1. DNA methylation-based classification of central nervous system tumours
- Author
-
Till Milde, Matija Snuderl, Martin Bendszus, Ralf Ketter, Catherine Keohane, Marco Prinz, Katja von Hoff, Aristotelis Tsirigos, Hildegard Dohmen, Manfred Westphal, Ute Pohl, Gabriele Schackert, Christian Koelsche, Eleonora Aronica, Bernhard Radlwimmer, Bjarne Winther Kristensen, Martin Hasselblatt, David T.W. Jones, Christian Mawrin, Dominik Sturm, Patricia Kohlhof, Peter Lichter, Annekathrin Kratz, Anne K. Braczynski, Helmut Mühleisen, Wolf Mueller, Wolfgang Brück, Stephan Frank, Andreas Unterberg, Michael Weller, Matthias A. Karajannis, Astrid Gnekow, Lukas Chavez, Andreas E. Kulozik, Christoph Geisenberger, Christine Haberler, Ori Staszewski, Amar Gajjar, Stephanie Rozsnoki, Mélanie Pagès, Olaf Witt, Paul A. Northcott, Matt Lechner, Thomas S. Jacques, Martina Deckert, Axel Benner, Jordan R. Hansford, Ingmar Blümcke, Marina Ryzhova, Gudrun Fleischhack, Jonathan Serrano, Jens Schittenhelm, Martin Sill, Sebastian Brandner, Stephan Tippelt, Dietmar R. Lohmann, Hermann L. Müller, Petra Temming, Nils W. Engel, Khalida Wani, Pablo Hernáiz Driever, Christel Herold-Mende, David W. Ellison, Arie Perry, Michael C. Frühwald, Stefan M. Pfister, Christof M. Kramm, Stefanie Brehmer, Daniel Hänggi, Jane Cryan, Torsten Pietsch, Wolfram Scheurlen, Marcel Seiz-Rosenhagen, Volkmar Hans, Adriana Olar, Werner Paulus, Chris Jones, Annie Huang, Patrick N. Harter, Felice Giangaspero, Marcel Kool, Kenneth Aldape, Marco Gessi, Silvia Hofer, Fausto J. Rodriguez, Anne Jouvet, Roland Coras, Annika K. Wefers, Leonille Schweizer, Vincent Peter Collins, Beatriz Lopes, Rolf Bjerkvig, Matthias Schick, Michel Mittelbronn, Andrey Korshunov, Johannes Schramm, Marc Zapatka, Annett Hölsken, Michael Platten, Kerstin Lindenberg, Jürgen Debus, Christian Hartmann, Ekkehard Hewer, Pascale Varlet, Melanie Bewerunge-Hudler, Till Acker, Matthias Preusser, Elisabeth J. Rushing, Michael A. Farrell, Kristian W. Pajtler, Nada Jabado, Kasthuri Kannan, Wolfgang Wick, David E. Reuss, Rolf Buslei, Nicholas G. Gottardo, Giles W. Robinson, Stefan Rutkowski, Jürgen Hench, Andreas von Deimling, Ulrich Schüller, Zane Jaunmuktane, Pieter Wesseling, Hendrik Witt, Albert J. Becker, Frank L. Heppner, Roger Fischer, Ziad Khatib, Guido Reifenberger, Arend Koch, Gabriele Calaminus, Karl H. Plate, Volker Hovestadt, Michael D. Taylor, Camelia-Maria Monoranu, Damian Stichel, Felix Sahm, Kristin Huang, David Capper, Florian Selt, Daniel Schrimpf, Rudi Beschorner, Boyan K. Garvalov, Pathology, Amsterdam Neuroscience - Brain Imaging, Amsterdam Neuroscience - Systems & Network Neuroscience, CCA - Imaging and biomarkers, APH - Mental Health, ANS - Cellular & Molecular Mechanisms, and APH - Aging & Later Life more...
- Subjects
Adult ,Male ,0301 basic medicine ,Adolescent ,DNA methylation-based classification ,Central nervous system ,Medizin ,Bioinformatics ,CNS cancer ,Central Nervous System Neoplasms ,Cohort Studies ,Young Adult ,03 medical and health sciences ,0302 clinical medicine ,Age groups ,central nervous system tumours ,pathological diagnosis ,cancer ,Humans ,Medicine ,Central Nervous System Neoplasms/classification ,General ,Child ,Aged ,Aged, 80 and over ,Multidisciplinary ,business.industry ,Extramural ,Infant ,Reproducibility of Results ,DNA Methylation ,Middle Aged ,Standard methods ,Optimal management ,3. Good health ,030104 developmental biology ,medicine.anatomical_structure ,Child, Preschool ,DNA methylation ,Female ,DNA microarray ,business ,030217 neurology & neurosurgery ,Unsupervised Machine Learning - Abstract
Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging - with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology. more...
- Published
- 2018
- Full Text
- View/download PDF