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Molecular Signatures of Estrogen Receptor-Associated Genes in Breast Cancer Predict Clinical Outcome

  • James L. Wittliff
  • Traci L. Kruer
  • Sarah A. Andres
  • Irina Smolenkova
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 617)

Summary

Our goal is to identify new molecular targets for drug design and improve understanding of the molecular basis of clinical behavior and therapeutic response of breast cancer (BC). Pure populations of BC cells were procured by laser capture microdissection (LCM) from deidentified tissue specimens. RNA from either LCM-procured cells or whole tissue sections was extracted, purified, and quantified by RT-qPCR using β-actin for relative quantification. RNA was amplified, Cy5-labeled, and hybridized for microarray. Spectrophotometric and BioAnalyzer™ analyses evaluated aRNA yield, purity, and transcript length for gene microarray. Unsupervised and supervised methods selected 7 000 genes with significant variation. Expression profiles of BC cells were dominated by genes associated with estrogen receptor-α (ERα) status; over 3 000 genes were identified as differentially expressed between ERα+ and ERα- BC cells. Other prominent gene expression patterns divided ERα+ BCs into subgroups, which were associated with significantly different clinical outcomes (p < 0.01). While exploiting larger gene sets derived from LCM-cells and reports using whole tissues, a preliminary 14 gene subset was selected by UniGene Cluster analysis. Additionally, ERE-binding proteins (ERE-BP) were detected by EMSA, which were not recognized by ERα antibodies. Kaplan-Meier analysis indicated that patients with ERE-BP positive BCs had lower over-all survival than those with ERE-BP negative cancers. Collectively, these results will establish molecular signatures for assessing clinical features of BC and aid in the selection of molecular targets for drug development.

Keywords

Breast Cancer Breast Cancer Molecular Signature Laser Capture Microdissection Gene Subset 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer 2008

Authors and Affiliations

  • James L. Wittliff
    • 1
  • Traci L. Kruer
  • Sarah A. Andres
  • Irina Smolenkova
  1. 1.Hormone Receptor Laboratory Deptartment of Biochemistry and Molecular BiologyUniversity of LouisvilleLouisvilleUSA

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