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.

Background We and others have identified mutually-exclusive molecular subgroups of meningiomas, however, the implications of this classification for clinical prognostication remain unclear. Integrated genomic and epigenomic analyses implicate unique oncogenic processes associated with each subgroup, suggesting the potential for divergent clinical courses. The aim of this study was to understand the associated clinical outcomes of each subgroup, as this could optimize treatment for patients. Methods We analyzed outcome data for 469 meningiomas of known molecular subgroup, including extent-of-resection, post-operative radiation, surveillance imaging, and time to recurrence, when applicable. Statistical relationships between outcome variables and subgroup were assessed. Features previously associated with recurrence were further investigated after stratification by subgroup. We used Kaplan-Meier analyses to compare progression free survival, and identified factors significantly associated with recurrence using Cox proportional hazards modeling. Results Meningioma molecular subgroups exhibited divergent clinical courses at two years of follow-up, with several aggressive subgroups (NF2, PI3K, HH, TRAF7) recurring at an average rate of 22x higher than others (KLF4, POLR2A, SMARCB1). PI3K-activated tumors recurred earlier than other subgroups, but had intermediate long-term outcome. Among low-grade tumors, HH and TRAF7 meningiomas exhibited elevated recurrence compared to other subgroups. Recurrence of NF2 tumors was associated with male gender, high-grade, and elevated Ki-67. Multi-variate analysis identified molecular subgroup as an independent predictor of recurrence, along with grade and previous recurrence. Conclusion We describe distinct clinical outcomes and recurrence rates associated with meningioma molecular subgroups. Our findings emphasize the importance of genomic characterization to guide post-operative management decisions for meningiomas.

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