Transcriptional Control of the <i>ERBB2</i> Amplicon by ERRα and PGC-1β Promotes Mammary Gland Tumorigenesis
Bibliographic record
Abstract
Overexpression of ERBB2 and its neighboring genes on chromosome 17 occurs in approximately 25% of breast tumors and is associated with poor prognosis. While amplification of the 17q12-21 chromosomal region often correlates with an increase in the transcriptional rates of the locus, the molecular mechanisms and the factors involved in the coordinated expression of genes residing within the ERBB2 amplicon remain largely unknown. Here we demonstrate that estrogen-related receptor α (ERRα, NR3B1) and its coregulator PGC-1β are key effectors in this process. Using a mouse model of ERBB2-initiated mammary tumorigenesis, we first show that ablation of ERRα significantly delays ERBB2-induced tumor development and lowers the levels of amplicon transcripts. Chromosome 17q-wide binding site location analyses in human breast cancer cells show preferential recruitment of ERRα to DNA segments associated with the ERBB2 amplicon. Furthermore, ERRα directs the co-recruitment of the coactivator PGC-1β to segments in the 17q12 region and the recruitment of RNA polymerase II to the promoters of the ERBB2 and coamplified genes. ERRα and PGC-1β also participate in the de-repression of ERBB2 expression through competitive genomic cross-talk with estrogen receptor α (ERα) and, as a consequence, influence tamoxifen sensitivity in breast cancer cells. Taken together, our results suggest that ERRα and PGC-1β are key players in the etiology of malignant breast cancer by coordinating the transcriptional regulation of genes located in the 17q12 region, a process that also involves interference with the repressive function of ERα on ERBB2 expression.
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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 | 0.000 |
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 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".