LRH-1 Governs Vital Transcriptional Programs in Endocrine-Sensitive and -Resistant Breast Cancer Cells
Bibliographic record
Abstract
Tumor characteristics are decisive in the determination of treatment strategy for patients with breast cancer. Patients with estrogen receptor α (ERα)-positive breast cancer can benefit from long-term hormonal treatment. Nonetheless, the majority of patients will develop resistance to these therapies. Here, we investigated the role of the nuclear receptor liver receptor homolog-1 (LRH-1, NR5A2) in antiestrogen-sensitive and -resistant breast cancer cells. We identified genome-wide LRH-1-binding sites using ChIP-seq (chromatin immunoprecipitation sequencing), uncovering preferential binding to regions distal to transcriptional start sites. We further characterized these LRH-1-binding sites by integrating overlapping layers of specific chromatin marks, revealing that many LRH-1-binding sites are active and could be involved in long-range enhancer-promoter looping. Combined with transcriptome analysis of LRH-1-depleted cells, these results show that LRH-1 regulates specific subsets of genes involved in cell proliferation in antiestrogen-sensitive and antiestrogen-resistant breast cancer cells. Furthermore, the LRH-1 transcriptional program is highly associated with a signature of poor outcome and high-grade breast cancer tumors in vivo. Herein, we report the genome-wide location and molecular function of LRH-1 in breast cancer cells and reveal its therapeutic potential for the treatment of breast cancers, notably for tumors resistant to treatments currently used in therapies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".