Treating ER-positive breast cancer: a review of the current FDA-approved SERMs and SERDs and their mechanisms of action
Bibliographic record
Abstract
Breast cancer is one of the most significant causes of mortality among women and the second most prevalent cancer worldwide. Estrogen receptor (ER)-positive breast cancers are the most common molecular subtype of breast cancer, comprising about 70% of breast carcinoma diagnoses worldwide. Endocrine therapy is the foremost strategy for the treatment of ER-positive breast cancer. In the United States, the Food and Drug Administration (FDA) has approved endocrine therapies for ER-positive breast cancers that include selective estrogen receptor modulators (SERMs), selective estrogen receptor downregulators/degraders (SERDs) and aromatase inhibitors (AIs). The approved SERMS, tamoxifen, toremifene and raloxifene, are the gold-standard treatments. The only FDA-approved SERD available for treating ER and hormone-positive breast cancers is fulvestrant, and various generations of AIs, including exemestane, letrozole, and anastrozole, have also received FDA approval. Herein, we review the major FDA-approved SERMs and SERDs for treating ER-positive breast cancer, focusing on their mechanisms of action. We also explore molecular events that contribute to the resistance of these drugs to endocrine therapies and combinational strategies with drugs such as cyclin-dependant kinases 4/6 (CDK4/6) inhibitors in clinical trials to combat endocrine drug 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".