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DOI 10.1038/s41588-025-02475-w

Медицинская библиотека · Nature genetics · 2026

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

Maas Sybren L N, Tang Yiheng, Stutheit-Zhao Eric, Rahmanzade Ramin, Blume Christina, Hielscher Thomas, Zettl Ferdinand, Benfatto Salvatore, Calafato Domenico, Sill Martin, Benotmane Jasim Kada, Yabo Yahaya A, Behling Felix, Suwala Abigail, Kardo Helin, Ritter Michael, Peyre Matthieu, Sankowski Roman, Okonechnikov Konstantin, Sievers Philipp, Patel Areeba, Reuss David, Friedrich Mirco J, Stichel Damian, Schrimpf Daniel, Van den Bosch Thierry P P, Beck Katja, Wirsching Hans-Georg, Jungwirth Gerhard, Hanemann C Oliver, Lamszus Katrin, Etminan Nima, Unterberg Andreas, Mawrin Christian, Remke Marc, Ayrault Olivier, Lichter Peter, Reifenberger Guido, Platten Michael, Kacprowski Tim, List Markus, Pauling Josch K, Baumbach Jan, Milde Till, Grossmann Rachel, Ram Zvi, Ratliff Miriam, Mallm Jan-Philipp, Neidert Marian C, Bos Eelke M, Prinz Marco, Weller Michael, Acker Till, Hartmann Felix J, Preusser Matthias, Tabatabai Ghazaleh, Herold-Mende Christel, Krieg Sandro M, Jones David T W, Pfister Stefan M, Wick Wolfgang, Kalamarides Michel, von Deimling Andreas, Heiland Dieter Henrik, Hovestadt Volker, Gerstung Moritz, Schlesner Matthias, German “Aggressive Meningiomas” Consortium (KAM), Sahm Felix

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Аннотация

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.

Для цитирования: Maas Sybren L N, Tang Yiheng, Stutheit-Zhao Eric, Rahmanzade Ramin, Blume Christina, Hielscher Thomas, Zettl Ferdinand, Benfatto Salvatore, Calafato Domenico, Sill Martin, Benotmane Jasim Kada, Yabo Yahaya A, Behling Felix, Suwala Abigail, Kardo Helin, Ritter Michael, Peyre Matthieu, Sankowski Roman, Okonechnikov Konstantin, Sievers Philipp, Patel Areeba, Reuss David, Friedrich Mirco J, Stichel Damian, Schrimpf Daniel, Van den Bosch Thierry P P, Beck Katja, Wirsching Hans-Georg, Jungwirth Gerhard, Hanemann C Oliver, Lamszus Katrin, Etminan Nima, Unterberg Andreas, Mawrin Christian, Remke Marc, Ayrault Olivier, Lichter Peter, Reifenberger Guido, Platten Michael, Kacprowski Tim, List Markus, Pauling Josch K, Baumbach Jan, Milde Till, Grossmann Rachel, Ram Zvi, Ratliff Miriam, Mallm Jan-Philipp, Neidert Marian C, Bos Eelke M, Prinz Marco, Weller Michael, Acker Till, Hartmann Felix J, Preusser Matthias, Tabatabai Ghazaleh, Herold-Mende Christel, Krieg Sandro M, Jones David T W, Pfister Stefan M, Wick Wolfgang, Kalamarides Michel, von Deimling Andreas, Heiland Dieter Henrik, Hovestadt Volker, Gerstung Moritz, Schlesner Matthias, German “Aggressive Meningiomas” Consortium (KAM), Sahm Felix. A microenvironment-determined risk continuum refines subtyping in meningioma and reveals determinants of machine learning-based tumor classification.. Nature genetics. 2026. DOI: 10.1038/s41588-025-02475-w.

Предметные рубрики MeSH

HumansMeningiomaTumor MicroenvironmentMachine LearningMeningeal NeoplasmsDNA MethylationGene Expression ProfilingClassification Algorithms
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