Subtopic Deep Dive
DNA Methylation Classification of Meningiomas
Research Guide
What is DNA Methylation Classification of Meningiomas?
DNA methylation classification of meningiomas uses genome-wide methylation arrays to stratify tumors into molecular subtypes with distinct clinical behaviors beyond WHO histological grading.
Felix Sahm et al. (2017) developed a methylation-based classifier for meningiomas in a multicentre retrospective analysis (808 citations). Farshad Nassiri et al. (2021) proposed an integrative molecular classification incorporating methylation profiles (412 citations). These classifiers correlate methylation patterns with recurrence risk and progression (Nassiri et al., 2019, 253 citations).
Why It Matters
Methylation classifiers improve prognostic accuracy for meningiomas, identifying aggressive subtypes missed by WHO grading (Sahm et al., 2017). Nassiri et al. (2019) validated a nomogram using methylation to predict recurrence, optimizing adjuvant radiotherapy selection. This enables personalized management, reducing overtreatment in low-risk cases and intensifying therapy for high-risk tumors (Nassiri et al., 2021). EANO guidelines now recommend molecular testing including methylation profiling (Goldbrunner et al., 2021).
Key Research Challenges
Clinical Validation Across Cohorts
Methylation classifiers require multicentre validation to ensure reproducibility across diverse populations (Sahm et al., 2017). Variability in array platforms and tissue processing affects profile consistency. Nassiri et al. (2021) addressed this through integrative models but larger prospective trials are needed.
Integration with Histology and Genetics
Combining methylation data with WHO grading and mutations like NF2 or CDKN2A remains challenging (Boström et al., 2001). Current classifiers underperform in atypical meningiomas with heterogeneous profiles. Nassiri et al. (2019) developed nomograms but full multimodal integration is incomplete.
Recurrence Prediction in Low-Grade Tumors
Predicting progression in WHO grade 1 meningiomas using methylation is limited by low event rates (Wiemels et al., 2010). Nomograms improve risk stratification but lack prospective confirmation (Nassiri et al., 2019). Standardization of cutoffs for clinical use is unresolved.
Essential Papers
The WHO Classification of Tumors of the Nervous System
Paul Kleihues, David N. Louis, Bernd W. Scheithauer et al. · 2002 · Journal of Neuropathology & Experimental Neurology · 1.9K citations
The new World Health Organization (WHO) classification of nervous system tumors, published in 2000, emerged from a 1999 international consensus conference of neuropathologists. New entities include...
Epidemiology and etiology of meningioma
Joseph L. Wiemels, Margaret Wrensch, Elizabeth B. Claus · 2010 · Journal of Neuro-Oncology · 1.2K citations
Although most meningiomas are encapsulated and benign tumors with limited numbers of genetic aberrations, their intracranial location often leads to serious and potentially lethal consequences. The...
DNA methylation-based classification and grading system for meningioma: a multicentre, retrospective analysis
Felix Sahm, Daniel Schrimpf, Damian Stichel et al. · 2017 · The Lancet Oncology · 808 citations
EANO guideline on the diagnosis and management of meningiomas
Roland Goldbrunner, Pantelis Stavrinou, Michael D. Jenkinson et al. · 2021 · Neuro-Oncology · 635 citations
Abstract Meningiomas are the most common intracranial tumors. Yet, only few controlled clinical trials have been conducted to guide clinical decision making, resulting in variations of management a...
Neurofibromatosis type 2 (NF2): A clinical and molecular review
D. Gareth Evans · 2009 · Orphanet Journal of Rare Diseases · 535 citations
Neurofibromatosis type 2 (NF2) is a tumour-prone disorder characterised by the development of multiple schwannomas and meningiomas. Prevalence (initially estimated at 1: 200,000) is around 1 in 60,...
A clinically applicable integrative molecular classification of meningiomas
Farshad Nassiri, Jeff Liu, Vikas Patil et al. · 2021 · Nature · 412 citations
Alterations of the Tumor Suppressor Genes CDKN2A (p16), p14, CDKN2B (p15), and CDKN2C (p18) in Atypical and Anaplastic Meningiomas
Jan Boström, Birgit Meyer‐Puttlitz, Marietta Wolter et al. · 2001 · American Journal Of Pathology · 268 citations
Reading Guide
Foundational Papers
Start with Sahm et al. (2017) for the first methylation classifier (808 citations), then WHO 2002 (Kleihues et al., 1949 citations) for histological context, and Evans (2009) for NF2-meningioma links.
Recent Advances
Nassiri et al. (2021, Nature, 412 citations) for integrative classification. Goldbrunner et al. (2021) EANO guidelines incorporating methylation. Torp et al. (2022) on WHO 2021 updates.
Core Methods
Genome-wide methylation arrays (850k BeadChip). Unsupervised clustering and supervised random forest classifiers. Nomogram construction via Cox regression (Nassiri et al., 2019).
How PapersFlow Helps You Research DNA Methylation Classification of Meningiomas
Discover & Search
Research Agent uses searchPapers and exaSearch to find key papers like 'DNA methylation-based classification... meningioma' by Sahm et al. (2017), then citationGraph reveals 808 citing works including Nassiri et al. (2021). findSimilarPapers expands to related classifiers from 250M+ OpenAlex papers.
Analyze & Verify
Analysis Agent applies readPaperContent to extract methylation signatures from Sahm et al. (2017), then runPythonAnalysis with pandas to compare profiles across cohorts. verifyResponse (CoVe) and GRADE grading verify recurrence predictions against Nassiri et al. (2019) nomogram data.
Synthesize & Write
Synthesis Agent detects gaps in methylation-histology integration from Sahm (2017) and Nassiri (2021), flags contradictions with WHO 2021 updates (Torp et al., 2022). Writing Agent uses latexEditText, latexSyncCitations for classifiers review, latexCompile for publication-ready manuscript with exportMermaid for subtype diagrams.
Use Cases
"Analyze methylation data from Sahm 2017 to predict meningioma recurrence using Python."
Research Agent → searchPapers(Sahm 2017) → Analysis Agent → readPaperContent → runPythonAnalysis(pandas survival curves on methylation profiles) → statistical output with Kaplan-Meier plots.
"Write LaTeX review on DNA methylation classifiers for meningiomas citing Nassiri 2021."
Synthesis Agent → gap detection(Nassiri 2021) → Writing Agent → latexEditText(review draft) → latexSyncCitations(12 papers) → latexCompile → PDF with integrated figures.
"Find GitHub code for meningioma methylation classifiers."
Research Agent → searchPapers(Nassiri 2019) → Code Discovery → paperExtractUrls → paperFindGithubRepo → githubRepoInspect → R scripts for nomogram implementation.
Automated Workflows
Deep Research workflow conducts systematic review: searchPapers(50+ meningioma methylation) → citationGraph → structured report on classifiers (Sahm 2017 baseline). DeepScan applies 7-step analysis: readPaperContent(Nassiri 2021) → runPythonAnalysis → CoVe checkpoints → GRADE-scored summary. Theorizer generates hypotheses linking methylation subtypes to NF2 mutations (Evans 2009).
Frequently Asked Questions
What is DNA methylation classification of meningiomas?
It stratifies meningiomas into molecular subtypes using 850k methylation arrays, identifying 5-6 classes with distinct prognoses (Sahm et al., 2017).
What are the main methods used?
Random forest classifiers trained on genome-wide methylation data, validated in multicentre cohorts (Sahm et al., 2017). Nomograms integrate methylation scores with clinical variables (Nassiri et al., 2019).
What are the key papers?
Sahm et al. (2017, The Lancet Oncology, 808 citations) introduced the classifier. Nassiri et al. (2021, Nature, 412 citations) developed integrative classification. Nassiri et al. (2019) added recurrence nomogram.
What open problems remain?
Prospective validation of classifiers in randomized trials. Standardization of methylation platforms for routine diagnostics. Integration with TERT promoter mutations and transcriptomics.
Research Meningioma and schwannoma management with AI
PapersFlow provides specialized AI tools for Medicine researchers. Here are the most relevant for this topic:
Systematic Review
AI-powered evidence synthesis with documented search strategies
AI Literature Review
Automate paper discovery and synthesis across 474M+ papers
Find Disagreement
Discover conflicting findings and counter-evidence
Paper Summarizer
Get structured summaries of any paper in seconds
See how researchers in Health & Medicine use PapersFlow
Field-specific workflows, example queries, and use cases.
Start Researching DNA Methylation Classification of Meningiomas with AI
Search 474M+ papers, run AI-powered literature reviews, and write with integrated citations — all in one workspace.
See how PapersFlow works for Medicine researchers