Evidence map›Paper›PMID 41466325›Full record

ArticleActa neuropathologica communications2025

Development, validation, and utility of a clinically applicable methylation classifier for recurrence risk prediction in meningiomas.

Drew Duckett, Lucas Santana-Santos, Matthew McCord, Vanessa Smith, M Beatriz S Lopes, Mark W Youngblood, Stephen T Magill, James P Chandler, Amy B Heimberger, Rimas Lukas and 9 more

Abstract readValidation Study
In one paragraph

Article in Acta neuropathologica communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

Drew Duckett *Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Lucas Santana-Santos *Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Matthew McCord *Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Vanessa SmithDepartment of Pathology, University of Virginia School of Medicine, Charlottesville, VA, USA.
M Beatriz S LopesDepartment of Pathology, University of Virginia School of Medicine, Charlottesville, VA, USA.
Mark W YoungbloodDepartment of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Stephen T MagillDepartment of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
James P ChandlerDepartment of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Amy B HeimbergerDepartment of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Rimas LukasDepartment of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Jared T AhrendsenDepartment of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Ditte PrimdahlDepartment of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Roger StuppDepartment of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Rudolph J CastellaniDepartment of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Craig M HorbinskiDepartment of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Daniel J BratDepartment of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Lawrence J JenningsDepartment of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Madina SukhanovaDepartment of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA. madina.sukhanova@northwestern.edu.
Pouya JamshidiDepartment of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA. pouya.jamshidi@northwestern.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Meningiomas are common intracranial tumors with complex behavior that can be difficult to predict. Historically, morphology has been used to predict tumor aggressiveness and risk of recurrence, but this strategy has limitations as a prognostic tool. DNA methylation, transcriptomics, and copy number data are valuable for identifying groups of tumors with distinct biological signatures, thereby predicting recurrence risk. Multiple risk-stratifying classifiers which incorporate methylation data are available, but to date, a clinically validated risk-predicting classifier which exclusively uses methylation data has not been created. Using samples from 217 patients, we developed, validated, and implemented a clinically applicable methylation classifier for prognostic stratification of meningiomas based on k-means clustering of methylation data. Our classifier is 96% accurate, with 91% of samples receiving high confidence scores in the validation cohort (n = 76). This classifier is unique in that it includes de novo identification of risk groups by DNA methylation, confidence score calculation, internal clinical validation, and public model availability. Our newly validated classifier has the potential to aid diagnostic workup, improve recurrence risk prediction, and enhance clinical management of meningiomas.

Indexed as

DNA MethylationMeningeal NeoplasmsMeningiomaNeoplasm Recurrence, LocalAdultAgedFemaleHumansMaleMiddle AgedPrognosisClinical validationDNA methylationK-means clusteringMeningiomaPrognosis

Identifiers

PMID41466325
PMCPMC12750935

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.