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Background: With a prevalence of 170 000 adults in the US alone, meningiomas are the most common primary intracranial tumors. The management of skull base meningiomas is challenging due to their complexity and proximity to crucial nearby structures. The identification of oncogenic mutations has provided further insights into the tumorigenesis of meningioma and the possibility of targeted therapy. This study aimed to further investigate the association of mutational profiles with anatomical distribution, histological subtype, WHO grade, and recurrence in patients with meningioma. Methods: Tissue samples were collected from 71 patients diagnosed with meningioma from 2008 to 2016. A total of 51 cases were skull based. Samples were subjected to targeted sequencing using a next generation customized cancer gene panel (n = 66 genes analyzed). Results: We detected genomic alterations (GAs) in 68 tumors, averaging 1.56 ± 1.07 genomic alterations (GAs) per sample. NF2 was the most frequently altered gene (36/71 cases). Interestingly, we identified a number of mutations in non-NF2 genes, including a hotspot TERTp c.-124: G > A mutation that may be related to poor prognosis and FGFR3 mutations that may represent biomarkers of a favorable prognosis as reported in other cancers. Conclusions: We demonstrate that comprehensive genomic profiling in our population can reveal a potential new prognostic biomarkers of skull base meningioma. These mutations can enhance diagnostic accuracy and clinical decision-making. Among our findings were the identification of a TERTp mutation and the first report of FGFR3 mutations that may represent biomarkers for the identification of skull base meningioma patients with a favorable prognosis.
Pubmed ID: 31565188
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Curated database of known (published) gene lesions responsible for human inherited disease.
View all literature mentionsA software tool which predicts whether an amino acid substitution or indel has an impact on the biological function of a protein.
View all literature mentionsCommercial vendor and service provider of laboratory reagents and antibodies. Supplier of scientific instrumentation, reagents and consumables, and software services.
View all literature mentionsA dataset containing the full genomic sequence of 1,700 individuals, freely available for research use. The 1000 Genomes Project is an international research effort coordinated by a consortium of 75 companies and organizations to establish the most detailed catalogue of human genetic variation. The project has grown to 200 terabytes of genomic data including DNA sequenced from more than 1,700 individuals that researchers can now access on AWS for use in disease research free of charge. The dataset containing the full genomic sequence of 1,700 individuals is now available to all via Amazon S3. The data can be found at: http://s3.amazonaws.com/1000genomes The 1000 Genomes Project aims to include the genomes of more than 2,662 individuals from 26 populations around the world, and the NIH will continue to add the remaining genome samples to the data collection this year. Public Data Sets on AWS provide a centralized repository of public data hosted on Amazon Simple Storage Service (Amazon S3). The data can be seamlessly accessed from AWS services such Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Elastic MapReduce (Amazon EMR), which provide organizations with the highly scalable compute resources needed to take advantage of these large data collections. AWS is storing the public data sets at no charge to the community. Researchers pay only for the additional AWS resources they need for further processing or analysis of the data. All 200 TB of the latest 1000 Genomes Project data is available in a publicly available Amazon S3 bucket. You can access the data via simple HTTP requests, or take advantage of the AWS SDKs in languages such as Ruby, Java, Python, .NET and PHP. Researchers can use the Amazon EC2 utility computing service to dive into this data without the usual capital investment required to work with data at this scale. AWS also provides a number of orchestration and automation services to help teams make their research available to others to remix and reuse. Making the data available via a bucket in Amazon S3 also means that customers can crunch the information using Hadoop via Amazon Elastic MapReduce, and take advantage of the growing collection of tools for running bioinformatics job flows, such as CloudBurst and Crossbow.
View all literature mentionsEvaluates disease-causing potential of sequence alterations.
View all literature mentionsCell line BCBL-1 is a Cancer cell line with a species of origin Homo sapiens (Human)
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