Journal article
Correlations between genomic subgroup and clinical features in a cohort of more than 3000 meningiomas
Journal of neurosurgery, v 133(5), pp 1-10
01 Nov 2020
PMID: 31653806
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
OBJECTIVE:Recent large-cohort sequencing studies have investigated the genomic landscape of meningiomas, identifying somatic coding alterations in NF2, SMARCB1, SMARCE1, TRAF7, KLF4, POLR2A, BAP1, and members of the PI3K and Hedgehog signaling pathways. Initial associations between clinical features and genomic subgroups have been described, including location, grade, and histology. However, further investigation using an expanded collection of samples is needed to confirm previous findings, as well as elucidate relationships not evident in smaller discovery cohorts.METHODS:Targeted sequencing of established meningioma driver genes was performed on a multiinstitution cohort of 3016 meningiomas for classification into mutually exclusive subgroups. Relevant clinical information was collected for all available cases and correlated with genomic subgroup. Nominal variables were analyzed using Fisher's exact tests, while ordinal and continuous variables were assessed using Kruskal-Wallis and 1-way ANOVA tests, respectively. Machine-learning approaches were used to predict genomic subgroup based on noninvasive clinical features.RESULTS:Genomic subgroups were strongly associated with tumor locations, including correlation of HH tumors with midline location, and non-NF2 tumors in anterior skull base regions. NF2 meningiomas were significantly enriched in male patients, while KLF4 and POLR2A mutations were associated with female sex. Among histologies, the results confirmed previously identified relationships, and observed enrichment of microcystic features among "mutation unknown" samples. Additionally, KLF4-mutant meningiomas were associated with larger peritumoral brain edema, while SMARCB1 cases exhibited elevated Ki-67 index. Machine-learning methods revealed that observable, noninvasive patient features were largely predictive of each tumor's underlying driver mutation.CONCLUSIONS:Using a rigorous and comprehensive approach, this study expands previously described correlations between genomic drivers and clinical features, enhancing our understanding of meningioma pathogenesis, and laying further groundwork for the use of targeted therapies. Importantly, the authors found that noninvasive patient variables exhibited a moderate predictive value of underlying genomic subgroup, which could improve with additional training data. With continued development, this framework may enable selection of appropriate precision medications without the need for invasive sampling procedures.
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Details
- Title
- Correlations between genomic subgroup and clinical features in a cohort of more than 3000 meningiomas
- Creators
- Mark Youngblood - Yale UniversityDaniel Duran - University of Mississippi Medical CenterJulio Montejo - Dartmouth–Hitchcock Medical CenterChang Li - Central South UniversitySacit Bulent Omay - Yale UniversityKoray Özduman - Acıbadem Adana HospitalAmar Sheth - Yale UniversityAmy Zhao - Yale UniversityEvgeniya Tyrtova - Yale UniversityDanielle Miyagishima - Yale UniversityElena Fomchenko - Yale UniversityChristopher Hong - Yale UniversityVictoria Clark - Massachusetts General HospitalMaximilien Riche - Assistance Publique – Hôpitaux de ParisMatthieu Peyre - Sorbonne UniversitéJulien Boetto - Sorbonne UniversitéSadaf Sohrabi - Yale UniversitySarah Koljaka - Yale UniversityJacob Baranoski - St. Joseph's Hospital and Medical CenterJames Knight - Yale UniversityHongda Zhu - Huashan HospitalM. Necmettin Pamir - Kent HastanesiTimuçin Avşar - Istanbul UniversityTürker Kilic - Bahçeşehir UniversityJohannes Schramm - University of BonnMarco Timmer - University Hospital CologneRoland Goldbrunner - University Hospital CologneYe Gong - Huashan HospitalYaşar Bayri - Marmara UniversityNduka Amankulor - University of Pittsburgh Medical CenterRonald Hamilton - University of Pittsburgh Medical CenterKaya Bilguvar - Yale UniversityIrina Tikhonova - Yale UniversityPatrick Tomak - Yale UniversityAnita Huttner - Yale UniversityMatthias Simon - University of BonnBoris Krischek - University Hospital CologneMichel Kalamarides - Assistance Publique – Hôpitaux de ParisE. Zeynep Erson-Omay - Yale UniversityJennifer Moliterno - Yale UniversityMurat Günel - Yale University
- Publication Details
- Journal of neurosurgery, v 133(5), pp 1-10
- Publisher
- American Association of Neurological Surgeons
- Number of pages
- 10
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Neurology
- Web of Science ID
- WOS:000586105700009
- Scopus ID
- 2-s2.0-85095702457
- Other Identifier
- 991022004959204721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Clinical Neurology
- Surgery