Filling the Gap after CDK4/6 Inhibitors: Novel Endocrine and Biologic Treatment Options for Metastatic Hormone Receptor Positive Breast Cancer
Bibliographic record
Abstract
The rise of cyclin-dependent kinase (CDK)4/6 inhibitors has rapidly reshaped treatment algorithms for hormone receptor (HR)-positive metastatic breast cancer, with endocrine treatment (ET) plus a CDK4/6-inhibitor currently representing the standard of care in the first line setting. However, treatment selection for those patients experiencing progression while on ET + CDK4/6-inhibitors remains challenging due to the suboptimal activity or significant toxicities of the currently available options. There is also a paucity of data regarding the efficacy of older regimens, such as everolimus + exemestane, post-CDK4/6 inhibition. In this setting of high unmet need, several clinical trials of novel drugs have recently reported encouraging results: the addition of the AKT-inhibitor capivasertib to fulvestrant demonstrated a significant improvement in progression-free survival (PFS); the oral selective estrogen receptor degrader (SERD) elacestrant prolonged PFS compared to traditional ET in a phase 3 trial, particularly among patients with detectable ESR1 mutations; finally, PARP inhibitors are available treatment options for patients with pathogenic BRCA1/2 germline mutations. Overall, a plethora of novel endocrine and biologic treatment options are finally filling the gap between first-line ET and later line chemotherapy. In this review article, we recapitulate the activity of these novel treatment options and their potential role in future treatment algorithms.
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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) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".