenzinger Albrecht
Publication year - 2019
Publication title -
international journal of cancer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.475
H-Index - 234
eISSN - 1097-0215
pISSN - 0020-7136
DOI - 10.1002/ijc.32002
Subject(s) - in silico , computational biology , gene , genetics , molecular diagnostics , biology , bioinformatics
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.