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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.027 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it