Research Resource: Enhanced Genome-Wide Occupancy of Estrogen Receptor α by the Cochaperone p23 in Breast Cancer Cells
Bibliographic record
Abstract
p23 is a chaperone with multiple heat shock protein 90 dependent and independent cellular functions, including stabilizing unliganded steroid receptors and modulating receptor-DNA dynamics. p23 protein is also up-regulated in several cancers, notably breast cancer. We previously demonstrated that higher expression of p23 in the estrogen-dependent breast cancer line MCF-7 (MCF-7+p23) selectively increased estrogen receptor (ER) target gene transcription and ER recruitment to regulatory elements, promoted cell invasion, and predicted a poor prognosis in breast cancer patients. To probe the impact of p23 on ER binding throughout the human genome, we compared ER occupancy in MCF-7+p23 cells relative to MCF-7-control cells by using chromatin immunoprecipitation followed by ultrahigh-throughput DNA sequencing in the absence and presence of 17β-estradiol (E2) treatment. We found that increased expression of p23 resulted in a 230% increase in the number of E2-induced ER-binding sites throughout the genome compared with control cells and also increased ER binding under basal conditions. Motif analysis indicated that ER binds to a similar DNA sequence regardless of p23 status. We also observed that ER tends to bind closer to genes that were induced, rather than repressed by either E2 treatment or p23 overexpression. Interestingly, we also found that the increased invasion of MCF-7+p23 cells was not only p23 dependent but also ER dependent. Thus, a small increase in the expression of p23 amplifies ER-binding genome wide and, in combination with ER, elicits an invasive phenotype. This makes p23 an attractive target for combating tumor cell metastasis in breast cancer patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".