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Subcellular localization and protein levels of cyclin-dependent kinase inhibitor p27 independently predict for survival in epithelial ovarian cancer

  • Amanda Psyrri
  • , Aris Bamias
  • , Ziwei Yu
  • , Paul M. Weinberger
  • , Mohamad Kassar
  • , Sophia Markakis
  • , Diane Kowalski
  • , Eleni Efstathiou
  • , Robert L. Camp
  • , David L. Rimm
  • , Meletios A. Dimopoulos

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)8384-8390
Number of pages7
JournalClinical Cancer Research
Volume11
Issue number23
DOIs
StatePublished - Dec 1 2005
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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