Efficacy and Safety of Anti-HER2 Targeted Therapy for Metastatic HR-Positive and HER2-Positive Breast Cancer: A Bayesian Network Meta-Analysis
Bibliographic record
Abstract
Despite the development of HER2-targeted drugs, achieving favorable outcomes for patients with HR+/HER2+MBC remains challenging. This study utilized Bayesian Network Meta-analysis to compare the efficacy and safety of anti-HER2 combination regimens. The primary analysis focused on progression-free survival (PFS), while secondary analyses included objective response rate, overall survival (OS) and the incidence rate of grade 3/4 adverse events (AEs). A comprehensive search across seven databases identified 25 randomized controlled trials for inclusion in this meta-analysis. For patients eligible for endocrinotherapy, our findings revealed that dual-target combined endocrine therapy, such as Her2-mAb+Her2-mAb+Endo (HR = 0.38; 95%CrI: 0.16-0.88) and Her2-mAb+Her2-tki+Endo (HR = 0.45; 95%CrI: 0.23-0.89), significantly improved PFS compared to endocrine therapy alone. According to the surface under the cumulative ranking curves (SUCRAs), Her2-mAb+Her2-mAb+Endo and Her2-mAb+Her2-tki+Endo ranked highest in terms of PFS and OS, respectively. For patients unsuitable for endocrine therapy, anti-HER2 dual-target combined chemotherapy, such as Her2-mAb+Her2-mAb+Chem (HR = 0.76; 95%CrI: 0.6-0.96) and Her2-mAb+Her2-tki+Chem (HR = 0.48; 95%CrI: 0.29-0.81), demonstrated significant improvements in PFS compared to Her2-mAb+Chem. The results were the same when compared with Her2-tki+Chem. According to the SUCRAs, Her2-mAb+Her2-tki+Chem and Her2-mAb+Her2-mAb+Chem ranked highest for PFS and OS, respectively. Subgroup analyses consistently supported these overall findings, indicating that dual-target therapy was the optimal approach irrespective of treatment line.
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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 | 0.027 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.041 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".