Jan Budczies1, Daniel Kazdal2, Michael Allgäuer3, Petros Christopoulos4, Eugen Rempel3, Nicole Pfarr5, Wilko Weichert5, Stefan Fröhling6, Michael Thomas4, Solange Peters7, Volker Endris3, Peter Schirmacher8, Albrecht Stenzinger9. 1. Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany; German Cancer Consortium (DKTK), Partner Site Heidelberg, Heidelberg, Germany; German Center for Lung Research (DZL), Translational Lung Research Center Heidelberg (TLRC-H), Heidelberg, Germany. Electronic address: jan.budczies@med.uni-heidelberg.de. 2. Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany; German Center for Lung Research (DZL), Translational Lung Research Center Heidelberg (TLRC-H), Heidelberg, Germany. 3. Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany. 4. German Center for Lung Research (DZL), Translational Lung Research Center Heidelberg (TLRC-H), Heidelberg, Germany; Department of Thoracic Oncology, Thoraxklinik at Heidelberg University Hospital, Heidelberg, Germany. 5. Institute of Pathology, Technical University of Munich (TUM), Munich, Germany. 6. National Center for Tumor Diseases (NCT), German Cancer Research Center, Heidelberg, Germany. 7. Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne University, Switzerland. 8. Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany; German Cancer Consortium (DKTK), Partner Site Heidelberg, Heidelberg, Germany. 9. Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany; German Cancer Consortium (DKTK), Partner Site Heidelberg, Heidelberg, Germany; German Center for Lung Research (DZL), Translational Lung Research Center Heidelberg (TLRC-H), Heidelberg, Germany. Electronic address: albrecht.stenzinger@med.uni-heidelberg.de.
Abstract
OBJECTIVES: Retrospective data including subgroup analyses in clinical studies have sparked strong interest in developing tumor mutational burden (TMB) as a predictive biomarker for immune checkpoint blockade. While individual factors influencing panel sequencing based measurement of TMB (psTMB) have been discussed in the recent literature, an integrative study quantifying, comparing and combining all potential confounders is still missing. MATERIAL AND METHODS: We separated different potential confounders of psTMB measurement including "panel size", "germline mutation filtering", "biological variance" and "technical variance" and developed a specific error model for each of these factors. Published experimental psTMB data were fitted to the error models to quantify the contribution of each of the confounders. The total psTMB variance was obtained as sum over the variance contributions of each of the confounders. RESULTS: Using a typical large panel (size 1-1.5 Mbp) total errors of 57 %, 42 %, 34 % and 28 % were observed for tumors with psTMB of 5, 10, 20 and 40 muts/Mbp. Even for large panels, the stochastic error connected to the panel size represented the largest of all contributions to the total psTMB variance, especially for tumors with TMB up to 20 muts/Mbp. Other sources of psTMB variability could be kept under control, but rigorous quality control, best practice laboratory workflows and optimized bioinformatics pipelines are essential. CONCLUSION: A statistical framework for the analysis of complex, genomic biomarkers was developed and applied to the analysis of psTMB variability. The methods developed here can support the analysis of other quantitative biomarkers and their implementation in clinical practice.
OBJECTIVES: Retrospective data including subgroup analyses in clinical studies have sparked strong interest in developing tumor mutational burden (TMB) as a predictive biomarker for immune checkpoint blockade. While individual factors influencing panel sequencing based measurement of TMB (psTMB) have been discussed in the recent literature, an integrative study quantifying, comparing and combining all potential confounders is still missing. MATERIAL AND METHODS: We separated different potential confounders of psTMB measurement including "panel size", "germline mutation filtering", "biological variance" and "technical variance" and developed a specific error model for each of these factors. Published experimental psTMB data were fitted to the error models to quantify the contribution of each of the confounders. The total psTMB variance was obtained as sum over the variance contributions of each of the confounders. RESULTS: Using a typical large panel (size 1-1.5 Mbp) total errors of 57 %, 42 %, 34 % and 28 % were observed for tumors with psTMB of 5, 10, 20 and 40 muts/Mbp. Even for large panels, the stochastic error connected to the panel size represented the largest of all contributions to the total psTMB variance, especially for tumors with TMB up to 20 muts/Mbp. Other sources of psTMB variability could be kept under control, but rigorous quality control, best practice laboratory workflows and optimized bioinformatics pipelines are essential. CONCLUSION: A statistical framework for the analysis of complex, genomic biomarkers was developed and applied to the analysis of psTMB variability. The methods developed here can support the analysis of other quantitative biomarkers and their implementation in clinical practice.
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
Authors: Dan Sha; Zhaohui Jin; Jan Budczies; Klaus Kluck; Albrecht Stenzinger; Frank A Sinicrope Journal: Cancer Discov Date: 2020-11-02 Impact factor: 38.272
Authors: Jan Budczies; Martina Kirchner; Klaus Kluck; Daniel Kazdal; Julia Glade; Michael Allgäuer; Mark Kriegsmann; Claus-Peter Heußel; Felix J Herth; Hauke Winter; Michael Meister; Thomas Muley; Stefan Fröhling; Solange Peters; Barbara Seliger; Peter Schirmacher; Michael Thomas; Petros Christopoulos; Albrecht Stenzinger Journal: Oncoimmunology Date: 2021-01-11 Impact factor: 8.110
Authors: Solange Peters; Rafal Dziadziuszko; Alessandro Morabito; Enriqueta Felip; Shirish M Gadgeel; Parneet Cheema; Manuel Cobo; Zoran Andric; Carlos H Barrios; Masafumi Yamaguchi; Eric Dansin; Pongwut Danchaivijitr; Melissa Johnson; Silvia Novello; Michael S Mathisen; Sarah M Shagan; Erica Schleifman; Jin Wang; Mark Yan; Simonetta Mocci; David Voong; David A Fabrizio; David S Shames; Todd Riehl; David R Gandara; Tony Mok Journal: Nat Med Date: 2022-08-22 Impact factor: 87.241