What Do We Know About the Mechanisms of Aromatase Inhibitor Resistance?

Overview

Journal J Steroid Biochem Mol Biol

Specialties Biochemistry
Molecular Biology

Date 2006 Oct 24

PMID 17055257

Citations 30

Authors

Shiuan Chen

Selma Masri

Xin Wang

Sheryl Phung

Yate-Ching Yuan

Xiwei Wu

Affiliations

Soon will be listed here.

Abstract

Clinical trials have demonstrated the importance of aromatase inhibitor (AI) therapy in the effective treatment of hormone-dependent breast cancers. Yet, as with all prolonged drug therapy, resistance to aromatase inhibitors does develop. To date, the precise mechanism responsible for resistance to aromatase inhibitors is not completely understood. In this paper, several mechanisms of de novo/intrinsic resistance and acquired resistance to AIs are discussed. These mechanisms are hypothesized based on important findings from a number of laboratories. To better understand this question, our lab has generated, in vitro, breast cancer cell lines that are resistant to aromatase inhibitors. Resistant cell lines were generated over a prolonged period of time using the MCF-7aro (aromatase overexpressed) breast cancer line. These cell lines are resistant to the aromatase inhibitors letrozole, anastrozole and exemestane and the anti-estrogen tamoxifen, for comparison. Two types of resistant cell lines have been generated, those that grow in the presence of testosterone (T) which is needed for cell growth, and resistant lines that are cultured in the presence of inhibitor only (no T). In addition to functional characterization of aromatase and ERalpha in these resistant cell lines, microarray analysis has been employed in order to determine differential gene expression within the aromatase inhibitor resistant cell lines versus tamoxifen, in order to better understand the mechanism responsible for AI resistance on a genome-wide scale. We anticipate that our studies will generate important information on the mechanisms of AI resistance. Such information can be valuable for the development of treatment strategies against AI-resistant breast cancers.

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