Choudhury/Raleigh UCSF meningioma multi-omic cohort
This UCSF cohort (Choudhury, Magill, Raleigh et al., Nature Genetics 2022, PMID 35534562) is the most heavily cross-linked dataset in this registry: 565 meningiomas profiled by DNA methylation array, 185 of the same tumors additionally bulk RNA-sequenced, and a 10-sample subset (6 patients, 57,114 cells, including matched dura and brain-tumor-interface pieces) single-cell RNA-sequenced. The paper established the widely-used three-group DNA methylation classification of meningioma (Merlin-intact, immune-enriched, hypermitotic) that several later datasets in this registry explicitly reference or build on. Strengths: large methylation cohort, real multi-modal matching (methylation + bulk RNA-seq + scRNA-seq on overlapping samples, a rare combination), open access with processed data files available. Limitations: per-sample clinical fields are only partially extracted so far. The bulk RNA-seq set has a per-sample grade breakdown (86 grade 1, 74 grade 2, 25 grade 3) and per-sample age, and the scRNA-seq set has per-sample age and a brain-tumor-interface sample count, but sex distribution remains unreported for all three records, and the 565-sample methylation series itself has no per-sample clinical fields extracted yet. Five other publications are also linked to this GEO SuperSeries family, suggesting a productive, still-active dataset with secondary reuse.
Overview
- Modality
- Bulk RNA-seq
- Sample count
- 185
- Patient count
- 185
- Institution
- University of California, San Francisco
- Corresponding author
- David R Raleigh / Abrar Choudhury
- Platform
- Illumina HiSeq 4000 (GPL20301)
- Access type
- open
- Tissue preservation
- Not reported
- WHO edition
- Not reported
Cohort detail
- Grade breakdown
- G1: 86 G2: 74 G3: 25
- Sex distribution
- Not reported
- Age distribution
- n=185, range 11.2-83.0, mean 55.2 (extracted from GSM Sample_characteristics_ch1: age)
- Anatomic location
- Not reported
- Brain invasion
- Not reported
- Normal/control tissue
- None
Priority attributes
Publications
- Choudhury A, et al. Meningioma DNA methylation groups identify biological drivers and therapeutic vulnerabilities. Nature Genetics, 2022. PMID 35534562 · DOI
- Vasudevan HN, et al. Intratumor and informatic heterogeneity influence meningioma molecular classification. Acta neuropathologica, 2022. PMID 35759011 · DOI
- Nguyen MP, et al. Supervised machine learning algorithms demonstrate proliferation index correlates with long-term recurrence after complete resection of WHO grade I meningioma. Journal of neurosurgery, 2023. PMID 36303473 · DOI
- Zakimi N, et al. Gene transcript fusions are associated with clinical outcomes and molecular groups of meningiomas. Acta neuropathologica, 2024. PMID 38509407 · DOI
- Mirchia K, et al. Meningeal solitary fibrous tumor cell states phenocopy cerebral vascular development and homeostasis. Neuro-oncology, 2025. PMID 39207122 · DOI
- Nguyen MP, et al. Pan-cancer copy number analysis identifies optimized size thresholds and co-occurrence models for individualized risk stratification. Nature communications, 2025. PMID 40603285 · DOI
Related datasets
- Meningioma DNA methylation grouping reveals biologic drivers and therapeutic vulnerabilities (array) →
- Single-cell RNA sequencing of meningiomas →
Also related: GSE183656, PRJNA761580, SRP336137
Sources
- GEOhttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE183653
- PubMedhttps://pubmed.ncbi.nlm.nih.gov/35534562/
- PubMedhttps://pubmed.ncbi.nlm.nih.gov/35759011/
- PubMedhttps://pubmed.ncbi.nlm.nih.gov/36303473/
- PubMedhttps://pubmed.ncbi.nlm.nih.gov/38509407/
- PubMedhttps://pubmed.ncbi.nlm.nih.gov/39207122/
- PubMedhttps://pubmed.ncbi.nlm.nih.gov/40603285/
- DOIhttps://doi.org/10.1038/s41588-022-01061-8
- ncbi.nlm.nih.govhttps://www.ncbi.nlm.nih.gov/bioproject/PRJNA761580