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Usage Information

Targeting ESR1 mutation–induced transcriptional addiction in breast cancer with BET inhibition
Sm N. Udden, … , Cheng-Ming Chiang, Prasanna G. Alluri
Sm N. Udden, … , Cheng-Ming Chiang, Prasanna G. Alluri
Published July 26, 2022
Citation Information: JCI Insight. 2022;7(17):e151851. https://doi.org/10.1172/jci.insight.151851.
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Research Article Oncology

Targeting ESR1 mutation–induced transcriptional addiction in breast cancer with BET inhibition

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Abstract

Acquired mutations in the ligand-binding domain (LBD) of the gene encoding estrogen receptor α (ESR1) are common mechanisms of endocrine therapy resistance in patients with metastatic ER+ breast cancer. The ESR1 Y537S mutation, in particular, is associated with development of resistance to most endocrine therapies used to treat breast cancer. Employing a high-throughput screen of nearly 1,200 Federal Drug Administration–approved (FDA-approved) drugs, we show that OTX015, a bromodomain and extraterminal domain (BET) inhibitor, is one of the top suppressors of ESR1 mutant cell growth. OTX015 was more efficacious than fulvestrant, a selective ER degrader, in inhibiting ESR1 mutant xenograft growth. When combined with abemaciclib, a CDK4/6 inhibitor, OTX015 induced more potent tumor regression than current standard-of-care treatment of abemaciclib + fulvestrant. OTX015 has preferential activity against Y537S mutant breast cancer cells and blocks their clonal selection in competition studies with WT cells. Thus, BET inhibition has the potential to both prevent and overcome ESR1 mutant–induced endocrine therapy resistance in breast cancer.

Authors

Sm N. Udden, Qian Wang, Sunil Kumar, Venkat S. Malladi, Shwu-Yuan Wu, Shuguang Wei, Bruce A. Posner, Sophie Geboers, Noelle S. Williams, Yulun Liu, Jayesh K. Sharma, Ram S. Mani, Srinivas Malladi, Karla Parra, Mia Hofstad, Ganesh V. Raj, Jose M. Larios, Reshma Jagsi, Max S. Wicha, Ben Ho Park, Gaorav P. Gupta, Arul M. Chinnaiyan, Cheng-Ming Chiang, Prasanna G. Alluri

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Usage data is cumulative from July 2022 through March 2023.

Usage JCI PMC
Text version 4,563 145
PDF 868 47
Figure 606 0
Supplemental data 249 11
Citation downloads 59 0
Totals 6,345 203
Total Views 6,548

Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.

Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.

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