Genomic Analysis of Non-NF2 Meningiomas Reveals Mutations in TRAF7, KLF4, AKT1, and SMO

We report genomic analysis of 300 meningiomas, the most common primary brain tumors, leading to the discovery of mutations in TRAF7, a proapoptotic E3 ubiquitin ligase, in nearly one-fourth of all meningiomas. Mutations in TRAF7 commonly occurred with a recurrent mutation ( K409Q) in KLF4, a transcription factor known for its role in inducing pluripotency, or with AKT1(E17K), a mutation known to activate the PI3K pathway. SMO mutations, which activate Hedgehog signaling, were identified in similar to 5% of non-NF2 mutant meningiomas. These non-NF2 meningiomas were clinically distinctive-nearly always benign, with chromosomal stability, and originating from the medial skull base. In contrast, meningiomas with mutant NF2 and/or chromosome 22 loss were more likely to be atypical, showing genomic instability, and localizing to the cerebral and cerebellar hemispheres. Collectively, these findings identify distinct meningioma subtypes, suggesting avenues for targeted therapeutics

Integrated genomic characterization of IDH1-mutant glioma malignant progression

Gliomas represent approximately 30% of all central nervous system tumors and 80% of malignant brain tumors(1). To understand the molecular mechanisms underlying the malignant progression of low-grade gliomas with mutations in IDH1 (encoding isocitrate dehydrogenase 1), we studied paired tumor samples from 41 patients, comparing higher-grade, progressed samples to their lower-grade counterparts. Integrated genomic analyses, including whole-exome sequencing and copy number, gene expression and DNA methylation profiling, demonstrated nonlinear clonal expansion of the original tumors and identified oncogenic pathways driving progression. These include activation of the MYC and RTK-RAS-PI3K pathways and upregulation of the FOXM1- and E2F2-mediated cell cycle transitions, as well as epigenetic silencing of developmental transcription factor genes bound by Polycomb repressive complex 2 in human embryonic stem cells. Our results not only provide mechanistic insight into the genetic and epigenetic mechanisms driving glioma progression but also identify inhibition of the bromodomain and extraterminal (BET) family as a potential therapeutic approach

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

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