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Detection of the androgen receptor in primary human breast cancer by multiparameter flow cytometry technique

Lookup NU author(s): Susan Stamp, Seb Aspinall, Dr Brian Shenton, Professor Thomas Lennard


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Whilst androgen receptor expression (AR) has been extensively investigated in primary breast cancer, methods used for its detection have been limited due to problems with quantification. The present study was to develop a rapid, accurate and quantitative method for AR detection using multiparameter flow cytometry. METHOD: In order to analyze the AR expression in breast tumor cells a 2-color flow cytomerty test was developed. All antibodies were titrated, using positive and negative cell lines, to establish optimum staining and their specificity checked using Western blot analysis. 100ul of previously fixed and permiablised cells were incubated primarily with 3ul of 1:5 dilution of biotinylated monoclonal androgen receptor (Dako). P.E. labeled Streptavidin (Dako) was then used to detect the primary antibody together with 5D3 FITC (Novacastra) for the detection of cytokeratin positive staining cells. RESULTS: Having optimized the method, tests were performed on cell lines of known AR status and the staining pattern was confirmed. The staining method was then used on primary breast tumors. The method allows detection of cytokeratin positive epithelial tumor cells, which are analyzed for AR status. The resulting data was expressed as AR molecules using quantitative beads. CONCLUSION: The described method provides an accurate and quantifiable assay for AR in breast cell lines. Comparative data shows a high degree of correlation between ER alpha status and AR status in breast tumors (r=0.47,p=0.008) by the above method.

Publication metadata

Author(s): Stamp, S., Aspinall, S., Shenton, B.K., Lennard, T.W.J.

Publication type: Article

Publication status: Published

Journal: Breast Cancer Research and Treatment

Year: 2001

Volume: 69

Issue: 3

Pages: 294-

Print publication date: 01/01/2001

ISSN (print): 0167-6806

ISSN (electronic): 1573-7217