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Baran Yılmaz, Zafer Orkun Toktaş, Akın Akakın, Semra Işık, Kaya Bilguvar, Türker Kılıç and Murat Günel

OBJECTIVE

Brain arteriovenous malformations (AVMs) can occur in patients with hereditary hemorrhagic telangiectasia (HHT). However, brain AVM without HHT has also been reported. Using whole exome sequencing, the authors performed comprehensive genomic characterization of a 6-person Turkish family with 3 cases of brain AVM without HHT.

METHODS

Three siblings with brain AVM, one of whom also had spinal AVM, were evaluated. The parents and the fourth sibling had no AVM on cranial MRI. The authors performed a whole exome capture and Illumina sequencing on blood samples from 2 siblings with AVM.

RESULTS

An ACVRL1 heterozygous mutation (p.Lys332Glu) was identified in 2 patients via whole exome sequencing. Variant segregation was confirmed using direct Sanger sequencing.

CONCLUSIONS

Study results suggested that whole exome sequencing analysis is particularly useful in cases of locus heterogeneity and uncertain diagnostic classification schemes in patients with hereditary brain AVM.

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Ege Ülgen, Özge Can, Kaya Bilguvar, Yavuz Oktay, Cemaliye B. Akyerli, Ayça Erşen Danyeli, M. Cengiz Yakıcıer, O. Uğur Sezerman, M. Necmettin Pamir and Koray Özduman

OBJECTIVE

Processes that cause or contribute to cancer, such as aging, exposure to carcinogens, or DNA damage repair deficiency (DDRd), create predictable and traceable nucleotide alterations in one’s genetic code (termed “mutational signatures”). Large studies have previously identified various such mutational signatures across cancers that can be attributed to the specific causative processes. To gain further insight into the processes in glioma development, the authors analyzed mutational signatures in adult diffuse gliomas (DGs).

METHODS

Twenty-five DGs and paired blood samples were whole exome sequenced. Somatic mutational signatures were identified using 2 different methods. Associations of the signatures with age at diagnosis, molecular subset, and mutational load were investigated. As DDRd-related signatures were frequently observed, germline and somatic DDR gene mutations as well as microsatellite instability (MSI) status were determined for all samples. For validation of signature prevalence, publicly available data from The Cancer Genome Atlas (TCGA) were used.

RESULTS

Each tumor had a unique combination of signatures. The most common signatures were signature 1 (88%, aging related), signature 3 (52%, homologous recombination related), and signature 15 (56%, mismatch repair related). Eighty-four percent of the tumors contained at least 1 DDRd signature. The findings were validated using public TCGA data. The weight of signature 1 positively correlated with age (r = 0.43) while cumulative weight of DDRd signatures negatively correlated with age (r = −0.16). Each subject had at least 1 germline/somatic alteration in a DDR gene, the most common being the risk single nucleotide polymorphism rs1800734 in MLH1. The rs1800734-AA genotype had a higher cumulative DDRd weight as well as higher mutational load; TP53 was the most common somatically altered DDR gene. MSI was observed in 24% of the tumors. No significant associations of MSI status with mutational load, rs1800734, or the cumulative weight of DDRd signatures were identified.

CONCLUSIONS

Current findings suggest that DDRd may act as a fundamental mechanism in gliomagenesis rather than being a random, secondary event.

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Kaya Bilguvar, Mohamad Bydon, Fatih Bayrakli, A. Gulhan Ercan-Sencicek, Yasar Bayri, Christopher Mason, Michael L. DiLuna, Margretta Seashore, Richard Bronen, Richard P. Lifton, Matthew State and Murat Gunel

Object

Greig cephalopolysyndactyly syndrome (GCPS) is one of a spectrum of overlapping clinical syndromes resulting from mutations in the gene GLI3 on chromosome 7p. Cerebral cavernous malformation (CCM) is caused by mutations in three distinct genes, including Malcavernin (CCM2), which also maps to chromosome 7p and is located 2.8 Mbp from GLI3. The authors describe a new syndrome that combines the vascular lesions characteristic of CCM with the hallmarks of GCPS, including polydactyly, hypertelorism, and developmental delay.

Methods

The authors used high-resolution array-based comparative genome hybridization (CGH) analysis to characterize the 3 million–bp deletion on chromosome 7 that accounts for this novel clinical presentation. A 4-year-old girl presented with polydactyly, hypertelorism, and developmental delay and was also found to have multiple CCMs after suffering a seizure.

Results

Genetic analysis using array-based CGH revealed a deletion affecting multiple genes in the 7p14-13 locus, the interval that includes both CCM2 and GLI3. Quantitative real-time polymerase chain reaction (RT-PCR) on genomic DNA confirmed this genomic lesion.

Conclusions

A novel syndrome, combining features of CCM and GCPS, can be added to the group of entities that result from deleterious genetic variants involving GLI3, including GCPS, acrocallosal syndrome, Pallister–Hall syndrome, and contiguous gene syndrome. The deletion responsible for this new entity can be easily detected using either array-based chromosomal analysis or quantitative RT-PCR.

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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

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.