Divergent ERα co-factor landscapes in gynecological cancers: implications for disease progression and therapy
Bibliographic record
Abstract
Estrogen receptor alpha (ERα) is an established biomarker for breast tumors, the loss of which is associated with poor cancer progression. Over 70% of breast cancers express ERα and targeting this protein has helped stem the progress of breast cancer. Therefore, it is paradoxical that only a small fraction of patients with ovarian and uterine cancers, which express ERα, are insensitive to antiestrogenic therapies. We propose the hypothesis that ERα association with different cofactors dictates the susceptibility of these cancers to therapies. To support this hypothesis, we analyzed data from cBioportal patient samples and showed that a strong positive correlation exists between ERα and its cofactors GATA3 and FOXA1 in breast cancer, but not in ovarian and uterine cancers. We further show that ERα genomic localization differs in the three cancer types, using available ChIP-seq datasets. Together, our analyses suggest that both localization and the nature of co-factors might be relevant for driving ERα-dependent cancer progression in different cell environments. We further discuss potential mechanisms for these differences in this commentary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".