1. ¹Centre de Recherche of the Center hospitalier de l'Université de Montréal (CR-CHUM)/Institut du cancer de Montréal, Montreal, Quebec, Canada H2L 4M1
2. ²Departement de medicine, Université de Montréal, Montreal, Quebec, Canada H2L 4M1
3. ³McGill University and Genome Quebec Innovation Center, Montreal, Quebec, Canada H2L 4M1
4. ⁴Departments of Human Genetics and Medicine, McGill University, Montreal, Quebec, Canada H2L 4M1
5. ⁵The Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada H2L 4M1
6. ⁶Division de gynecologie et obstetrique/Université de Montréal, Montreal, Quebec, Canada H2L 4M1

Correspondence: Professor A-M Mes-Masson, Centre de Recherche du Centre hospitalier de l'Université de Montréal (CHUM)/Institut du cancer de Montréal, 1560, rue Sherbrooke est, Montreal, Quebec, Canada H2L 4M1. E-mail: anne-marie.mes-masson@umontreal.ca

Received 17 August 2005; Revised 17 November 2005; Accepted 29 November 2005; Published online 17 January 2006.

Abstract

In order to elucidate the biological variance between normal ovarian surface epithelial (NOSE) and epithelial ovarian cancer (EOC) cells, and to build a molecular classifier to discover new markers distinguishing these cells, we analysed gene expression patterns of 65 primary cultures of these tissues by oligonucleotide microarray. Unsupervised clustering highlights three subgroups of tumours: low malignant potential tumours, invasive solid tumours and tumour cells derived from ascites. We selected 18 genes with expression profiles that enable the distinction of NOSE from these three groups of EOC with 92% accuracy. Validation using an independent published data set derived from tissues or primary cultures confirmed a high accuracy (87–96%). The distinctive expression pattern of a subset of genes was validated by quantitative reverse transcription–PCR. An ovarian-specific tissue array representing tissues from NOSE and EOC samples of various subtypes and grades was used to further assess the protein expression patterns of two differentially expressed genes (Msln and BMP-2) by immunohistochemistry. This study highlights the relevance of using primary cultures of epithelial ovarian cells as a model system for gene profiling studies and demonstrates that the statistical analysis of gene expression profiling is a useful approach for selecting novel molecular tumour markers.