A clinically applicable integrative molecular classification of meningiomas

脑膜瘤 体细胞 DNA甲基化 分子病理学 免疫组织化学 DNA测序 DNA 疾病 甲基化 生物信息学 病理 生物 基因 计算生物学 医学 癌症研究 基因表达 遗传学
作者
Farshad Nassiri,Jeff Liu,Vikas Patil,Yasin Mamatjan,Justin Z. Wang,Rupert Hugh-White,Andrew Macklin,Shahbaz Khan,Olivia Singh,Shirin Karimi,Rosario I. Corona,Lydia Liu,Caroline Y. Chen,Ankur Chakravarthy,Qingxia Wei,Bharati Mehani,Suganth Suppiah,Andrew Gao,Adriana M Workewych,Ghazaleh Tabatabai,Paul C. Boutros,Gary D. Bader,Daniel D. De Carvalho,Thomas Kislinger,Kenneth Aldape,Gelareh Zadeh
出处
期刊:Nature [Springer Nature]
卷期号:597 (7874): 119-125 被引量:219
标识
DOI:10.1038/s41586-021-03850-3
摘要

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.
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