A microenvironment-determined risk continuum refines subtyping in meningioma and reveals determinants of machine learning-based tumor classification

Classification of tumors in neuro-oncology today relies on molecular patterns (mostly DNA methylation) and their machine learning-supported interpretation. Understanding the process of algorithmic interpretation is essential for safe application in clinical routine. This is paradigmatically true for the most common primary intracranial tumor in adults, meningioma. Here, by applying multiomic profiling and multiple lines of orthogonal computational evaluation in multiple independent datasets, we found that not only tumor cell characteristics but also incremental changes in the tumor microenvironment (TME) have impact on epigenetic meningioma classification and clinical outcome. Besides revealing the decisive role of non-neoplastic cells in the CNS methylation classifier, this challenges the model of distinct meningioma subgroups toward a TME-determined risk continuum. This refines current controversies in molecular meningioma subtyping. In addition, we apply these learnings to devise and validate a simple diagnostic approach for increased clinical prediction accuracy based on immunohistochemistry, which is also applicable in resource-limited settings.

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Journal
Nature Genetics
Published
2026-02-01
DOI
https://doi.org/10.1038/s41588-025-02475-w
Citations
1
Primary Topic
Meningioma and schwannoma management
Type
article
Field-Weighted Citation Impact
31.52
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article

A microenvironment-determined risk continuum refines subtyping in meningioma and reveals determinants of machine learning-based tumor classification

Sybren L. N. Maas, Konstantin Okonechnikov, C. Mawrin, Eric Y. Stutheit-Zhao et al.
1 citations
Nature Genetics
Meningioma and schwannoma management
31.52
article

A microenvironment-determined risk continuum refines subtyping in meningioma and reveals determinants of machine learning-based tumor classification

Sybren L. N. Maas, Konstantin Okonechnikov, C. Mawrin, Eric Y. Stutheit-Zhao, A. Unterberg, Christina Blume, S. Masoud, M. Krieg Sandro, Martin Sill, Daniel Schrimpf, Felix J. Hartmann, G. Tabatabai, Salvatore Benfatto, Yahaya A Yabo, Yiheng Tang, Felix Behling, K Lamszus, M. Peyre, Till Milde, D. E. Reuss, Ferdinand Zettl, Jan‐Philipp Mallm, Gerhard Jungwirth, Rachel Grossmann, Eelke M. Bos, Areeba Patel, Mirco Friedrich, Miriam Ratliff, Thomas Hielscher, Hans‐Georg Wirsching, D Calafato, Katja Beck, Damian Stichel, Peter Lichter, Michel Kalamaridès, Josch K. Pauling, Philipp Sievers, Helin Kardo, Jan Baumbach, Nima Etminan, Abigail K. Suwala, Marc Remke, T P P van den Bosch, M Prinz, Matthias Schlesner, Olivier Ayrault, Matthias Preusser, Roman Sankowski, Andreas von Deimling, Michael Weller, Ramin Rahmanzade, Christel Herold-Mende, Jasim Kada Benotmane, David T. W. Jones, Felix Sahm, Wolfgang Wick, Michael Platten, Dieter Henrik Heiland, Volker Hovestadt, Till Acker, C. Oliver Hanemann, Marian C. Neidert, Zvi Ram, Markus List, Michael Ritter, Guido Reifenberger
article en
1 citations

Abstract

Classification of tumors in neuro-oncology today relies on molecular patterns (mostly DNA methylation) and their machine learning-supported interpretation. Understanding the process of algorithmic interpretation is essential for safe application in clinical routine. This is paradigmatically true for the most common primary intracranial tumor in adults, meningioma. Here, by applying multiomic profiling and multiple lines of orthogonal computational evaluation in multiple independent datasets, we found that not only tumor cell characteristics but also incremental changes in the tumor microenvironment (TME) have impact on epigenetic meningioma classification and clinical outcome. Besides revealing the decisive role of non-neoplastic cells in the CNS methylation classifier, this challenges the model of distinct meningioma subgroups toward a TME-determined risk continuum. This refines current controversies in molecular meningioma subtyping. In addition, we apply these learnings to devise and validate a simple diagnostic approach for increased clinical prediction accuracy based on immunohistochemistry, which is also applicable in resource-limited settings.

Nature GeneticsVol. 58(2)
Université Paris-Sud (FR), Broad Institute (US), Northwestern University (US), Boston Children's Hospital (US), Centre National de la Recherche Scientifique (FR), Inserm (FR), Universität Hamburg (DE), University of Freiburg (DE), Tel Aviv University (IL), University of Mannheim (DE), University of Southern Denmark (DK), University of Augsburg (DE), German Cancer Research Center (DE), Friedrich-Alexander-Universität Erlangen-Nürnberg (DE), University of Toronto (CA), Justus-Liebig-Universität Gießen (DE), University of Zurich (CH), Heidelberg University (DE), Université Paris Sciences et Lettres (FR), Université Paris-Saclay (FR), Tel Aviv Sourasky Medical Center (IL), Kantonsspital St. Gallen (CH), Leiden University Medical Center (NL), University Medical Center Freiburg (DE), Rambam Health Care Campus (IL), University Hospital Augsburg (DE), University Hospital Heidelberg (DE), Robert Bosch Hospital (DE), Sorbonne Université (FR), Universitätsklinikum Erlangen (DE), Düsseldorf University Hospital (DE), Assistance Publique – Hôpitaux de Paris (FR), Kantonsspital Winterthur (CH), University Hospital of Zurich (CH), Comprehensive Cancer Center Mainfranken (DE), University Medical Center Hamburg-Eppendorf (DE), National Center for Tumor Diseases (DE), Hopp Children's Cancer Center Heidelberg (DE), Dana-Farber Cancer Institute (US), Pitié-Salpêtrière Hospital (FR), University Children's Hospital Tübingen (DE), Hegau-Bodensee-Klinikum Singen (DE), University Hospital Magdeburg (DE), Comprehensive Cancer Center Erlangen (DE), Erasmus MC Cancer Institute (NL), Heidelberg Institute for Stem Cell Technology and Experimental Medicine (DE), Institut du Cerveau (FR), University Medical Centre Mannheim (DE), University Hospital Carl Gustav Carus (DE), Heinrich Heine University Düsseldorf (DE), Technical University of Munich (DE), Medical University of Vienna (AT), Friedrich Schiller University Jena (DE), University of Tübingen (DE), Ludwig-Maximilians-Universität München (DE), University of Plymouth (GB), Saarland University (DE), Technische Universität Braunschweig (DE)
Openalex Percentile: Top 1%
Meningioma and schwannoma management
31.52
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