Efficacy and safety of inetetamab-containing regimens in patients with HER2-positive metastatic breast cancer in first-line/second-line setting
Bibliographic record
Abstract
Background Inetetamab is a novel recombinant humanized anti-Human epidermal growth factor receptor 2 (HER2) monoclonal antibody. This real-world retrospective study assessed the efficacy and safety of inetetamab-containing regimens in first-line/second-line treatment of HER2-positive metastatic breast cancer (MBC). Methods This study retrospectively recruited HER2-positive MBC patients who received inetetamab- containing regimens from June 2020 to May 2023. The outcomes included progression-free survival (PFS), objective response rate (ORR), and disease control rate (DCR). Results A total of 329 patients were enrolled and included in the efficacy analysis. The most frequently used treatment strategy was contained inetetamab plus pyrotinib (205/329, 62.3%). Patients treated with first-line regimens benefited the most, with a median PFS of 15.0 versus (vs.) 10.0 months (first-line- vs. second-line inetetamab plus pyrotinib, p <0.001), 19.0 vs. 17.0 months (first-line- vs. second-line inetetamab plus pertuzumab, p=0.096), and 13.0 vs. not reached months (first-line- vs. second-line inetetamab plus chemotherapy, p=0.229). The complete response (CR) was observed in 16 (4.9%) patients of all cohort, with the ORR was 51.1% (95% confidence interval [CI], 45.7%-56.4%), and the DCR was 96.4% (95% CI, 93.7%-97.9%). The grade 3 or higher adverse events (AEs) were observed in 29.5% of the whole study cohort. Diarrhea (39.2%), white blood cell count decreased (33.0%), and myelosuppression (18.6%) as the most frequent ones. Conclusions Following the first- and second-line of treatment, inetetamab- containing combinations demonstrated promising clinical activity and a manageable safety profile in patients with HER2-positive MBC, especially in the first-line treatment.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".