Patrick Tomak, Murat Gunel, Maximilien Riche, Ye Gong, Christopher S. Hong, Turker Kilic, James R. Knight, Sadaf Sohrabi, Sarah Koljaka, Koray Özduman, E. Zeynep Erson-Omay, Yasar Bayri, Johannes Schramm, Jennifer Moliterno, Danielle F Miyagishima, Evgeniya Tyrtova, Roland Goldbrunner, Jacob F Baranoski, Daniel Duran, Anita Huttner, Victoria E. Clark, Hongda Zhu, Julien Boetto, Michel Kalamarides, Elena I Fomchenko, Nduka Amankulor, Amar H. Sheth, Mark W. Youngblood, M. Necmettin Pamir, Julio D Montejo, Timucin Avsar, Chang Li, Kaya Bilguvar, Marco Timmer, Ronald L. Hamilton, Amy Y Zhao, Matthieu Peyre, Boris Krischek, Matthias Simon, Sacit Bulent Omay, Irina Tikhonova, Yale University School of Medicine, University of Mississippi Medical Center (UMMC), Darmouth College [Hanover, New Hampshire], Huazhong University of Science and Technology [Wuhan] (HUST), Hunan University [Changsha] (HNU), Istanbul University, Massachusetts General Hospital [Boston], Service de Neurochirurgie [CHU Pitié-Salpêtrière], CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Service de Neurochirurgie [Montpellier], Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-CHU Gui de Chauliac [Montpellier], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Barrow Neurological Institute, Fudan University [Shanghai], Bahcesehir University [Istanbul], University Hospital Bonn, University Hospital of Cologne [Cologne], Surgical Department of the LMU Munich, University of Pittsburgh School of Medicine, Pennsylvania Commonwealth System of Higher Education (PCSHE), University of Pittsburgh (PITT), Department of Genetics, Yale University [New Haven], Institut de médecine moléculaire de Rangueil (I2MR), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-IFR150-Institut National de la Santé et de la Recherche Médicale (INSERM), and Universität Bielefeld
OBJECTIVERecent 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.METHODSTargeted 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.RESULTSGenomic 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.CONCLUSIONSUsing 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.