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      https://www.ias.ac.in/article/fulltext/jbsc/032/05/1027-1039

    • Keywords

       

      Breast cancer subtypes; clustering; metastatic risk; microarray

    • Abstract

       

      We develop a new technique to analyse microarray data which uses a combination of principal components analysis and consensus ensemble 𝑘-clustering to find robust clusters and gene markers in the data. We apply our method to a public microarray breast cancer dataset which has expression levels of genes in normal samples as well as in three pathological stages of disease; namely, atypical ductal hyperplasia or ADH, ductal carcinoma in situ or DCIS and invasive ductal carcinoma or IDC. Our method averages over clustering techniques and data perturbation to find stable, robust clusters and gene markers. We identify the clusters and their pathways with distinct subtypes of breast cancer (Luminal, Basal and Her2+). We confirm that the cancer phenotype develops early (in early hyperplasia or ADH stage) and find from our analysis that each subtype progresses from ADH to DCIS to IDC along its own specific pathway, as if each was a distinct disease.

    • Author Affiliations

       

      G Alexe1 2 G S Dalgin3 S Ganesan4 C DeLisi5 G Bhanot2 3 4 5 6

      1. The Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge MA, 02142, USA
      2. The Simons Center for Systems Biology, Institute for Advanced Study, Princeton, NJ 08540, USA
      3. Molecular Biology, Cell Biology and Biochemistry Program, Boston University, Boston, MA 02215, USA
      4. Cancer Institute of New Jersey, 195 Little Albany Street, New Brunswick, NJ 08903, USA
      5. Center for Advanced Genomic Technology, Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
      6. BioMaPS Institute and Department of Biomedical Engineering, Rutgers University, Piscataway, NJ 08854, USA
    • Dates

       
  • Journal of Biosciences | News

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