Dissecting Pathway Disturbances Using Network Topology and Multi-platform Genomics Data

  • Yuping Zhang
  • M. Henry Linder
  • Ali Shojaie
  • Zhengqing Ouyang
  • Ronglai Shen
  • Keith A. Baggerly
  • Veerabhadran Baladandayuthapani
  • Hongyu Zhao
Article
  • 77 Downloads

Abstract

Complex diseases such as cancers usually result from accumulated disturbance of pathways instead of the disruptions of one or a few major genes. As opposed to single-platform analyses, it is likely that integrating diverse molecular regulatory elements and their interactions can lead to more insights on pathway-level disturbances of biological systems and their potential consequences in disease development and progression. To explore the benefit of pathway-based analysis, we focus on multi-platform genomics, epigenomics, and transcriptomics (-omics, for short) from 11 cancer types collected by The Cancer Genome Atlas project. Specifically, we use a well-studied oncogenic pathway, the BRAF pathway, to investigate the relevant copy number variants (CNVs), methylations, and gene expressions, and quantify their effects on discovering tumor-specific aberrations across multiple tumor lineages. We also perform simulation studies to further investigate the effects of network topology and multiple omics on dissecting pathway disturbances. Our analysis shows that adding molecular regulatory elements such as CNVs and/or methylations to the baseline mRNA molecules can improve our power of discovering tumorous aberrances. Also, incorporating CNVs with the baseline mRNA molecules can be more beneficial than incorporating methylations. Moreover, employing regulatory topologies can improve the discoveries of tumorous aberrances. Finally, our analysis reveals similarities and differences among diverse cancer types based on disturbance of the BRAF pathway.

Keywords

Data integration Multi-platform genomics Network topology Pathway analysis 

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Copyright information

© International Chinese Statistical Association 2017

Authors and Affiliations

  • Yuping Zhang
    • 1
  • M. Henry Linder
    • 2
  • Ali Shojaie
    • 3
  • Zhengqing Ouyang
    • 4
  • Ronglai Shen
    • 5
  • Keith A. Baggerly
    • 6
  • Veerabhadran Baladandayuthapani
    • 7
  • Hongyu Zhao
    • 8
  1. 1.Department of Statistics, Institute for Systems Genomics, Center for Quantitative Medicine, Institute for Collaboration on Health, Intervention, and Policy, The Connecticut Institute for the Brain and Cognitive SciencesUniversity of ConnecticutStorrsUSA
  2. 2.Department of StatisticsUniversity of ConnecticutStorrsUSA
  3. 3.Department of BiostatisticsUniversity of WashingtonSeattleUSA
  4. 4.The Jackson Laboratory for Genomic MedicineFarmingtonUSA
  5. 5.Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer CenterNew YorkUSA
  6. 6.Department of Bioinformatics and Computational BiologyThe University of Texas MD Anderson Cancer CenterHoustonUSA
  7. 7.Department of BiostatisticsThe University of Texas MD Anderson Cancer CenterHoustonUSA
  8. 8.Department of Biostatistics, Yale School of Public Health, and Department of GeneticsYale School of MedicineNew HavenUSA

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