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Automated quantitative analysis of DCC tumor suppressor protein in ovarian cancer tissue microarray shows association with {beta}-catenin levels and outcome in patients with epithelial ovarian cancer.

Bamias A, Yu Z, Weinberger P, Markakis S, Kowalski D, Camp R, Rimm D, Dimopoulos M, Psyrri A

Department of Clinical Therapeutics, University of Athens, School of Medicine, Athens, Greece; Departments of.

BACKGROUND: The deleted in colorectal cancer (DCC) protein, the product of DCC tumor suppressor gene, is frequently altered in cancer. Preclinical data demonstrate that DCC regulates beta-catenin levels. Here, we sought to determine the association of DCC with beta-catenin protein levels, clinicopathological parameters and patient outcome in ovarian cancer using a method of in situ compartmentalized protein analysis. METHODS: A tissue array composed of 150 advanced-stage ovarian cancers, treated with surgical debulking and platinum-paclitaxel (Taxol) combination chemotherapy, was constructed. For evaluation of protein expression, we used an immunofluorescence-based method of automated in situ quantitative measurement of protein analysis (AQUA). RESULTS: One hundred and twelve patients (74%) had sufficient tissue for AQUA. The median follow-up time for the entire cohort was 33 months. Patients with low nuclear DCC expression had a 3-year progression-free survival (PFS) rate of 0% compared with 33% of those with high DCC expression (P = 0.0067). In multivariate analysis, low nuclear DCC expression level retained its prognostic significance for PFS. Between DCC and beta-catenin, a significant relationship was found, where tumors with low DCC had low beta-catenin and vice versa (P = 0.003). CONCLUSIONS: Low nuclear DCC levels predict for poor patient outcome in epithelial ovarian cancer. DCC may exert its antitumor function, in part, through regulation of beta-catenin levels.

Published 7 December 2006 in Ann Oncol, 17(12): 1797-802.
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Microarrays Books

Analysis of Microarray Gene Expression Data (Trends in Logic)

Analysis of Microarray Gene Expression Data (Trends in Logic)