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题目:: Molecular response to aromatase inhibitor treatment in primary breast cancer.
作者:: Mackay(Alan),Urruticoechea(Ander),Dixon(J Michael),Dexter(Tim),Fenwick(Kerry),Ashworth(Alan),Drury(Suzanne),Larionov(Alexey),Young(Oliver),White(Sharon),Miller(William R),Evans(Dean B),Dowsett(Mitch)
状态:: 发布时间2007-07-30 , 更新时间 2014-11-20
期刊:: Breast Cancer Res
摘要:: Aromatase inhibitors such as anastrozole and letrozole are highly effective suppressants of estrogen synthesis in postmenopausal women and are the most effective endocrine treatments for hormone receptor positive breast cancer in such women. Little is known of the molecular effects of these agents on human breast carcinomas in vivo.,We randomly assigned primary estrogen receptor positive breast cancer patients to treatment with anastrozole or letrozole for 2 weeks before surgery. Expression profiling using cDNA arrays was conducted on pretreatment and post-treatment biopsies. Sample pairs from 34 patients provided sufficient RNA for analysis.,Profound changes in gene expression were seen with both aromatase inhibitors, including many classical estrogen-dependent genes such as TFF1, CCND1, PDZK1 and AGR2, but also many other genes that are likely to represent secondary responses; decrease in the expression of proliferation-related genes were particularly prominent. Many upregulated genes are involved in extracellular matrix remodelling, including collagens and members of the small leucine-rich proteoglycan family (LUM, DCN, and ASPN). No significant differences were seen between letrozole and anastrozole in terms of molecular effects. The gene changes were integrated into a Global Index of Dependence on Estrogen (GIDE), which enumerates the genes changing by at least twofold with therapy. The GIDE varied markedly between tumours and related significantly to pretreatment levels of HER2 and changes in immunohistochemically detected Ki67.,Our findings identify the transcriptional signatures associated with aromatase inhibitor treatment of primary breast tumours. Larger datasets using this approach should enable identification of estrogen-dependent molecular changes, which are the determinants of benefit or resistance to endocrine therapy.
语言:: eng
DOI:

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