Gene expression profiling of primary breast carcinomas using arrays of candidate genes

Breast cancer is characterized by an important histoclinical heterogeneity that currently hampers the selection of the most appropriate treatment for each case. This problem could be solved by the identification of new parame ters that better predict the natural history of the disease and its sensiti vity to treatment. A large-scale molecular characterization of breast cance r could help in this context. Using cDNA arrays, we studied the quantitativ e mRNA expression levels of 176 candidate genes in 34 primary breast carcin omas along three directions: comparison of tumor samples, correlations of m olecular data with conventional histoclinical prognostic features and gene correlations. The study evidenced extensive heterogeneity of breast tumors at the transcriptional level. A hierarchical clustering algorithm identifie d two molecularly distinct subgroups of tumors characterized by a different clinical outcome after chemotherapy. This outcome could not have been pred icted by the commonly used histoclinical parameters. No correlation was fou nd with the age of patients, tumor size, histological type and grade. Howev er, expression of genes was differential in tumors with lymph node metastas is and according to the estrogen receptor status; ERBB2 expression was stro ngly correlated with the lymph node status (P < 0.0001) and that of GATA3 w ith the presence of estrogen receptors (P < 0.001). Thus, our results ident ified new ways to group tumors according to outcome and new potential targe ts of carcinogenesis. They show that the systematic use of cDNA array testi ng holds great promise to improve the classification of breast cancer in te rms of prognosis and chemosensitivity and to provide new potential therapeu tic targets.