DETECTION OF DISCRETE ANDROGEN RECEPTOR EPITOPES IN PROSTATE-CANCER BY IMMUNOSTAINING - MEASUREMENT BY COLOR VIDEO IMAGE-ANALYSIS

Citation
Wd. Tilley et al., DETECTION OF DISCRETE ANDROGEN RECEPTOR EPITOPES IN PROSTATE-CANCER BY IMMUNOSTAINING - MEASUREMENT BY COLOR VIDEO IMAGE-ANALYSIS, Cancer research, 54(15), 1994, pp. 4096-4102
Citations number
41
Categorie Soggetti
Oncology
Journal title
ISSN journal
00085472
Volume
54
Issue
15
Year of publication
1994
Pages
4096 - 4102
Database
ISI
SICI code
0008-5472(1994)54:15<4096:DODARE>2.0.ZU;2-4
Abstract
To determine whether multiple features of immunohistochemical staining of the androgen receptor (AR) in prostate cancer could reliably predi ct androgen dependence, tumor biopsy specimens from 30 patients (stage s A-D-2) were stained using anti-peptide antibodies to the amino- and carboxyl-termini of the AR. Measurements were made of the mean area an d total amount (i.e., integrated optical density) of AR staining in at least 20 fields per section using a color video image analysis system , and the mean intensity of AR staining per cell and the percentage of AR positive tumor cells were derived. Video image analysis measuremen t identified quantitative differences in AR staining between the two a ntibodies, suggesting that this approach may provide a means of identi fying receptor variants in prostate tumors. The AR staining measuremen ts were analyzed by discriminant function analysis to assign individua l cases to good and poor clinical outcome groups. AR staining features measured with a single antibody (e.g., amino-terminal) were sufficien t to predict outcome following hormonal therapy in stage D-2 patients (predictive value, 1.0), whereas all features of AR staining measured with both antibodies were required for the entire patient group (predi ctive value, 0.97). The principal discriminant in both patient groups contributing to the correct assignment of outcome was the mean intensi ty of AR staining per cell. These findings suggest that AR staining fe atures measured by video image analysis have the potential to predict outcome in prostate cancer.