A clinically applicable integrative molecular classification of meningiomas

Nature. 2021 Sep;597(7874):119-125. doi: 10.1038/s41586-021-03850-3. Epub 2021 Aug 25.


Meningiomas are the most common primary intracranial tumour in adults1. Patients with symptoms are generally treated with surgery as there are no effective medical therapies. The World Health Organization histopathological grade of the tumour and the extent of resection at surgery (Simpson grade) are associated with the recurrence of disease; however, they do not accurately reflect the clinical behaviour of all meningiomas2. Molecular classifications of meningioma that reliably reflect tumour behaviour and inform on therapies are required. Here we introduce four consensus molecular groups of meningioma by combining DNA somatic copy-number aberrations, DNA somatic point mutations, DNA methylation and messenger RNA abundance in a unified analysis. These molecular groups more accurately predicted clinical outcomes compared with existing classification schemes. Each molecular group showed distinctive and prototypical biology (immunogenic, benign NF2 wild-type, hypermetabolic and proliferative) that informed therapeutic options. Proteogenomic characterization reinforced the robustness of the newly defined molecular groups and uncovered highly abundant and group-specific protein targets that we validated using immunohistochemistry. Single-cell RNA sequencing revealed inter-individual variations in meningioma as well as variations in intrinsic expression programs in neoplastic cells that mirrored the biology of the molecular groups identified.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Biomarkers, Tumor / metabolism*
  • DNA Methylation
  • Data Analysis
  • Drug Discovery
  • Female
  • Gene Expression Regulation, Neoplastic
  • Humans
  • Immunohistochemistry
  • Male
  • Meningioma / classification*
  • Meningioma / drug therapy
  • Meningioma / genetics
  • Meningioma / metabolism*
  • Mutation
  • Proteogenomics*
  • RNA-Seq
  • Reproducibility of Results
  • Single-Cell Analysis


  • Biomarkers, Tumor