Blog
About

  • Record: found
  • Abstract: found
  • Article: found

Integrated Genomic Analysis Identifies Clinically Relevant Subtypes of Glioblastoma Characterized by Abnormalities in PDGFRA, IDH1, EGFR, and NF1

Read this article at

ScienceOpenPublisher
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

      Related collections

      Most cited references 41

      • Record: found
      • Abstract: found
      • Article: found

      Comprehensive genomic characterization defines human glioblastoma genes and core pathways

        (2008)
      Human cancer cells typically harbor multiple chromosomal aberrations, nucleotide substitutions and epigenetic modifications that drive malignant transformation. The Cancer Genome Atlas (TCGA) pilot project aims to assess the value of large-scale multidimensional analysis of these molecular characteristics in human cancer and to provide the data rapidly to the research community. Here, we report the interim integrative analysis of DNA copy number, gene expression and DNA methylation aberrations in 206 glioblastomas (GBM), the most common type of adult brain cancer, and nucleotide sequence aberrations in 91 of the 206 GBMs. This analysis provides new insights into the roles of ERBB2, NF1 and TP53, uncovers frequent mutations of the PI3 kinase regulatory subunit gene PIK3R1, and provides a network view of the pathways altered in the development of GBM. Furthermore, integration of mutation, DNA methylation and clinical treatment data reveals a link between MGMT promoter methylation and a hypermutator phenotype consequent to mismatch repair deficiency in treated glioblastomas, an observation with potential clinical implications. Together, these findings establish the feasibility and power of TCGA, demonstrating that it can rapidly expand knowledge of the molecular basis of cancer.
        Bookmark
        • Record: found
        • Abstract: found
        • Article: found
        Is Open Access

        In silico prediction of protein-protein interactions in human macrophages

        Background: Protein-protein interaction (PPI) network analyses are highly valuable in deciphering and understanding the intricate organisation of cellular functions. Nevertheless, the majority of available protein-protein interaction networks are context-less, i.e. without any reference to the spatial, temporal or physiological conditions in which the interactions may occur. In this work, we are proposing a protocol to infer the most likely protein-protein interaction (PPI) network in human macrophages. Results: We integrated the PPI dataset from the Agile Protein Interaction DataAnalyzer (APID) with different meta-data to infer a contextualized macrophage-specific interactome using a combination of statistical methods. The obtained interactome is enriched in experimentally verified interactions and in proteins involved in macrophage-related biological processes (i.e. immune response activation, regulation of apoptosis). As a case study, we used the contextualized interactome to highlight the cellular processes induced upon Mycobacterium tuberculosis infection. Conclusion: Our work confirms that contextualizing interactomes improves the biological significance of bioinformatic analyses. More specifically, studying such inferred network rather than focusing at the gene expression level only, is informative on the processes involved in the host response. Indeed, important immune features such as apoptosis are solely highlighted when the spotlight is on the protein interaction level.
          Bookmark
          • Record: found
          • Abstract: found
          • Article: not found

          Summaries of Affymetrix GeneChip probe level data.

          High density oligonucleotide array technology is widely used in many areas of biomedical research for quantitative and highly parallel measurements of gene expression. Affymetrix GeneChip arrays are the most popular. In this technology each gene is typically represented by a set of 11-20 pairs of probes. In order to obtain expression measures it is necessary to summarize the probe level data. Using two extensive spike-in studies and a dilution study, we developed a set of tools for assessing the effectiveness of expression measures. We found that the performance of the current version of the default expression measure provided by Affymetrix Microarray Suite can be significantly improved by the use of probe level summaries derived from empirically motivated statistical models. In particular, improvements in the ability to detect differentially expressed genes are demonstrated.
            Bookmark

            Author and article information

            Journal
            Cancer Cell
            Cancer Cell
            Elsevier BV
            15356108
            January 2010
            January 2010
            : 17
            : 1
            : 98-110
            10.1016/j.ccr.2009.12.020
            © 2010

            http://www.elsevier.com/tdm/userlicense/1.0/

            Comments

            Comment on this article