Prospective Cohort Study of Combination Therapy With Abemaciclib and Hormonal Therapy for Chemotherapy-Treated Patients With Hormone Receptor-Positive Metastatic Breast Cancer
Bibliographic record
Abstract
Background: Combination therapy with cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitors and hormonal therapy as the first-line and second-line treatments has already been shown to be effective in patients with hormone receptor-positive (HR+) human epidermal growth factor receptor 2-negative (HER2-) metastatic breast cancer (MBC) in clinical trials. On the other hand, in clinical practice, CDK4/6 inhibitors are used not only as first-/second-line but also as later-line hormonal therapies, or for patients receiving prior chemotherapy in metastatic setting. However, the efficacy and safety of combination therapy in these patients remain unclear. In this study, we evaluate the clinical efficacy and safety of combination therapy with abemaciclib and hormonal therapy for chemotherapy-treated patients with HR+ HER2- MBC. Methods: This multi-institutional prospective cohort study will involve a total of 300 chemotherapy-treated patients with HR+ HER2- MBC. The primary endpoint is progression-free survival (PFS). Secondary endpoints include overall survival, time to treatment failure, response rate, clinical benefit rate, and adverse events. The preplanned subpopulation analysis is the number of chemotherapy regimens for HR+ HER2- MBC (two or less vs. three or more), prior treatment history with CDK4/6 inhibitors other than abemaciclib (presence vs. absence) and menopausal status (pre vs. post). We also planned to determine PFS of the subpopulation treated with abemaciclib as maintenance therapy after chemotherapy. Discussion: In this multi-institutional prospective cohort study, we evaluate the clinical efficacy and safety of combination therapy with abemaciclib and hormonal therapy for chemotherapy-treated patients with HR+ HER2- MBC. We also evaluate this combination therapy as maintenance therapy in patients who respond to early-line chemotherapy.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".