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      Integrative analysis of RUNX1 downstream pathways and target genes

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          Abstract

          Background

          The RUNX1 transcription factor gene is frequently mutated in sporadic myeloid and lymphoid leukemia through translocation, point mutation or amplification. It is also responsible for a familial platelet disorder with predisposition to acute myeloid leukemia (FPD-AML). The disruption of the largely unknown biological pathways controlled by RUNX1 is likely to be responsible for the development of leukemia. We have used multiple microarray platforms and bioinformatic techniques to help identify these biological pathways to aid in the understanding of why RUNX1 mutations lead to leukemia.

          Results

          Here we report genes regulated either directly or indirectly by RUNX1 based on the study of gene expression profiles generated from 3 different human and mouse platforms. The platforms used were global gene expression profiling of: 1) cell lines with RUNX1 mutations from FPD-AML patients, 2) over-expression of RUNX1 and CBFβ, and 3) Runx1 knockout mouse embryos using either cDNA or Affymetrix microarrays. We observe that our datasets (lists of differentially expressed genes) significantly correlate with published microarray data from sporadic AML patients with mutations in either RUNX1 or its cofactor, CBFβ. A number of biological processes were identified among the differentially expressed genes and functional assays suggest that heterozygous RUNX1 point mutations in patients with FPD-AML impair cell proliferation, microtubule dynamics and possibly genetic stability. In addition, analysis of the regulatory regions of the differentially expressed genes has for the first time systematically identified numerous potential novel RUNX1 target genes.

          Conclusion

          This work is the first large-scale study attempting to identify the genetic networks regulated by RUNX1, a master regulator in the development of the hematopoietic system and leukemia. The biological pathways and target genes controlled by RUNX1 will have considerable importance in disease progression in both familial and sporadic leukemia as well as therapeutic implications.

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          Most cited references47

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          Prognostically useful gene-expression profiles in acute myeloid leukemia.

          In patients with acute myeloid leukemia (AML) a combination of methods must be used to classify the disease, make therapeutic decisions, and determine the prognosis. However, this combined approach provides correct therapeutic and prognostic information in only 50 percent of cases. We determined the gene-expression profiles in samples of peripheral blood or bone marrow from 285 patients with AML using Affymetrix U133A GeneChips containing approximately 13,000 unique genes or expression-signature tags. Data analyses were carried out with Omniviz, significance analysis of microarrays, and prediction analysis of microarrays software. Statistical analyses were performed to determine the prognostic significance of cases of AML with specific molecular signatures. Unsupervised cluster analyses identified 16 groups of patients with AML on the basis of molecular signatures. We identified the genes that defined these clusters and determined the minimal numbers of genes needed to identify prognostically important clusters with a high degree of accuracy. The clustering was driven by the presence of chromosomal lesions (e.g., t(8;21), t(15;17), and inv(16)), particular genetic mutations (CEBPA), and abnormal oncogene expression (EVI1). We identified several novel clusters, some consisting of specimens with normal karyotypes. A unique cluster with a distinctive gene-expression signature included cases of AML with a poor treatment outcome. Gene-expression profiling allows a comprehensive classification of AML that includes previously identified genetically defined subgroups and a novel cluster with an adverse prognosis. Copyright 2004 Massachusetts Medical Society
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            Discovering statistically significant pathways in expression profiling studies.

            Accurate and rapid identification of perturbed pathways through the analysis of genome-wide expression profiles facilitates the generation of biological hypotheses. We propose a statistical framework for determining whether a specified group of genes for a pathway has a coordinated association with a phenotype of interest. Several issues on proper hypothesis-testing procedures are clarified. In particular, it is shown that the differences in the correlation structure of each set of genes can lead to a biased comparison among gene sets unless a normalization procedure is applied. We propose statistical tests for two important but different aspects of association for each group of genes. This approach has more statistical power than currently available methods and can result in the discovery of statistically significant pathways that are not detected by other methods. This method is applied to data sets involving diabetes, inflammatory myopathies, and Alzheimer's disease, using gene sets we compiled from various public databases. In the case of inflammatory myopathies, we have correctly identified the known cytotoxic T lymphocyte-mediated autoimmunity in inclusion body myositis. Furthermore, we predicted the presence of dendritic cells in inclusion body myositis and of an IFN-alpha/beta response in dermatomyositis, neither of which was previously described. These predictions have been subsequently corroborated by immunohistochemistry.
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              oPOSSUM: identification of over-represented transcription factor binding sites in co-expressed genes

              Targeted transcript profiling studies can identify sets of co-expressed genes; however, identification of the underlying functional mechanism(s) is a significant challenge. Established methods for the analysis of gene annotations, particularly those based on the Gene Ontology, can identify functional linkages between genes. Similar methods for the identification of over-represented transcription factor binding sites (TFBSs) have been successful in yeast, but extension to human genomics has largely proved ineffective. Creation of a system for the efficient identification of common regulatory mechanisms in a subset of co-expressed human genes promises to break a roadblock in functional genomics research. We have developed an integrated system that searches for evidence of co-regulation by one or more transcription factors (TFs). oPOSSUM combines a pre-computed database of conserved TFBSs in human and mouse promoters with statistical methods for identification of sites over-represented in a set of co-expressed genes. The algorithm successfully identified mediating TFs in control sets of tissue-specific genes and in sets of co-expressed genes from three transcript profiling studies. Simulation studies indicate that oPOSSUM produces few false positives using empirically defined thresholds and can tolerate up to 50% noise in a set of co-expressed genes.
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                Author and article information

                Journal
                BMC Genomics
                BMC Genomics
                BioMed Central
                1471-2164
                2008
                31 July 2008
                : 9
                : 363
                Affiliations
                [1 ]Molecular Medicine Division, The Walter and Eliza Hall Institute of Medical Research, Parkville 3050, Victoria, Australia
                [2 ]Department of Medical Biology, The University of Melbourne, 3050 Parkville, Victoria, Australia
                [3 ]Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville 3050, Victoria, Australia
                [4 ]Division of Medical Genetics, University of Geneva Medical School, 1211 Geneva, Switzerland
                [5 ]Experimental Therapeutics Program, Children's Cancer Institute Australia for Medical Research, 2031 NSW, Australia
                [6 ]Department of Hematology and Genetic Pathology, School of Medicine, Flinders University, 5001 South Australia, Australia
                [7 ]Molecular and Cell Biology, National University of Singapore, 117543 Singapore
                [8 ]Division of Medical Genetics, University of Washington, Seattle, USA
                [9 ]Center for Integrative Genomics, University of Lausanne, Switzerland
                [10 ]Internal Medicine, University Hospital, Berne, Switzerland
                [11 ]Université de Lyon, Lyon, F-69008, France; Université Lyon 1, Domaine Rockfeller, Lyon, F-69008, France
                [12 ]CNRS UMR 5201, Laboratoire de Génétique Moléculaire, Signalisation et Cancer, Lyon, F-69008, France
                [13 ]Division of Molecular Genome Analysis, German Cancer Research Center (DKFZ), Heidelberg, Germany
                [14 ]Division of Molecular Pathology, the Institute of Medical and Veterinary Science and The Hanson Institute, Box 14 Rundle Mall Post Office, Adelaide, SA 5000, Australia
                [15 ]The School of Medicine the University of Adelaide, SA, 5005, Australia
                Article
                1471-2164-9-363
                10.1186/1471-2164-9-363
                2529319
                18671852
                597866ca-0080-4912-85a2-6486c7953020
                Copyright © 2008 Michaud et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 24 September 2007
                : 31 July 2008
                Categories
                Research Article

                Genetics
                Genetics

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