Accelerating drug access from advanced to early breast cancer: the special case of oral selective estrogen receptor degraders
Bibliographic record
Abstract
PURPOSE OF REVIEW: For hormone receptor positive breast cancer, the development of endocrine resistance commonly occurs, presenting as either disease progression in the metastatic setting or recurrence during or following adjuvant endocrine therapy. Various mechanisms of resistance have been described. In order to reduce or overcome endocrine resistance, there has been substantial interest in developing potent and orally bioavailable selective estrogen receptor degraders (SERDs) for metastatic disease and select patients with early-stage estrogen receptor positive breast cancer. RECENT FINDINGS: At least 11 oral SERDs have entered clinical development. We review current studies in both the metastatic and neoadjuvant/adjuvant setting and present the available evidence of benefit and toxicity for these novel agents. Further characterization of changes to tissue-based biomarkers such as estrogen receptor, progesterone receptor and Ki67 expression and blood-based biomarkers such as ctDNA and estrogen receptor 1 mutation may help to refine therapeutic strategies, combinations, and patient selection to identify women who are most likely to benefit from these novel endocrine agents. SUMMARY: Although SERDs have clear therapeutic potential based on nonclinical studies and have demonstrated early signs of activity in phase I and II studies in the metastatic setting, ongoing research is needed to clarify when and in whom these agents may have greatest clinical benefit.
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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 | 0.001 |
| 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) | 0.004 | 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".