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Identification of genomic features that uniquely impact estrogen receptor alpha binding and its effects on gene expression in endometrial cancer [ChIP-seq]

GSE334097 Homo sapiens Genome binding/occupancy profiling by high throughput sequencing 7 samples Submitted 2026/06/03 Platform GPL24676Platform GPL34281
Summary
Estrogen receptor alpha (ER) is an established oncogenic transcription factor in breast and endometrial cancer; however, more is known about the mechanisms controlling ER behavior in breast cancer, and therapies targeting ER have been much more successful in breast cancer. To address this disparity, we seek to identify genomic features that control ER in endometrial cancer and determine to what extent these factors differ from those in breast cancer. We focus on the locations of estrogen response elements (EREs), ER’s preferred DNA binding motif, throughout the human genome. To identify factors that predict ER genomic binding and effects on target gene expression, we apply machine learning to genomic data for each ERE in Ishikawa cells (ER+ endometrial cancer) and T-47D cells (ER+ breast cancer). Many of these factors, such as chromatin accessibility and histone modifications, are predictive of ER activity in both cell lines. However, the transcription factors that predict ER activity are found to be cell type-specific, including FOXA1 and GATA3 in T-47D cells, and ETV4 and SOX17 in Ishikawa cells. In addition, the features that predict ER binding and effects on gene expression differ, with transcription at EREs in the absence of estrogen being predictive of regulatory activity. To verify our findings in the Ishikawa cells, we perform a CRISPR knockout screen, which confirms the discovery that SOX17 controls ER activity in endometrial cancer cells. These results identify important genomic features of ER binding and regulatory activity and how these features differ between endometrial cancer and breast cancer cells.
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Direct links to NCBI, no account and no request form: the whole study as GSE334097_RAW.tar, processed values as the series matrix, the supplementary file directory, and per-sample supplementary files for any of the 7 samples. Raw sequencing reads are also available from ENA.

Also filed as BioProject PRJNA1473723 and SRA study SRP706143. Searching any of these in the dataset finder brings you back here.

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