Novel Strategies for the Treatment of Estrogen Receptor-Negative Breast Cancer

Abstract

Using Affymetrix gene expression profiling on 2 independent sets of human breast tumor samples with known ER, PR, and Her2/neu status, we were able to molecularly profile breast tumors and identify a list of kinases that were differentially expressed in ER-negative tumors. Supervised clustering analysis based on ER status was performed and a gene expression profile was generated using 779 known and putative human kinases. Analysis in two independent sets of tumor samples identified 70 and 84 differentially expressed kinases, respectively (2.3 fold higher in ER-negative tumors, p-value <.05). The intersection of these lists contained 37 kinases. Additionally, unsupervised clustering analysis in both sets seemed to identify kinases that defined ER-negative, Her2/neu positive tumors as well as ER-negative, Her2/neu negative tumors. Overexpresssion of kinases was confirmed and siRNA knockdown of kinases identified in the microarray analysis identifies several kinases that are critical for ER-negative, but not ER-positive breast cancer cell growth.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2007
Accession Number
ADA478800

Entities

People

  • Corey Speers

Organizations

  • Baylor College of Medicine

Tags

DTIC Thesaurus Topics

  • Alkenes
  • Breast Cancer
  • Cell Line
  • Cell Physiological Processes
  • Cells
  • Chemistry
  • Data Analysis
  • Data Sets
  • Diseases And Disorders
  • Gene Expression
  • Kinases
  • Lymphocytes
  • Microarray Analysis
  • Neoplasms
  • Peptide Growth Factors
  • Proteins
  • Tumor Cell Line

Fields of Study

  • Biology

Readers

  • Breast cancer cell signaling and growth regulation.
  • Molecular and genetic basis of cancer.