1. A clinically applicable integrative molecular classification of meningiomas
- Author
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Vikas Patil, Rupert Hugh-White, Thomas Kislinger, Bharati Mehani, Kenneth Aldape, Daniel D. De Carvalho, Caroline Y. Chen, Andrew Macklin, Ankur Chakravarthy, Qingxia Wei, Gary D. Bader, Lydia Y Liu, Rosario I. Corona, Paul C. Boutros, Adriana M. Workewych, Justin Z. Wang, Suganth Suppiah, Jeffrey C Liu, Yasin Mamatjan, Ghazaleh Tabatabai, Shirin Karimi, Farshad Nassiri, Andrew Gao, Gelareh Zadeh, Olivia Singh, and Shahbaz Khan
- Subjects
Mutation ,Multidisciplinary ,business.industry ,Point mutation ,Disease ,medicine.disease ,Proteogenomics ,Bioinformatics ,medicine.disease_cause ,Meningioma ,DNA methylation ,medicine ,Immunohistochemistry ,Cancer epigenetics ,business - Abstract
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. Multi-omics datasets are integrated to generate a unified and clinically informed molecular classification of meningiomas.
- Published
- 2021
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