EPCO-52. MACHINE LEARNING AND MULTI-OMIC ANALYSIS IDENTIFY A MICROENVIRONMENT-DRIVEN MENINGIOMA RISK CONTINUUM UNDERLYING MOLECULAR CLASSIFICATIONS

Abstract Machine learning-based molecular classifications, particularly those using DNA methylation data, have greatly advanced diagnostics for meningioma, the most common type of primary intracranial tumor. Meningiomas have historically been classified into NF2-mutant and NF2-wild-type groups, while additional mutations and copy-number variations associated with progression risk have been incorporated into WHO grading. Several genome-wide methylation-based classification systems have been proposed. The systems, such as the random forest Brain Tumour Classifier, have been incorporated into diagnostic guidelines. However, while a number of core archetypes are shared among the different classifications, discrepancies on the definition and granularity of subtypes remain an obstacle to their clinical application. Understanding the underlying heterogeneity driving these classifications is therefore crucial. Through an integrated analysis of single-nuclei and spatially resolved transcriptomic data, as well as DNA methylation array data from multiple meningioma cohorts, we identified cell types and epigenetic signatures that are associated with increased aggressiveness in meningiomas. The results demonstrated that incremental changes in the tumor microenvironment (TME), particularly shifts in compositions and epigenetic-transcriptomic signatures in tumor-associated monocytes/macrophages and microglia-like cells, have a decisive impact on epigenetic classifications alongside tumor cells, and significantly affect clinical outcome. Therefore, we refine the previously proposed distinct molecular subtypes with a TME-determined risk continuum model for NF2-mutant meningiomas. Based on these discoveries, we additionally designed an immunohistochemistry-based diagnostic approach, which also captures intra-tumoral heterogeneities.

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Publication Details

Journal
Neuro-Oncology
Published
2025-11-01
DOI
https://doi.org/10.1093/neuonc/noaf201.0051
Primary Topic
Meningioma and schwannoma management
Type
article
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article

EPCO-52. MACHINE LEARNING AND MULTI-OMIC ANALYSIS IDENTIFY A MICROENVIRONMENT-DRIVEN MENINGIOMA RISK CONTINUUM UNDERLYING MOLECULAR CLASSIFICATIONS

Sybren L. N. Maas, Moritz Gerstung, Konstantin Okonechnikov, Sandro M. Krieg et al.
Neuro-Oncology
Meningioma and schwannoma management
article

EPCO-52. MACHINE LEARNING AND MULTI-OMIC ANALYSIS IDENTIFY A MICROENVIRONMENT-DRIVEN MENINGIOMA RISK CONTINUUM UNDERLYING MOLECULAR CLASSIFICATIONS

Sybren L. N. Maas, Moritz Gerstung, Konstantin Okonechnikov, Sandro M. Krieg, Roman Sankowski, Ramin Rahmanzade, Andreas Unterberg, Till Acker, Michael Weller, Katrin Lamszus, Marco Prinz, Christel C. Herold‐Mende, Jasim Kada Benotmane, Felix J. Hartmann, Christian Mawrin, David Jones, Salvatore Benfatto, Stefan M. Pfister, Yahaya A Yabo, Yiheng Tang, Marian Christoph Neidert, Philipp Sievers, Matthieu Peyre, Andreas von Deimling, Eric Stutheit-Zhao, David Reuß, Matthias Preusser, Ferdinand Zettl, Oliver Hanemann, Felix Sahm, Dieter Henrik Heiland, Matthias Schlesner, Zvi Ram, Volker Hovestadt, Rachel Grossmann, Eelke M. Bos, Wolfgang Wick, Areeba Patel, Miriam Ratliff, Thomas Hielscher, Nima Etminan, Martin Sill, Ghazaleh Tabatabai, D Calafato, Michel Kalamaridès
article en

Abstract

Abstract Machine learning-based molecular classifications, particularly those using DNA methylation data, have greatly advanced diagnostics for meningioma, the most common type of primary intracranial tumor. Meningiomas have historically been classified into NF2-mutant and NF2-wild-type groups, while additional mutations and copy-number variations associated with progression risk have been incorporated into WHO grading. Several genome-wide methylation-based classification systems have been proposed. The systems, such as the random forest Brain Tumour Classifier, have been incorporated into diagnostic guidelines. However, while a number of core archetypes are shared among the different classifications, discrepancies on the definition and granularity of subtypes remain an obstacle to their clinical application. Understanding the underlying heterogeneity driving these classifications is therefore crucial. Through an integrated analysis of single-nuclei and spatially resolved transcriptomic data, as well as DNA methylation array data from multiple meningioma cohorts, we identified cell types and epigenetic signatures that are associated with increased aggressiveness in meningiomas. The results demonstrated that incremental changes in the tumor microenvironment (TME), particularly shifts in compositions and epigenetic-transcriptomic signatures in tumor-associated monocytes/macrophages and microglia-like cells, have a decisive impact on epigenetic classifications alongside tumor cells, and significantly affect clinical outcome. Therefore, we refine the previously proposed distinct molecular subtypes with a TME-determined risk continuum model for NF2-mutant meningiomas. Based on these discoveries, we additionally designed an immunohistochemistry-based diagnostic approach, which also captures intra-tumoral heterogeneities.

Neuro-OncologyVol. 27(Supplement_5)
Boston University (US), Boston Children's Hospital (US), Universität Hamburg (DE), University of Freiburg (DE), Tel Aviv University (IL), University of Augsburg (DE), German Cancer Research Center (DE), Friedrich-Alexander-Universität Erlangen-Nürnberg (DE), University of Toronto (CA), Justus-Liebig-Universität Gießen (DE), Heidelberg University (DE), 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 Heidelberg (DE), Sorbonne Université (FR), University Hospital of Zurich (CH), 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), University Hospital Magdeburg (DE), Erasmus MC Cancer Institute (NL), Boston Children's Museum (US), University Medical Centre Mannheim (DE), Medical University of Vienna (AT), University of Tübingen (DE), University of Plymouth (GB)
Openalex Percentile: Top 55%
Meningioma and schwannoma management
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EPCO-52. MACHINE LEARNING AND MULTI-OMIC ANALYSIS IDENTIFY A MICROENVIRONMENT-DRIVEN MENINGIOMA RISK CONTINUUM UNDERLYING MOLECULAR CLASSIFICATIONS — Sybren L. N. Maas, Moritz Gerstung, et al. · Neuro-Oncology (2025) | TGRS Research Map | TGRS