Benchmarking whole exome sequencing in the German network for personalized medicine

Menzel M, Martis-Thiele M, Goldschmid H, Ott A, Romanovsky E, Siemanowski-Hrach J, Seillier L, Brüchle NO, Maurer A, Lehmann KV, Begemann M, Elbracht M, Meyer R, Dintner S, Claus R, Meier-Kolthoff JP, Blanc E, Möbs M, Joosten M, Benary M, Basitta P, Hölscher F, Tischler V, Groß T, Kutz O, Prause R, William D, Horny K, Goering W, Sivalingam S, Borkhardt A, Blank C, Junk SV, Yasin L, Moskalev E, Carta MG, Ferrazzi F, Tögel L, Wolter S, Adam E, Matysiak U, Rosenthal T, Dönitz J, Lehmann U, Schmidt G, Bartels S, Hofmann W, Hirsch S, Dikow N, Göbel K, Banan R, Hamelmann S, Fink A, Ball M, Neumann O, Rehker J, Kloth M, Murtagh J, Hartmann N, Jurmeister P, Mock A, Kumbrink J, Jung A, Mayr EM, Jacob A, Trautmann M, Kirmse S, Falkenberg K, Ruckert C, Hirsch D, Immel A, Dietmaier W, Haack T, Marienfeld R, Fürstberger A, Niewöhner J, Gerstenmaier U, Eberhardt T, Greif PA, Appenzeller S, Maurus K, Doll J, Jelting Y, Jonigk D, Märkl B, Beule D, Horst D, Wulf AL, Aust D, Werner M, Reuter-Jessen K, Ströbel P, Auber B, Sahm F, Merkelbach-Bruse S, Siebolts U, Roth W, Lassmann S, Klauschen F, Gaisa NT, Weichert W, Evert M, Armeanu-Ebinger S, Ossowski S, Schroeder C, Schaaf CP, Malek N, Schirmacher P, Kazdal D, Pfarr N, Budczies J, Stenzinger A (2024)

Publication Type: Journal article

Publication year: 2024

Journal

Book Volume: 211

Article Number: 114306

DOI: 10.1016/j.ejca.2024.114306

Abstract

Introduction: Whole Exome Sequencing (WES) has emerged as an efficient tool in clinical cancer diagnostics to broaden the scope from panel-based diagnostics to screening of all genes and enabling robust determination of complex biomarkers in a single analysis. Methods: To assess concordance, six formalin-fixed paraffin-embedded (FFPE) tissue specimens and four commercial reference standards were analyzed by WES as matched tumor-normal DNA at 21 NGS centers in Germany, each employing local wet-lab and bioinformatics. Somatic and germline variants, copy-number alterations (CNAs), and complex biomarkers were investigated. Somatic variant calling was performed in 494 diagnostically relevant cancer genes. The raw data were collected and re-analyzed with a central bioinformatic pipeline to separate wet- and dry-lab variability. Results: The mean positive percentage agreement (PPA) of somatic variant calling was 76 % while the positive predictive value (PPV) was 89 % in relation to a consensus list of variants found by at least five centers. Variant filtering was identified as the main cause for divergent variant calls. Adjusting filter criteria and re-analysis increased the PPA to 88 % for all and 97 % for the clinically relevant variants. CNA calls were concordant for 82 % of genomic regions. Homologous recombination deficiency (HRD), tumor mutational burden (TMB), and microsatellite instability (MSI) status were concordant for 94 %, 93 %, and 93 % of calls, respectively. Variability of CNAs and complex biomarkers did not decrease considerably after harmonization of the bioinformatic processing and was hence attributed mainly to wet-lab differences. Conclusion: Continuous optimization of bioinformatic workflows and participating in round robin tests are recommended.

Authors with CRIS profile

Involved external institutions

Universitätsklinikum Heidelberg Germany (DE) Technische Universität München (TUM) Germany (DE) Eberhard Karls Universität Tübingen Germany (DE) Centrum für Integrierte Onkologie Aachen Bonn Köln Düsseldorf (CIO ABCD) Germany (DE) Universitätsklinikum Düsseldorf Germany (DE) Universitätsklinikum Freiburg Germany (DE) Universitätsklinikum Göttingen Germany (DE) Medizinische Hochschule Hannover (MHH) / Hannover Medical School Germany (DE) Ruprecht-Karls-Universität Heidelberg Germany (DE) Universitätsklinikum Köln Germany (DE) Universitätsklinikum Aachen (UKA) Germany (DE) Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen Germany (DE) Universität Augsburg Germany (DE) Berliner Institut für Gesundheitsforschung (BIH) Germany (DE) Charité - Universitätsmedizin Berlin Germany (DE) Universitätsklinikum Bonn Germany (DE) Universitätsklinikum Carl Gustav Carus Dresden Germany (DE) Universitätsklinikum Ulm Germany (DE) Deutsches Krebsforschungszentrum (DKFZ) Germany (DE) Universitätsklinikum Würzburg Germany (DE) Julius-Maximilians-Universität Würzburg Germany (DE) Universitätsmedizin der Johannes Gutenberg-Universität Mainz Germany (DE) Ludwig-Maximilians-Universität (LMU) Germany (DE) Universitätsklinikum Münster Germany (DE) Universität Regensburg Germany (DE)

How to cite

APA:

Menzel, M., Martis-Thiele, M., Goldschmid, H., Ott, A., Romanovsky, E., Siemanowski-Hrach, J.,... Stenzinger, A. (2024). Benchmarking whole exome sequencing in the German network for personalized medicine. European Journal of Cancer, 211. https://doi.org/10.1016/j.ejca.2024.114306

MLA:

Menzel, Michael, et al. "Benchmarking whole exome sequencing in the German network for personalized medicine." European Journal of Cancer 211 (2024).

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