Letrozole-, anastrozole-, and tamoxifen-responsive genes in MCF-7aro cells: a microarray approach.

Antiestrogens and aromatase inhibitors are important drugs in the treatment of estrogen-dependent breast cancer. To investigate the effects of these drugs on gene expression in breast cancer cells, we treated estrogen receptor-positive MCF-7 cells stably transfected with the aromatase gene (known as MCF-7aro cells) with testosterone, 17 beta-estradiol, two aromatase inhibitors (letrozole and anastrozole), and an antiestrogen (tamoxifen). We found that testosterone or 17 beta-estradiol induced the proliferation of MCF-7aro cells at a rate six times faster than the untreated cells. In addition, the testosterone-induced proliferation of MCF-7aro cells was effectively suppressed by letrozole, anastrozole, or tamoxifen. Microarray analyses on Affymetrix Human Genome U133A GeneChips (Affymetrix, Santa Clara, CA) were carried out using total RNA isolated from the control and treated cells. At the false discovery rate of 0.05 and a minimum fold-change criteria of 1.5, 104 genes were identified that were up-regulated and 109 genes were identified that were down-regulated by both androgen and estrogen. More than 50% of these hormone-regulated genes were counter-regulated by all three inhibitors and >90% were counter-regulated by at least one of the inhibitors. Comparing the effect of each inhibitor on gene expression, we observed that letrozole and anastrozole are more similar in terms of the genes they affect compared with treatment with tamoxifen. To validate the gene expression profiles identified from microarray analyses, the expression patterns of 13 representative genes were examined by Northern analysis. Finally, the genes identified as statistically significant were classified based on their expression patterns and biological function/pathways. The results of this study provide us with a better understanding of the actions of both aromatase inhibitors and antiestrogens at the molecular level. We believe that the results of this study serve as the first step in identifying unique expression patterns following drug treatment, and that this will ultimately be useful in customizing patient treatment strategies for hormone-dependent breast cancer.