Decoding estrogen receptor and GPER biology: structural insights and therapeutic advances in ERα−positive breast cancer
Bibliographic record
Abstract
Classical estrogen receptors, ERα and ERβ, along with the membrane-bound G-protein-coupled estrogen receptor (GPER), play critical roles in driving ERα−positive breast cancer (BC). Clinical management of this subtype relies on endocrine therapy (ET), which targets ER signaling through selective estrogen receptors modulators (SERMs), degraders (SERDs), and aromatase inhibitors (AIs). While ET has significantly reduced recurrence and mortality rates, acquired resistance remains a major therapeutic challenge. Activating ESR1 mutations, which encode constitutively active ERα variants, are detected in 30-50% of therapy-resistant metastatic ERα−positive BC and serve as emerging biomarkers of poor prognosis. These hot-spot mutations stabilize ERα in its agonist conformation, thereby enabling ligand-independent transcriptional activation. Understanding the conformational constraints that keep wild-type ERα in an “off-state” in the absence of ligand—and how activating ESR1 mutations disrupt these regulatory mechanisms—is critical for developing effective targeted therapies. Concurrently, GPER-mediated non-genomic signaling, often inadvertently activated by SERMs and SERDs, contributes to tamoxifen resistance. This review explores the structural and functional intricacies of ERα, the impact of ESR1 mutations on its ligand-binding domain (ERα−LBD) and their contribution to ET resistance, and the role of GPER-mediated signaling in ERα−positive BC. We further highlight recent advances in next-generation therapeutics targeting both ERα mutants and GPER, which may offer a more effective, integrated strategy to overcome ET resistance.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".