Efficacy and Safety of Abemaciclib in Combination With Endocrine Therapy for HR+/HER2− Advanced or Metastatic Breast Cancer
Bibliographic record
Abstract
OBJECTIVES: Breast cancer, particularly the hormone receptor-positive (HR+) and human epidermal growth factor receptor 2-negative (HER2-) subtype, remains a major global health concern. Abemaciclib, a CDK4/6 inhibitor, has shown promising results in treating advanced cases. This study comprehensively assesses the efficacy and safety of abemaciclib in combination with endocrine therapy for HR+/HER2- advanced or metastatic breast cancer. METHODS: Following PRISMA guidelines, a systematic review and meta-analysis was conducted. A thorough literature search was conducted on PubMed, EMBASE, Cochrane Library, and ClinicalTrials.gov til December 2023. Inclusion criteria encompassed randomized controlled trials and retrospective cohort studies reporting on abemaciclib in approved doses, either as monotherapy or in combination. Outcome assessments included progression-free survival (PFS), overall response rate (ORR), side effects/adverse effects (SE/AE), and overall survival (OS). Quality assessment utilized Cochrane's revised risk of bias tool and Newcastle-Ottawa scale. RESULTS: Pooled results of 22 studies involving 14,010 patients revealed that abemaciclib significantly improved PFS (hazard ratio=0.53; 95% CI: 0.48-0.59; P =0.00; I 2 =0%), ORR (risk ratio=2.31; 95% CI: 1.93-2.75; P =0.00; I 2 =0%), and OS (risk ratio=0.76 (95% CI: 0.65-0.87; P =0.001; I 2 =0%). However, abemaciclib increased the risk of adverse events in the fulvestrant and nonsteroidal aromatase inhibitor (NSAI) combinations, respectively. CONCLUSIONS: Abemaciclib, particularly in combination with fulvestrant, emerges as an effective therapeutic option for HR+/HER2- advanced or metastatic breast cancer, improving PFS and OS. The higher toxicity profile warrants cautious use, especially in treatment-naive patients.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".