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Genome-wide Estrogen Receptor-? activation is sustained, not cyclical.

ABSTRACT: Estrogen Receptor-alpha (ER) drives 75% of breast cancers. Stimulation of the ER by estra-2-diol forms a transcriptionally-active chromatin-bound complex. Previous studies reported that ER binding follows a cyclical pattern. However, most studies have been limited to individual ER target genes and without replicates. Thus, the robustness and generality of ER cycling are not well understood. We present a comprehensive genome-wide analysis of the ER after activation, based on 6 replicates at 10 time-points, using our method for precise quantification of binding, Parallel-Factor ChIP-seq. In contrast to previous studies, we identified a sustained increase in affinity, alongside a class of estra-2-diol independent binding sites. Our results are corroborated by quantitative re-analysis of multiple independent studies. Our new model reconciles the conflicting studies into the ER at the TFF1 promoter and provides a detailed understanding in the context of the ER's role as both the driver and therapeutic target of breast cancer.

SUBMITTER: Holding AN

PROVIDER: S-EPMC6287946 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Genome-wide Estrogen Receptor-α activation is sustained, not cyclical.

Holding Andrew N AN Cullen Amy E AE Markowetz Florian F

eLife 20181120

Estrogen Receptor-alpha (ER) drives 75% of breast cancers. Stimulation of the ER by estra-2-diol forms a transcriptionally-active chromatin-bound complex. Previous studies reported that ER binding follows a cyclical pattern. However, most studies have been limited to individual ER target genes and without replicates. Thus, the robustness and generality of ER cycling are not well understood. We present a comprehensive genome-wide analysis of the ER after activation, based on 6 replicates at 10 ti ...[more]

PMID: 30457555

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Project description:BackgroundThe fallopian tube epithelium is one of the potential sources of high-grade serous ovarian cancer (HGSC). The use of estrogen only hormone replacement therapy increases ovarian cancer (OVCA) risk. Despite estrogen's influence in OVCA, selective estrogen receptor modulators (SERMs) typically demonstrate only a 20 % response rate. This low response could be due to a variety of factors including the loss of estrogen receptor signaling or the role of estrogen in different potential cell types of origin. The response of fallopian tube epithelium to SERMs is not known, and would be useful when determining therapeutic options for tumors arising from this cell type, such as HGSC.ResultsUsing normal murine derived oviductal epithelial cells (mouse equivalent to the fallopian tube) estrogen receptor expression was confirmed and interaction with its ligand, estradiol, triggered mRNA and protein induction of progesterone receptor (PR). The SERMs 4-hydroxytamoxifen, raloxifene and desmethylarzoxifene, functioned as estrogen receptor antagonists in oviductal cells. Cellular proliferation and migration assays suggested that estradiol does not significantly impact cellular migration and increased proliferation. Further, using RNAseq, the oviduct specific transcriptional genes targets of ER when stimulated by estradiol and 4-hydroxytamoxifen signaling were determined and validated. The RNA-seq revealed enrichment in proliferation, anti-apoptosis, calcium signaling and steroid signaling processes. Finally, the ER and PR receptor status of a panel of HGSC cell lines was investigated including Kuramochi, OVSAHO, OVKATE, OVCAR3, and OVCAR4. OVSAHO demonstrated receptor expression and response, which highlights the need for additional models of ovarian cancer that are estrogen responsive.ConclusionsOverall, the fallopian tube has specific gene targets of estrogen receptor and demonstrates a tissue specific response to SERMs consistent with antagonistic action.

Dataset Information

Genome-wide Estrogen Receptor-? activation is sustained, not cyclical.

Publications

Genome-wide Estrogen Receptor-α activation is sustained, not cyclical.

Similar Datasets

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