Vein of Galen malformations (VOGMs) are rare developmental cerebrovascular lesions characterized by fistulas between the choroidal circulation and the median prosencephalic vein. Although the treatment of VOGMs has greatly benefited from advances in endovascular therapy, including technical innovation in interventional neuroradiology, many patients are recalcitrant to procedural intervention or lack accessibility to specialized care centers, highlighting the need for improved screening, diagnostics, and therapeutics. A fundamental obstacle to identifying novel targets is the limited understanding of VOGM molecular pathophysiology, including its human genetics, and the lack of an adequate VOGM animal model. Herein, the known human mutations associated with VOGMs are reviewed to provide a framework for future gene discovery. Gene mutations have been identified in 2 Mendelian syndromes of which VOGM is an infrequent but associated phenotype: capillary malformation–arteriovenous malformation syndrome (RASA1) and hereditary hemorrhagic telangiectasia (ENG and ACVRL1). However, these mutations probably represent only a small fraction of all VOGM cases. Traditional genetic approaches have been limited in their ability to identify additional causative genes for VOGM because kindreds are rare, limited in patient number, and/or seem to have sporadic inheritance patterns, attributable in part to incomplete penetrance and phenotypic variability. The authors hypothesize that the apparent sporadic occurrence of VOGM may frequently be attributable to de novo mutation or incomplete penetrance of rare transmitted variants. Collaboration among treating physicians, patients’ families, and investigators using next-generation sequencing could lead to the discovery of novel genes for VOGM. This could improve the understanding of normal vascular biology, elucidate the pathogenesis of VOGM and possibly other more common arteriovenous malformation subtypes, and pave the way for advances in the diagnosis and treatment of patients with VOGM.
Daniel Duran, Philipp Karschnia, Jonathan R. Gaillard, Jason K. Karimy, Mark W. Youngblood, Michael L. DiLuna, Charles C. Matouk, Beverly Aagaard-Kienitz, Edward R. Smith, Darren B. Orbach, Georges Rodesch, Alejandro Berenstein, Murat Gunel, and Kristopher T. Kahle
Trisha P. Gupte, Chang Li, Lan Jin, Kanat Yalcin, Mark W. Youngblood, Danielle F. Miyagishima, Ketu Mishra-Gorur, Amy Y. Zhao, Joseph Antonios, Anita Huttner, Declan McGuone, Nicholas A. Blondin, Joseph N. Contessa, Yawei Zhang, Robert K. Fulbright, Murat Gunel, Zeynep Erson-Omay, and Jennifer Moliterno
The association of seizures with meningiomas is poorly understood. Moreover, any relationship between seizures and the underlying meningioma genomic subgroup has not been studied. Herein, the authors report on their experience with identifying clinical and genomic factors associated with preoperative and postoperative seizure presentation in meningioma patients.
Clinical and genomic sequencing data on 394 patients surgically treated for meningioma at Yale New Haven Hospital were reviewed. Correlations between clinical, histological, or genomic variables and the occurrence of preoperative and postoperative seizures were analyzed. Logistic regression models were developed for assessing multiple risk factors for pre- and postoperative seizures. Mediation analyses were also conducted to investigate the causal pathways between genomic subgroups and seizures.
Seventeen percent of the cohort had presented with preoperative seizures. In a univariate analysis, patients with preoperative seizures were more likely to have tumors with a somatic NF2 mutation (p = 0.020), WHO grade II or III tumor (p = 0.029), atypical histology (p = 0.004), edema (p < 0.001), brain invasion (p = 0.009), and worse progression-free survival (HR 2.68, 95% CI 1.30–5.50). In a multivariate analysis, edema (OR 3.11, 95% CI 1.46–6.65, p = 0.003) and atypical histology (OR 2.00, 95% CI 1.03–3.90, p = 0.041) were positive predictors of preoperative seizures, while genomic subgroup was not, such that the effect of an NF2 mutation was indirectly mediated through atypical histology and edema (p = 0.012). Seizure freedom was achieved in 83.3% of the cohort, and only 20.8% of the seizure-free patients, who were more likely to have undergone gross-total resection (p = 0.031), were able to discontinue antiepileptic drug use postoperatively. Preoperative seizures (OR 3.54, 95% CI 1.37–9.12, p = 0.009), recurrent tumors (OR 2.89, 95% CI 1.08–7.74, p = 0.035), and tumors requiring postoperative radiation (OR 2.82, 95% CI 1.09–7.33, p = 0.033) were significant predictors of postoperative seizures in a multivariate analysis.
Seizures are relatively common at meningioma presentation. While NF2-mutated tumors are significantly associated with preoperative seizures, the association appears to be mediated through edema and atypical histology. Patients who undergo radiation and/or have a recurrence are at risk for postoperative seizures, regardless of the extent of resection. Preoperative seizures may indeed portend a more potentially aggressive molecular entity and challenging clinical course with a higher risk of recurrence.
Mark W. Youngblood, Daniel Duran, Julio D. Montejo, Chang Li, Sacit Bulent Omay, Koray Özduman, Amar H. Sheth, Amy Y. Zhao, Evgeniya Tyrtova, Danielle F. Miyagishima, Elena I. Fomchenko, Christopher S. Hong, Victoria E. Clark, Maximilien Riche, Matthieu Peyre, Julien Boetto, Sadaf Sohrabi, Sarah Koljaka, Jacob F. Baranoski, James Knight, Hongda Zhu, M. Necmettin Pamir, Timuçin Avşar, Türker Kilic, Johannes Schramm, Marco Timmer, Roland Goldbrunner, Ye Gong, Yaşar Bayri, Nduka Amankulor, Ronald L. Hamilton, Kaya Bilguvar, Irina Tikhonova, Patrick R. Tomak, Anita Huttner, Matthias Simon, Boris Krischek, Michel Kalamarides, E. Zeynep Erson-Omay, Jennifer Moliterno, and Murat Günel
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.
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.
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.
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.