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)


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.


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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Wittliff JL, Erlander MG (2002) Laser capture microdissection and its applications in genomics and proteomics. Methods Enzymol 356:12–25.PubMedCrossRefGoogle Scholar
  2. 2.
    Wittliff JL, Pasic R, Bland KI (1998) Steroid and Peptide Hormone Receptors: Methods, Quality Control and Clinical Use. In: Bland KI, Copeland III EM, editors. Breast: Comprehensive Management of Benign and Malignant Diseases. Philadelphia, PA: W.B. Saunders, Co., p. 458–498.Google Scholar
  3. 3.
    Cole KA, Krizman DB, Emmert-Buck MR (1999) The genetics of cancer – a 3D model. Nat Genet 21:38–41.PubMedCrossRefGoogle Scholar
  4. 4.
    Bonner RF, Emmert-Buck M, Cole K, et al. (1997) Laser capture microdissection: molecular analysis of tissue. Science 278(5342):1481–1483.PubMedCrossRefGoogle Scholar
  5. 5.
    Emmert-Buck MR, Bonner RF, Smith PD, et al. (1996) Laser capture microdissection. Science 274 (5289):998–1001.PubMedCrossRefGoogle Scholar
  6. 6.
    Simone NL, Bonner RF, Gillespie JW, et al. (1998) Laser-capture microdissection: opening the microscopic frontier to molecular analysis. Trends Genet 14(7):272–276.PubMedCrossRefGoogle Scholar
  7. 7.
    Wittliff JL, Kunitake ST, Chu SS, Travis JC (2000) Applications of laser capture microdissection in genomics and proteomics. J Clin Ligand Assay 23:66–73.Google Scholar
  8. 8.
    Ma XJ, Wang W, Salunga R, et al. (2003) Gene expression associated with clinical outcome in breast cancer via laser capture microdissection. Breast Cancer Res Treat 82(S15).Google Scholar
  9. 9.
    Wittliff JL, Ma XJ, Wang W, et al. (2003) Expression of estrogen receptor-associated genes in breast cancer cells procured by laser capture microdissection correlate with clinical outcome. Jensen Symposium, 81.Google Scholar
  10. 10.
    van’t Veer JL, Dai H, Van de Vijver MJ, et al. (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415(6871):530–536.CrossRefGoogle Scholar
  11. 11.
    Kang Y, Siegel PM, Shu W, et al. (2003) A multigenic program mediating breast cancer metastasis to bone. Cancer Cell 3(6):537–549.PubMedCrossRefGoogle Scholar
  12. 12.
    Ma XJ, Salunga R, Tuggle JT, et al. (2003) Gene expression profiles of human breast cancer progression. Proc Natl Acad Sci USA 100(10):5974–5979.PubMedCrossRefGoogle Scholar
  13. 13.
    Ramaswamy S, Ross KN, Lander ES, Golub TR (2003) A molecular signature of metastasis in primary solid tumors. Nat Genet 33(1):49–54.PubMedCrossRefGoogle Scholar
  14. 14.
    Sorlie T, Tibshirani R, Parker J, et al. Repeated Observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci USA 100:8418–8423.Google Scholar
  15. 15.
    Sotiriou C, Neo S-Y, McShane LM, et al. (2003) Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proc Natl Acad Sci USA 100:10393–10398.PubMedCrossRefGoogle Scholar
  16. 16.
    Ma XJ, Wang Z, Ryan PD, et al. (2004) A two-gene expression ratio predicts clinical outcome in breast cancer patients treated with tamoxifen. Cancer Cell 5(6):607–616.PubMedCrossRefGoogle Scholar
  17. 17.
    Jansen MPHM, Foekens JA, van Staveren IL, et al. (2005) Molecular classification of tamoxifen-resistant breast carcinomas by gene expression profiling. J Clin Oncol 23:732–740.PubMedCrossRefGoogle Scholar
  18. 18.
    Wang Y, Klijn JG, Zhang Y, et al. (2005) Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 365(9460):671–679.PubMedGoogle Scholar
  19. 19.
    Graumann K, Wittliff JL, Raffelsberger W, et al. (1996) Structural and functional analysis of N-terminal point mutants of the human estrogen receptor. J Steroid Biochem Mol Biol 57(5–6):293–300.PubMedCrossRefGoogle Scholar
  20. 20.
    Wittliff JL, Wenz LL, Dong J, et al. (1990) Expression and characterization of an active human estrogen receptor as a ubiquitin fusion protein from Escherichia coli. J Biol Chem 265(35):22016–22022.PubMedGoogle Scholar

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

Personalised recommendations