Table 3.
Molecular subgroups and descriptions of multiomic classes from different studies
| Study | Derivation of molecular group | Group | WHO Grades | Mutations | CNV | Transcriptional profile | Clinical outcome |
|---|---|---|---|---|---|---|---|
| Nassiri et al. | Unsupervised sample-wise consensus hierarchical clustering of gene level somatic copy number alterations, DNA methylome, and transcriptome data followed by COCA | MG1: Immunogenic | 1 | NF2 | 22q- | Immune regulation and signaling pathways | Good |
| MG2: NF2-wildtype | 1, 2 | TRAF7, AKT1, KLF4, POLR2A | 5p/q+, 12p/q+, 13p/q+, 17p/q+,20p/q+ | Vasculature and angiogenic pathways | Intermediate | ||
| MG3: Hypermetabolic | 2, 1 | NF2, DMD, TERTp, CHD2, CREBBP | 1p−, 6q−, 14p/q−, 22q− | Metabolism of macromolecules | Poor | ||
| MG4: Proliferative | 2, 3 | NF2, TERTp, KDM6A, CHD2, CREBBP, PTEN, RB1, FBXW7 | 1p−, 1q+, 6p/q−, 10p/q−, 14p/q−, 22q−, CDKN2A/B- | Cell cycle regulation, proliferation associated transcription factor networks (MYC, FOXM1, E2F) | Poor | ||
| Bayley et al. | NMF and k-means clustering based on most variably methylated CpGs, followed by PLS models with DNA methylation and CNV data as independent data, and RNA-seq as the dependent data | MenG A | 1 | TRAF7, KLF4, AKT1, PI3KCA, SMO, POLR2A, TERTp | None | Vasculature development and cell cycle signaling | Good |
| MenG B | 2, 1 | NF2, SMARCB1 | 22q- | Immune signaling and Hedgehog pathway | Good | ||
| MenG C | 2, 1 | NF2, ARID1A, TERTp | 1p-, 22q- | Metabolic and cell cycle pathways | Poor | ||
| Choudhury et al. | PCA of most variable probes in DNA methylation data followed by K-means consensus clustering, continuous distribution functions, and unsupervised hierarchical clustering | Merlin-intact | 1 | TRAF7, AKT1, KLF4 | 5p+, 6p- | NF2 expression | Good |
| Immune-enriched | 1, 2 | NF2 | 6p+, 22q- | HLA and meningeal lymphatic genes | Intermediate | ||
| Hypermitotic* | 2, 1, 3 | NF2 | 1p−/+, 6p−, 9p−, 14q−/+, 22q−, CDKN2A/B− | FOXM1 and other cell cycle pathways | Poor |
COCA cluster of cluster analysis; NMF non-negative matrix factorization; PLS partial least squares; PCA principal component analysis
*Subsequently found to contain two separate methylation subgroups, corresponding to hypermetabolic and proliferative subgroups with distinct clinical outcomes (Choudhury et al.)