Blog
About

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

      NSD1- and NSD2-damaging mutations define a subset of laryngeal tumors with favorable prognosis

      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

          Squamous cell carcinomas of the head and neck (SCCHN) affect anatomical sites including the oral cavity, nasal cavity, pharynx, and larynx. Laryngeal cancers are characterized by high recurrence and poor overall survival, and currently lack robust molecular prognostic biomarkers for treatment stratification. Using an algorithm for integrative clustering that simultaneously assesses gene expression, somatic mutation, copy number variation, and methylation, we for the first time identify laryngeal cancer subtypes with distinct prognostic outcomes, and differing from the non-prognostic laryngeal subclasses reported by The Cancer Genome Atlas (TCGA). Although most common laryngeal gene mutations are found in both subclasses, better prognosis is strongly associated with damaging mutations of the methyltransferases NSD1 and NSD2, with findings confirmed in an independent validation cohort consisting of 63 laryngeal cancer patients. Intriguingly, NSD1/2 mutations are not prognostic for nonlaryngeal SCCHN. These results provide an immediately useful clinical metric for patient stratification and prognostication.

          Abstract

          The authors use an integrative clustering approach to identify two laryngeal cancer clusters with distinct prognosis and show that mutations damaging the NSD1 and NSD2 methyltransferases segregate to the cluster with favorable prognosis, and independently predict longer survival in patients with laryngeal, but not other head and neck cancers.

          Related collections

          Most cited references 57

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Fast and accurate short read alignment with Burrows–Wheeler transform

          Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

            DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

              Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
                Bookmark

                Author and article information

                Contributors
                suraj.peri@fccc.edu
                Erica.golemis@fccc.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                24 November 2017
                24 November 2017
                2017
                : 8
                Affiliations
                [1 ]ISNI 0000 0004 0456 6466, GRID grid.412530.1, Biostatistics and Bioinformatics Division, , Fox Chase Cancer Center, ; Philadelphia, 19111 PA USA
                [2 ]ISNI 0000 0001 2171 9311, GRID grid.21107.35, Department of Otolaryngology-Head and Neck Surgery, , Johns Hopkins University School of Medicine, ; Baltimore, MD 21287 USA
                [3 ]ISNI 0000 0004 0456 6466, GRID grid.412530.1, Molecular Therapeutics Program, , Fox Chase Cancer Center, ; Philadelphia, PA 19111 USA
                [4 ]ISNI 0000 0001 2171 9311, GRID grid.21107.35, Department of Oncology, , Johns Hopkins University School of Medicine, ; Baltimore, MD 21287 USA
                [5 ]ISNI 0000000419368710, GRID grid.47100.32, Department of Internal Medicine and Developmental Therapeutics Program, , Yale Cancer Center, Yale School of Medicine, Yale University, ; New Haven, CT 06520 USA
                [6 ]ISNI 0000 0001 0726 5157, GRID grid.5734.5, Department of Otorhinolaryngology-Head and Neck Surgery, Inselspital, , University Hospital and University of Bern, ; Bern, 3010 Switzerland
                1877
                10.1038/s41467-017-01877-7
                5701248
                © The Author(s) 2017

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commonslicense, unless indicated otherwise in a credit line to the material. If material is not included in the article’sCreative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                Categories
                Article
                Custom metadata
                © The Author(s) 2017

                Uncategorized

                Comments

                Comment on this article