Benchmarking whole exome sequencing in the German network for personalized medicine

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.

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Journal
European Journal of Cancer
Published
2024-09-08
DOI
https://doi.org/10.1016/j.ejca.2024.114306
Citations
5
Primary Topic
Cancer Genomics and Diagnostics
Type
article
Field-Weighted Citation Impact
1.14
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article

Benchmarking whole exome sequencing in the German network for personalized medicine

Silke Laßmann, Ralf Marienfeld, Verena Tischler, Uwe Gerstenmaier et al.
5 citations
European Journal of Cancer
Cancer Genomics and Diagnostics
1.14
article

Benchmarking whole exome sequencing in the German network for personalized medicine

Silke Laßmann, Ralf Marienfeld, Verena Tischler, Uwe Gerstenmaier, Markus Möbs, Jürgen Dönitz, Tobias B. Haack, Michael Menzel, Anna‐Lena Wulf, Wolfgang Goering, Katja Maurus, Sugirthan Sivalingam, Stephan Bartels, Steffen Hirsch, Wilko Weichert, Uta Matysiak, Bruno Märkl, Axel Fürstberger, Kjong-Van Lehmann, Matthias Evert, Patrick Basitta, Marcel Trautmann, Wilfried Roth, Albrecht Stenzinger, Andreas Jung, Philipp Ströbel, Christian Patrick Schaaf, Julia Doll, Nicola Dikow, Philipp A. Greif, Bernd Auber, Arndt Borkhardt, Eric Blanc, Matthias Begemann, Thomas Groß, Sorin Armeanu–Ebinger, Markus Ball, Florian Hölscher, Lars Tögel, Stefan Hamelmann, Steffen Wolter, Janna Siemanowski, Rouzbeh Banan, Jan P. Meier‐Kolthoff, Daniela Hirsch, Udo Siebolts, Eva Romanovsky, Oliver Kutz, Michael Kloth, Nadina Ortiz Brüchle, Nicole Pfarr, Christian Rückert, Kim D. Falkenberg, Christopher Schroeder, Nisar P. Malek, Sebastian Dintner, Frederick Klauschen, Alexander Immel, Lancelot Seillier, Maria Joosten, Daniela Aust, Evgeny A. Moskalev, Martin Werner, Daniel Kazdal, Tessa Rosenthal, Felix Sahm, Dieter Beule, Gunnar Schmidt, Angela Maurer, Rainer Claus, Danny Jonigk, Jörg Kumbrink, Doreen William, Andreas Möck, Wolfgang Dietmaier, David Horst, Winfried Hofmann, Eva-Maria Mayr, Nadine Therese Gaisa, Olaf Neumann, Silke Appenzeller, Sabine Merkelbach‐Bruse, Layal Yasin, Peter Schirmacher, Manuela Benary, Kai Horny, Stephan Ossowski, Jan Budczies, Kirsten Reuter‐Jessen, Santina Kirmse, Timo Eberhardt, Kirsten Göbel, Maria Giulia Carta, Ulrich Lehmann, Anne Jacob, Robert Meyer, Jan Rehker, Miriam Elbracht, Mihaela Martis-Thiele, Rebecca Prause, Phillip Jurmeister, Yvonne Jelting, Stefanie V Junk, Fulvia Ferrazzi, Eugen Adam, Justin Murtagh, A. Ott, Jakob Niewöhner, Annette Fink, Cornelia Blank, Hannah Goldschmid, Nils Hartmann
article en
5 citations

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.

European Journal of CancerVol. 211
University of Cologne (DE), Johannes Gutenberg University Mainz (DE), Heidelberg University (DE), University of Würzburg (DE), University Hospital Heidelberg (DE), Medizinische Hochschule Hannover (DE), University Hospital Münster (DE), University Hospital Cologne (DE), University of Regensburg (DE), Technical University of Munich (DE), University of Tübingen (DE), RWTH Aachen University (DE)
Zero hunger
Openalex Percentile: Top 21%
Cancer Genomics and Diagnostics
1.14
DOIOpen Access PDFOpenAlex
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