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Cited by 351

The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.

Estrogen-Receptor Biology: Continuing Progress and Therapeutic Implications

Osborne, C. Kent, Schiff, Rachel·2005·Journal of Clinical Oncology

Abstract

Endocrine therapies, first used more than 100 years ago, are the most effective treatments for breast cancers expressing the estrogen receptor (ER). All endocrine therapies are designed to block ER function in some way, thereby making them the first targeted therapies used for cancer. Selective ER modulators (SERMs) such as tamoxifen bindERandpartially block its activity.Ovarian ablation, luteinizing hormone–releasing hormone agonists, and aromatase inhibitors reduce the level of estrogen and inhibit ligand-induced activation of ER. Steroidal antiestrogens such as fulvestrant bind ER, more completely block its function, and induce receptor degradation.While all of these therapies are effective in certain patients, de novo and acquired resistance remain major problems. New information on the biology of ER provides insight into the mechanisms of treatment resistance and new strategies to overcome it, thereby potentially making these therapies even more effective.

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Metadata

Authors
Osborne, C. K., & Schiff, R.
Venue
Journal of Clinical Oncology
Published
2005-03-10
Volume
23
Issue
8
Pages
1616-1622
ISSN
  • 0732-183X (ISSN-L)
  • 1527-7755
OpenAlex
W2135278339
Cited by
351
References
56
FWCI
17.31
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Estrogen-Receptor Biology: Continuing Progress and Therapeutic Implications. https://app.cochat.ai/papers/10.1200/jco.2005.10.036
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Osborne, C. K., & Schiff, R. (2005). Estrogen-Receptor Biology: Continuing Progress and Therapeutic Implications. Journal of Clinical Oncology. https://doi.org/10.1200/jco.2005.10.036

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.