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Stenzinger, Albrecht; Endris, Volker; Budczies, Jan; Merkelbach-Bruse, Sabine; Kazdal, Daniel; Dietmaier, Wolfgang; Pfarr, Nicole; Siebolts, Udo; Hummel, Michael; Herold, Sylvia; Andreas, Johanna; Zoche, Martin; Toegel, Lars; Rempel, Eugen; Maas, Jörg; Merino, Diana; Stewart, Mark; Zaoui, Karim; Schlesner, Matthias; Glimm, Hanno; Froehling, Stefan; Allen, Jeff; Horst, David; Baretton, Gustavo; Wickenhauser, Claudia; Tiemann, Markus; Evert, Matthias; Moch, Holger; Kirchner, Thomas; Buettner, Reinhard; Schirmacher, Peter; Jung, Andreas; Haller, Florian; Weichert, Wilko und Dietel, Manfred (2020): Harmonization and Standardization of Panel-Based Tumor Mutational Burden Measurement: Real-World Results and Recommendations of the Quality in Pathology Study. In: Journal of Thoracic Oncology, Bd. 15, Nr. 7: S. 1177-1189

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Abstract

Introduction: Tumor mutational burden (TMB) is a quan-titative assessment of the number of somatic mutations within a tumor genome. Immunotherapy benefit has been associated with TMB assessed by whole-exome sequencing (wesTMB) and gene panel sequencing (psTMB). The initiatives of Quality in Pathology (QuIP) and Friends of Cancer Research have jointly addressed the need for harmonization among TMB testing options in tissues. This QuIP study identifies critical sources of variation in psTMB assessment. Methods: A total of 20 samples from three tumor types (lung adenocarcinoma, head and neck squamous cell car-cinoma, and colon adenocarcinoma) with available WES data were analyzed for psTMB using six panels across 15 testing centers. Interlaboratory and interplatform variation, including agreement on variant calling and TMB classifica-tion, were investigated. Bridging factors to transform psTMB to wesTMB values were empirically derived. The impact of germline filtering was evaluated. Results: Sixteen samples had low interlaboratory and interpanel psTMB variation, with 87.7% of pairwise com-parisons revealing a Spearman 's r greater than 0.6. A wesTMB cut point of 199 missense mutations projected to psTMB cut points between 7.8 and 12.6 mutations per megabase pair;the corresponding psTMB and wesTMB classifications agreed in 74.9% of cases. For three-tier classification with cut points of 100 and 300 mutations, agreement was observed in 76.7%, weak misclassification in 21.8%, and strong misclassification in 1.5% of cases. Confounders of psTMB estimation included fixation arti-facts, DNA input, sequencing depth, genome coverage, and variant allele frequency cut points. Conclusions: This study provides real-world evidence that all evaluated panels can be used to estimate TMB in a routine diagnostic setting and identifies important param-eters for reliable tissue TMB assessment that require careful control. As complex or composite biomarkers beyond TMB are likely playing an increasing role in therapy prediction, the efforts by QuIP and Friends of Cancer Research also delineate a general framework and blueprint for the eval-uation of such assays. (C) 2020 International Association for the Study of Lung Cancer.

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