Search

INTRODUCTION Vestibular schwannomas (VS) are known to be slow-growing lesions that can present significant issues for patients. Here, we sought to better understand their outcomes and risks for recurrence. METHODS We retrospectively reviewed clinical outcomes data for patients who had VS tumor that underwent whole-exome sequencing after surgical resection between 2015 and 2018. The association between any two categorical variables was investigated using Fisher’s exact test. The mean of a continuous variable was compared between groups using either a two-tailed t-test or ANOVA. RESULTS Forty patients met our inclusion criteria. Interestingly, men were more likely than women to experience recurrence (p = 0.05). In accordance with previous research demonstrating a significant association between NF2 and bilateral vestibular schwannomas, most VS were NF2-mutants, with only four being were non-NF2. Pre-operative tumor size significantly correlated with progression-free survival time from surgical resection to recurrence, such that larger tumors were more likely to recur slower post-operatively (p = 0.043). There appeared to be no significant association between the extent of surgical resection (EOR) and recurrence (p = 0.653). In addition, there was no significant difference in post-operative complications or clinical outcomes, including EOR and facial nerve function, between different surgical approaches (p > 0.30). Patients with pre-operative intra-tumoral hemorrhage were more likely to have smaller tumors, suggesting they present more acutely (p = 0.119). Finally, patients with NF2-mutated VS had a longer time from original diagnosis to surgery than patients who had non-NF2 VS mutants (p = 0.041). CONCLUSIONS Surgery for VS offers good clinical outcomes overall with low recurrence rates, regardless of prior treatment. Though not related to Ki67, larger tumors appear to confer a slower growth potential and lower risk of recurrence over time.

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.