| Title |
Overcoming aromatase inhibitor resistance in breast cancer: possible mechanisms and clinical applications
|
|---|---|
| Published in |
Breast Cancer, April 2017
|
| DOI | 10.1007/s12282-017-0772-1 |
| Pubmed ID | |
| Authors |
Toru Hanamura, Shin-ichi Hayashi |
| Abstract |
Estrogen plays crucial roles in the progression of hormone-dependent breast cancers through activation of nuclear estrogen receptor α (ER). Estrogen is produced locally from circulating inactive steroids and adrenal androgens in postmenopausal women. However, conversion by aromatase is a rate-limiting step in intratumoral estrogen production in breast cancer. Aromatase inhibitors (AIs) inhibit the growth of hormone-dependent breast cancers by blocking the conversion of adrenal androgens to estrogen and by unmasking the inhibitory effect of androgens, acting via the androgen receptor (AR). AIs are thus a standard treatment option for postmenopausal hormone-dependent breast cancer. However, although initial use of AIs provides substantial clinical benefit, some breast cancer patients relapse because of the acquisition of AI resistance. A better understanding of the mechanisms of AI resistance may contribute to the development of new therapeutic strategies and aid in the search for new therapeutic targets and agents. We have investigated AI-resistance mechanisms and established six AI-resistant cell lines. Some of them exhibit estrogen depletion-resistance properties via constitutive ER-activation or ER-independent growth signaling. We examined how breast cancer cells can adapt to estrogen depletion and androgen superabundance. Estrogen and estrogenic androgen produced independently from aromatase contributed to cell proliferation in some of these cell lines, while another showed AR-dependent cell proliferation. Based on these findings, currently proposed AI-resistance mechanisms include an aromatase-independent estrogen-producing pathway, estrogen-independent ER function, and ER-independent growth signaling. This review summarizes several hypotheses of AI-resistance mechanisms and discusses how existing or novel therapeutic agents may be applied to treat AI-resistant breast cancers. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 49 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 6 | 12% |
| Researcher | 6 | 12% |
| Student > Ph. D. Student | 5 | 10% |
| Other | 4 | 8% |
| Student > Postgraduate | 3 | 6% |
| Other | 5 | 10% |
| Unknown | 20 | 41% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 10 | 20% |
| Biochemistry, Genetics and Molecular Biology | 7 | 14% |
| Pharmacology, Toxicology and Pharmaceutical Science | 3 | 6% |
| Engineering | 2 | 4% |
| Agricultural and Biological Sciences | 2 | 4% |
| Other | 3 | 6% |
| Unknown | 22 | 45% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 April 2017.
All research outputs
#21,476,880
of 23,975,976 outputs
Outputs from Breast Cancer
#458
of 614 outputs
Outputs of similar age
#274,624
of 312,934 outputs
Outputs of similar age from Breast Cancer
#11
of 16 outputs
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