6
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Genomic landscape of high-grade meningiomas

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          High-grade meningiomas frequently recur and are associated with high rates of morbidity and mortality. To determine the factors that promote the development and evolution of these tumors, we analyzed the genomes of 134 high-grade meningiomas and compared this information with data from 595 previously published meningiomas. High-grade meningiomas had a higher mutation burden than low-grade meningiomas but did not harbor any significantly mutated genes aside from NF2. High-grade meningiomas also possessed significantly elevated rates of chromosomal gains and losses, especially among tumors with monosomy 22. Meningiomas previously treated with adjuvant radiation had significantly more copy number alterations than radiation-induced or radiation-naïve meningiomas. Across serial recurrences, genomic disruption preceded the emergence of nearly all mutations, remained largely uniform across time, and when present in low-grade meningiomas correlated with subsequent progression to a higher grade. In contrast to the largely stable copy number alterations, mutations were strikingly heterogeneous across tumor recurrences, likely due to extensive geographic heterogeneity in the primary tumor. While high-grade meningiomas harbored significantly fewer overtly targetable alterations than low-grade meningiomas, they contained numerous mutations that are predicted to be neoantigens, suggesting that immunologic targeting may be of therapeutic value.

          Brain tumors: uncovering genomic disruption in meningiomas

          Meningiomas, which arise from the tissue surrounding the brain and spinal cord, are the most common primary brain tumor in adults. The majority of these are slow-growing and amenable to surgical resection, if treatment is indicated. However, a subset of aggressive meningiomas are considered high-grade, producing significantly worse mortality. In a first study of its kind, Drs. Wenya Linda Bi, Ian Dunn, Sandro Santagata, Rameen Beroukhim, and colleagues at Harvard Medical School sequenced the genomes of 134 high-grade meningiomas and compared their makeup with lower-grade meningiomas. They found that aggressive tumors were more likely to harbor mutations in the NF2 gene and exhibit widespread genomic disruption. They also harbored an elevated rate of predicted immunogenic mutations, with implications for the use of immuno-modulatory therapies.

          Related collections

          Most cited references 34

          • Record: found
          • Abstract: found
          • Article: not found

          Reliable prediction of T-cell epitopes using neural networks with novel sequence representations.

          In this paper we describe an improved neural network method to predict T-cell class I epitopes. A novel input representation has been developed consisting of a combination of sparse encoding, Blosum encoding, and input derived from hidden Markov models. We demonstrate that the combination of several neural networks derived using different sequence-encoding schemes has a performance superior to neural networks derived using a single sequence-encoding scheme. The new method is shown to have a performance that is substantially higher than that of other methods. By use of mutual information calculations we show that peptides that bind to the HLA A*0204 complex display signal of higher order sequence correlations. Neural networks are ideally suited to integrate such higher order correlations when predicting the binding affinity. It is this feature combined with the use of several neural networks derived from different and novel sequence-encoding schemes and the ability of the neural network to be trained on data consisting of continuous binding affinities that gives the new method an improved performance. The difference in predictive performance between the neural network methods and that of the matrix-driven methods is found to be most significant for peptides that bind strongly to the HLA molecule, confirming that the signal of higher order sequence correlation is most strongly present in high-binding peptides. Finally, we use the method to predict T-cell epitopes for the genome of hepatitis C virus and discuss possible applications of the prediction method to guide the process of rational vaccine design.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Efficient de novo assembly of large genomes using compressed data structures.

            De novo genome sequence assembly is important both to generate new sequence assemblies for previously uncharacterized genomes and to identify the genome sequence of individuals in a reference-unbiased way. We present memory efficient data structures and algorithms for assembly using the FM-index derived from the compressed Burrows-Wheeler transform, and a new assembler based on these called SGA (String Graph Assembler). We describe algorithms to error-correct, assemble, and scaffold large sets of sequence data. SGA uses the overlap-based string graph model of assembly, unlike most de novo assemblers that rely on de Bruijn graphs, and is simply parallelizable. We demonstrate the error correction and assembly performance of SGA on 1.2 billion sequence reads from a human genome, which we are able to assemble using 54 GB of memory. The resulting contigs are highly accurate and contiguous, while covering 95% of the reference genome (excluding contigs <200 bp in length). Because of the low memory requirements and parallelization without requiring inter-process communication, SGA provides the first practical assembler to our knowledge for a mammalian-sized genome on a low-end computing cluster.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              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 ~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.
                Bookmark

                Author and article information

                Contributors
                +617-525-5686 , ssantagata@bics.bwh.harvard.edu
                +617-732-5633 , idunn@partners.org
                +617-582-7941 , Rameen_Beroukhim@dfci.harvard.edu
                Journal
                NPJ Genom Med
                NPJ Genom Med
                NPJ Genomic Medicine
                Nature Publishing Group UK (London )
                2056-7944
                26 April 2017
                26 April 2017
                2017
                : 2
                Affiliations
                [1 ]Center for Skull Base and Pituitary Surgery, Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA
                [2 ]ISNI 0000 0001 2106 9910, GRID grid.65499.37, Department of Cancer Biology, , Dana-Farber Cancer Institute, ; Boston, MA USA
                [3 ]GRID grid.66859.34, Broad Institute of MIT and Harvard, ; Cambridge, MA USA
                [4 ]ISNI 0000 0004 0378 8294, GRID grid.62560.37, Division of Neuropathology, Department of Pathology, , Brigham and Women’s Hospital, ; Boston, MA USA
                [5 ]ISNI 0000 0004 0593 1832, GRID grid.415277.2, Research Center, , King Fahad Medical City, ; Riyadh, Saudi Arabia
                [6 ]ISNI 0000 0000 8808 6435, GRID grid.452562.2, The Saudi Human Genome Project Lab, , King Abdulaziz City for Science and Technology, ; Riyadh, Saudi Arabia
                [7 ]ISNI 0000 0004 0386 9924, GRID grid.32224.35, Department of Neurosurgery, , Massachusetts General Hospital, ; Boston, MA USA
                [8 ]ISNI 0000 0004 1936 7822, GRID grid.170205.1, Department of Surgery, , The University of Chicago, ; Chicago, IL USA
                [9 ]ISNI 0000 0001 2355 7002, GRID grid.4367.6, Department of Pathology and Immunology, , Washington University School of Medicine, ; St. Louis, MO USA
                [10 ]ISNI 0000 0001 0413 4629, GRID grid.35915.3b, Computer Technologies Department, , ITMO University, ; Saint Petersburg, Russia
                [11 ]ISNI 0000 0001 2106 9910, GRID grid.65499.37, Center for Cancer Genome Discovery, , Dana-Farber Cancer Institute, ; Boston, MA USA
                [12 ]ISNI 0000 0004 0386 9924, GRID grid.32224.35, Department of Pathology, , Massachusetts General Hospital, ; Boston, MA USA
                [13 ]ISNI 0000 0001 2355 7002, GRID grid.4367.6, Department of Neurosurgery, , Washington University School of Medicine, ; St. Louis, MO USA
                [14 ]ISNI 0000 0001 2355 7002, GRID grid.4367.6, Center for Human Immunology and Immunotherapy Programs, , Washington University School of Medicine, ; St. Louis, MO USA
                [15 ]ISNI 0000 0001 2106 9910, GRID grid.65499.37, Department of Medical Oncology, , Dana-Farber Cancer Institute, ; Boston, MA USA
                Article
                14
                10.1038/s41525-017-0014-7
                5506858
                28713588
                © The Author(s) 2017

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                Categories
                Article
                Custom metadata
                © The Author(s) 2017

                Comments

                Comment on this article