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The Meningioma Enhancer Landscape Delineates Novel Subgroups and Drives Druggable Dependencies.
- Source :
-
Cancer discovery [Cancer Discov] 2020 Nov; Vol. 10 (11), pp. 1722-1741. Date of Electronic Publication: 2020 Jul 23. - Publication Year :
- 2020
-
Abstract
- Meningiomas are the most common primary intracranial tumor with current classification offering limited therapeutic guidance. Here, we interrogated meningioma enhancer landscapes from 33 tumors to stratify patients based upon prognosis and identify novel meningioma-specific dependencies. Enhancers robustly stratified meningiomas into three biologically distinct groups (adipogenesis/cholesterol, mesodermal, and neural crest) distinguished by distinct hormonal lineage transcriptional regulators. Meningioma landscapes clustered with intrinsic brain tumors and hormonally responsive systemic cancers with meningioma subgroups, reflecting progesterone or androgen hormonal signaling. Enhancer classification identified a subset of tumors with poor prognosis, irrespective of histologic grading. Superenhancer signatures predicted drug dependencies with superior in vitro efficacy to treatment based upon the NF2 genomic profile. Inhibition of DUSP1, a novel and druggable meningioma target, impaired tumor growth in vivo . Collectively, epigenetic landscapes empower meningioma classification and identification of novel therapies. SIGNIFICANCE: Enhancer landscapes inform prognostic classification of aggressive meningiomas, identifying tumors at high risk of recurrence, and reveal previously unknown therapeutic targets. Druggable dependencies discovered through epigenetic profiling potentially guide treatment of intractable meningiomas. This article is highlighted in the In This Issue feature, p. 1611 .<br /> (©2020 American Association for Cancer Research.)
- Subjects :
- Humans
Meningioma pathology
Prognosis
Epigenomics methods
Meningioma genetics
Subjects
Details
- Language :
- English
- ISSN :
- 2159-8290
- Volume :
- 10
- Issue :
- 11
- Database :
- MEDLINE
- Journal :
- Cancer discovery
- Publication Type :
- Academic Journal
- Accession number :
- 32703768
- Full Text :
- https://doi.org/10.1158/2159-8290.CD-20-0160