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Subcellular Localization and Protein Levels of Cyclin-Dependent Kinase Inhibitor p27 Independently Predict for Survival in Epithelial Ovarian Cancer

Authors' Affiliations: Departments of ¹ Medical Oncology and ² Pathology, Yale University School of Medicine, New Haven, Connecticut and Departments of ³ Clinical Therapeutics and ⁴ Pathology, University of Athens School of Medicine, Athens, Greece

Requests for reprints: Amanda Psyrri, Yale Cancer Center, P.O. Box 208032, New Haven, CT 06520. Phone: 203-737-2476; Fax: 203-785-7531; E-mail: diamando.psyrri{at}yale.edu.

Purpose: p27 protein is regarded as a valuable prognostic biomarker in cancer with a potential use as a molecular target. However, different methods of immunohistochemical assessment have yielded conflicting results. Here, we sought to determine the prognostic value of p27 in ovarian cancer using a novel method of compartmentalized in situ protein analysis.

Experimental Design: A tissue array composed of 150 advanced stage ovarian cancers uniformly treated, with surgical debulking followed by platinum-paclitaxel combination chemotherapy, was constructed. For evaluation of p27 protein expression, we used an immunofluorescence-based method of automated in situ quantitative measurement of protein analysis [automated quantitative analysis (AQUA)].

Results: The mean follow-up time of the patients was 34.3 months. Patients with low Fédération Internationale des Gynaecologistes et Obstetristes stage were more likely to have low nuclear p27 expression (P = 0.008). Low nuclear p27 expression was associated with improved 3-year overall survival (66% versus 20%, P = 0.0047) and disease-free survival (27% versus 12%, P = 0.022). In multivariable analysis, adjusting for well-characterized prognostic variables, low nuclear p27 expression level was the most significant prognostic factor for both disease-free and overall survival.

Conclusions: Our results indicate that quantitative assessment of nuclear p27 expression level by automated in situ quantitative analysis is a strong predictor for outcome in ovarian cancer.

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