Albrecht Stenzinger1, Volker Endris2, Jan Budczies2, Sabine Merkelbach-Bruse3, Daniel Kazdal4, Wolfgang Dietmaier5, Nicole Pfarr6, Udo Siebolts7, Michael Hummel8, Sylvia Herold9, Johanna Andreas10, Martin Zoche11, Lars Tögel12, Eugen Rempel2, Jörg Maas13, Diana Merino14, Mark Stewart14, Karim Zaoui15, Matthias Schlesner16, Hanno Glimm17, Stefan Fröhling18, Jeff Allen14, David Horst8, Gustavo Baretton9, Claudia Wickenhauser7, Markus Tiemann10, Matthias Evert5, Holger Moch11, Thomas Kirchner19, Reinhard Büttner3, Peter Schirmacher2, Andreas Jung19, Florian Haller12, Wilko Weichert6, Manfred Dietel13. 1. Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany; Translational Lung Research Center (TLRC) Heidelberg, German Center for Lung Research (DZL), Heidelberg, Germany. Electronic address: albrecht.stenzinger@med.uni-heidelberg.de. 2. Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany. 3. Institute of Pathology, University Hospital Cologne, Cologne, Germany. 4. Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany; Translational Lung Research Center (TLRC) Heidelberg, German Center for Lung Research (DZL), Heidelberg, Germany. 5. Institute of Pathology, University Regensburg, Regensburg, Germany. 6. Institute of Pathology, Technical University Munich (TUM), Munich, Germany. 7. Institute of Pathology, University Hospital Halle, Halle, Germany. 8. Institute of Pathology, Charité University Hospital, Berlin, Germany. 9. Institute of Pathology, University Hospital Dresden, Dresden, Germany. 10. Institute of Hematopathology, Hamburg, Germany. 11. Institute of Pathology, University Hospital Zurich, Zurich, Switzerland. 12. Institute of Pathology, University Hospital Erlangen, Erlangen, Germany. 13. Quality in Pathology (QuIP), Berlin, Germany. 14. Friends of Cancer Research (FoCR), Washington, District of Columbia. 15. Department of Otorhinolaryngology, University Hospital Heidelberg, Heidelberg, Germany. 16. Bioinformatics and Omics Data Analytics, German Cancer Research Center (DKFZ), Heidelberg, Germany. 17. Department of Translational Medical Oncology, National Center for Tumor Diseases (NCT Dresden) and University Hospital Carl Gustav Carus, Dresden, and Translational Functional Cancer Genomics, German Cancer Research Center (DKFZ), Heidelberg, Germany and German Cancer Consortium (DKTK), Dresden, Germany. 18. Division of Translational Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany. 19. Institute of Pathology, Ludwig-Maximilians University (LMU), Munich, Germany.
Abstract
INTRODUCTION: Tumor mutational burden (TMB) is a quantitative 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 carcinoma, 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 classification, 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 comparisons revealing a Spearman's ρ 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 artifacts, 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 parameters 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 evaluation of such assays.
INTRODUCTION:Tumor mutational burden (TMB) is a quantitative 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 carcinoma, 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 classification, 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 comparisons revealing a Spearman's ρ 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 artifacts, 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 parameters 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 evaluation of such assays.
Authors: D J McGrail; P G Pilié; N U Rashid; L Voorwerk; M Slagter; M Kok; E Jonasch; M Khasraw; A B Heimberger; B Lim; N T Ueno; J K Litton; R Ferrarotto; J T Chang; S L Moulder; S-Y Lin Journal: Ann Oncol Date: 2021-03-15 Impact factor: 32.976
Authors: Eva M Garrido-Martin; Luis Paz-Ares; Javier Ramos-Paradas; Susana Hernández-Prieto; David Lora; Elena Sanchez; Aranzazu Rosado; Tamara Caniego-Casas; Nuria Carrizo; Ana Belén Enguita; María Teresa Muñoz-Jimenez; Borja Rodriguez; Urbicio Perez-Gonzalez; David Gómez-Sánchez; Irene Ferrer; Santiago Ponce Aix; Ángel Nuñez Buiza; Pilar Garrido; José Palacios; Fernando Lopez-Rios Journal: J Immunother Cancer Date: 2021-05 Impact factor: 13.751
Authors: Daniel Kazdal; Eugen Rempel; Cristiano Oliveira; Michael Allgäuer; Alexander Harms; Kerstin Singer; Elke Kohlwes; Steffen Ormanns; Ludger Fink; Jörg Kriegsmann; Michael Leichsenring; Katharina Kriegsmann; Fabian Stögbauer; Luca Tavernar; Jonas Leichsenring; Anna-Lena Volckmar; Rémi Longuespée; Hauke Winter; Martin Eichhorn; Claus Peter Heußel; Felix Herth; Petros Christopoulos; Martin Reck; Thomas Muley; Wilko Weichert; Jan Budczies; Michael Thomas; Solange Peters; Arne Warth; Peter Schirmacher; Albrecht Stenzinger; Mark Kriegsmann Journal: Transl Lung Cancer Res Date: 2021-04