Efficacy and safety of antibody-drug conjugates in triple-negative and HER-2 positive breast cancer: A systematic review and meta-analysis of clinical trials
Bibliographic record
Abstract
Breast cancer (BC) is the 2nd most common cause of cancer-related deaths. Antibody-drug conjugates (ADCs) are monoclonal antibodies linked to cytotoxic agents and are directed towards a specific tumor protein. Therefore, they are more potent and can have relatively less toxicity. In this meta-analysis, we assessed the efficacy and safety of ADCs in breast cancer. We searched PubMed, Cochrane, Web of Science, and clinicaltrials.gov for relevant studies and included 7 randomized clinical trials (N = 5,302) and 7 non-randomized clinical trials (N = 658). R programming language software was used to conduct this meta-analysis. In 4 RCTs on HER-2 positive BC (N = 2,825), the pooled HR of PFS and OS was 0.72 (95% CI = 0.61-0.84, I2 = 71%) and 0.73 (95% CI = 0.64-0.84, I2 = 20%), respectively in favor of ADCs versus chemotherapy. In RCT on triple negative BC (N = 468), HR of PFS and OS were 0.55 (95%CI = 0.51-0.61) and 0.59 (95% CI = 0.54-0.66), respectively, in favor of saci-gov versus chemotherapy. In RCT on HER-2 positive residual invasive BC, HR of recurrence/death was 0.61 (95% CI = 0.54-0.69) in favor of ADC versus chemotherapy. In an RCT (N = 524), the HR of PFS and OS were 0.28 (95% CI = 0.22-0.37) and 0.55 (95%CI = 0.36-0.86), respectively, in favor of trastuzumab-deruxtecan (T-der) as compared to trastuzumab-emtansine (T-DM1). Anemia, rash, diarrhea, fatigue, hypertension, thrombocytopenia, and elevated aminotransferases were the common ≥grade 3 adverse events reported in 4%, 1%, 2%, 1%, 2%, 9%, and 3% of the patients, respectively. ADCs were more effective than single and double agent chemotherapy in patients with HER-2 positive or triple negative BC. Among ADCs, T-der was more effective than T-DM1.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.042 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".