ArticleNature genetics2026
A microenvironment-determined risk continuum refines subtyping in meningioma and reveals determinants of machine learning-based tumor classification.
Article in Nature genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Immune-Enriched Versus Matrix-Remodeling Microenvironments in Psammoma Body-Rich and Psammoma Body-Poor WHO Grade I Meningiomas: An Exploratory Transcriptomic Study.Neuropathology : official journal of the Japanese Society of Neuropathology · 2026Article
- Modeling meningioma in vitro in the omics era.Human cell · 2026Review
- DNA methylation profiling identifies long-range epigenetic silencing of clustered protocadherins as a key determinant of meningioma progression.Nature communications · 2026Article
- Distinct molecular subgroups in pediatric and young-onset meningiomas require age-adapted risk stratification.Nature communications · 2026Article
- Synthetic MRI and Delta Histogram Analysis: A Step Forward in Noninvasive Meningioma Grading.Radiology. Imaging cancer · 2026Article
- Risk stratification for meningiomas with discordant molecular classification.Acta neuropathologica · 2026Article
- A proteogenomic RNA processing mechanism drives sex differences in meningioma.Research square · 2026Article
- Loss of meningothelial identity and mesenchymal fate switching in NF2-mutant meningiomas.Acta neuropathologica · 2026Article
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69 authors.
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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.
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