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      Transcriptome characterization by RNA sequencing identifies a major molecular and clinical subdivision in chronic lymphocytic leukemia

      research-article
      1 , 2 , 3 , 4 , 4 , 3 , 3 , 4 , 5 , 1 , 2 , 1 , 2 , 1 , 2 , 6 , 1 , 2 , 2 , 7 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 3 , 5 , 1 , 2 , 8 , 8 , 8 , 6 , 4 , 9 , 5 , 10 , 11 , 4 , 6 , 3 , 1 , 2 , 14
      Genome Research
      Cold Spring Harbor Laboratory Press

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          Abstract

          Chronic lymphocytic leukemia (CLL) has heterogeneous clinical and biological behavior. Whole-genome and -exome sequencing has contributed to the characterization of the mutational spectrum of the disease, but the underlying transcriptional profile is still poorly understood. We have performed deep RNA sequencing in different subpopulations of normal B-lymphocytes and CLL cells from a cohort of 98 patients, and characterized the CLL transcriptional landscape with unprecedented resolution. We detected thousands of transcriptional elements differentially expressed between the CLL and normal B cells, including protein-coding genes, noncoding RNAs, and pseudogenes. Transposable elements are globally derepressed in CLL cells. In addition, two thousand genes—most of which are not differentially expressed—exhibit CLL-specific splicing patterns. Genes involved in metabolic pathways showed higher expression in CLL, while genes related to spliceosome, proteasome, and ribosome were among the most down-regulated in CLL. Clustering of the CLL samples according to RNA-seq derived gene expression levels unveiled two robust molecular subgroups, C1 and C2. C1/C2 subgroups and the mutational status of the immunoglobulin heavy variable ( IGHV) region were the only independent variables in predicting time to treatment in a multivariate analysis with main clinico-biological features. This subdivision was validated in an independent cohort of patients monitored through DNA microarrays. Further analysis shows that B-cell receptor (BCR) activation in the microenvironment of the lymph node may be at the origin of the C1/C2 differences.

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          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.
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            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.
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              ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking

              Summary: Unsupervised class discovery is a highly useful technique in cancer research, where intrinsic groups sharing biological characteristics may exist but are unknown. The consensus clustering (CC) method provides quantitative and visual stability evidence for estimating the number of unsupervised classes in a dataset. ConsensusClusterPlus implements the CC method in R and extends it with new functionality and visualizations including item tracking, item-consensus and cluster-consensus plots. These new features provide users with detailed information that enable more specific decisions in unsupervised class discovery. Availability: ConsensusClusterPlus is open source software, written in R, under GPL-2, and available through the Bioconductor project (http://www.bioconductor.org/). Contact: mwilkers@med.unc.edu Supplementary Information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Journal
                Genome Res
                Genome Res
                GENOME
                Genome Research
                Cold Spring Harbor Laboratory Press
                1088-9051
                1549-5469
                February 2014
                February 2014
                : 24
                : 2
                : 212-226
                Affiliations
                [1 ]Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG), 08003 Barcelona, Catalonia, Spain;
                [2 ]Universitat Pompeu Fabra (UPF), 08003 Barcelona, Catalonia, Spain;
                [3 ]Unitat d'Hematopatologia, Servei d'Anatomia Patològica, Hospital Clínic, Universitat de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Spain;
                [4 ]Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre (CNIO), Spanish National Bioinformatics Institute, 28029 Madrid, Spain;
                [5 ]Research Unit on Biomedical Informatics, Department of Experimental and Health Sciences, University Pompeu Fabra, 08003 Barcelona, Catalonia, Spain;
                [6 ]Departamento de Bioquímica y Biología Molecular, Instituto Universitario de Oncología, Universidad de Oviedo, 33006 Oviedo, Spain;
                [7 ]Gene Regulation Stem Cells and Cancer Programme, Centre for Genomic Regulation (CRG), 08003 Barcelona, Catalonia, Spain;
                [8 ]Centro Nacional de Análisis Genómico, PCB, 08028 Barcelona, Spain;
                [9 ]Departamento de Anatomía Patológica, Farmacología y Microbiología, Universitat de Barcelona, 08036 Barcelona, Spain;
                [10 ]Institució Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Spain;
                [11 ]Servei de Hematologia, Hospital Clínic, IDIBAPS, 08036 Barcelona, Spain
                Author notes
                [12]

                Present address: Department of Genetic Medicine and Development, University of Geneva Medical School, 1211 Geneva, Switzerland

                [13]

                These authors contributed equally to this work.

                [14 ]Corresponding author E-mail roderic.guigo@ 123456crg.cat
                Article
                9518021
                10.1101/gr.152132.112
                3912412
                24265505
                69abd985-90f5-4a76-b041-3ed26791f84f
                © 2014 Ferreira et al.; Published by Cold Spring Harbor Laboratory Press

                This article, published in Genome Research, is available under a Creative Commons License (Attribution-NonCommercial 3.0 Unported), as described at http://creativecommons.org/licenses/by-nc/3.0/.

                History
                : 15 November 2012
                : 12 November 2013
                Page count
                Pages: 15
                Categories
                Research

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