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Correlations between genomic subgroup and clinical features in a cohort of more than 3000 meningiomas

Mark Youngblood 1 Daniel Duran 2 Julio Montejo 3 Chang Li 4, 5 Sacit Bulent Omay 1 Koray Özduman 6 Amar Sheth 1 Amy Zhao 1 Evgeniya Tyrtova 1 Danielle Miyagishima 1 Elena Fomchenko 1 Christopher Hong 1 Victoria Clark 7 Maximilien Riche 8 Matthieu Peyre 8 Julien Boetto 9, 10 Sadaf Sohrabi 1 Sarah Koljaka 1 Jacob Baranoski 11 James Knight 1 Hongda Zhu 12 M. Necmettin Pamir 6 Timuçin Avşar 6 Türker Kilic 13 Johannes Schramm 14 Marco Timmer 15 Roland Goldbrunner 16, 15 Ye Gong 12 Yaşar Bayri Nduka Amankulor 17 Ronald Hamilton 18 Kaya Bilguvar 19, 20 Irina Tikhonova 21, 1 Patrick Tomak 1 Anita Huttner 1 Matthias Simon 22, 14 Boris Krischek 14 Michel Kalamarides 8 E. Zeynep Erson-Omay 1 Jennifer Moliterno 1 Murat Günel 1
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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Submitted on : Tuesday, April 21, 2020 - 12:33:58 PM
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Mark Youngblood, Daniel Duran, Julio Montejo, Chang Li, Sacit Bulent Omay, et al.. Correlations between genomic subgroup and clinical features in a cohort of more than 3000 meningiomas. Journal of Neurosurgery, American Association of Neurological Surgeons, 2019, pp.1-10. ⟨10.3171/2019.8.JNS191266⟩. ⟨hal-02549269⟩



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