4.5 Article Proceedings Paper

New experimental models for aromatase inhibitor resistance

期刊

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jsbmb.2007.05.020

关键词

models; aromatase inhibitor resistance

资金

  1. NCI NIH HHS [R01 CA044735, F31 CA123691, CA44735, R01 CA044735-18, CA123691] Funding Source: Medline
  2. NIEHS NIH HHS [R01 ES008258, R01 ES008258-09] Funding Source: Medline

向作者/读者索取更多资源

Clinical trials have demonstrated the importance of aromatase inhibitor (AI) therapy in the effective treatment of hormone-dependent breast cancers. In contrast to tamoxifen, an antagonist of the estrogen receptor (ER), AIs have shown to be better tolerated along with decreased recurrence rates of the disease. Currently, three third-generation AIs are being used: exemestane, letrozole, and anastrozole. Our laboratory is attempting to understand several aspects of AI functionality. In this paper, we first review recent findings from our structure-function studies of aromatase as well as the molecular characterization of the interaction between AIs and aromatase. Based on these studies, we propose new evidence for the interaction of letrozole and exemestane with aromatase. In addition, we will discuss recent results generated from our AI-resistant cell lines. Our laboratory has generated MCF-7aro cells that are resistant to letrozole, anastrozole, exemestane, and tamoxifen. Basic functional characterization of aromatase and ER(x in these resistant cell lines has been done and microarray analysis has been employed in order to better understand the mechanism responsible for AI resistance on a genome-wide scale. The results generated so far suggest the presence of at least four types of resistant cell lines. Overall, the information presented in this paper supplements our understanding of AI function, and such information can be valuable for the development of treatment strategies against AI resistant breast cancers. (C) 2007 Elsevier Ltd. All rights reserved.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据