Single-agent PARP inhibitors for the treatment of patients with BRCA-mutated HER2-negative metastatic breast cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
Single-agent poly (ADP-ribose) polymerase (PARP) inhibitors (PARPi) have been approved as the first targeted therapy available for patients with BRCA -mutated HER2-negative metastatic breast cancer. This meta-analysis aimed to better evaluate activity, efficacy and safety of single-agent PARPi in this population. A systematic search of Medline, Embase and conference proceedings up to 31 January 2018 was conducted to identify randomised controlled trials (RCTs) investigating single-agent PARPi versus monochemotherapy in patients with BRCA -mutated HER2-negative metastatic breast cancer. Using the random-effect model, we calculated summary risk estimates (pooled HR and OR with 95% CI) for progression-free survival (PFS), overall survival (OS), objective response rate (ORR), any grade and grade 3–4 adverse events (AEs), treatment discontinuation rate and time to deterioration in quality of life (QoL). Two RCTs (n=733) were included. As compared with monochemotherapy, single-agent PARPi significantly improved PFS (HR 0.56(95% CI 0.45 to 0.70)) and ORR (OR 4.15 (95% CI 2.82 to 6.10)), with no difference in OS (HR 0.82 (95% CI 0.64 to 1.05)). Single-agent PARPi significantly increased risk of anaemia and any grade headache, but reduced risk of neutropenia and any grade palmar-plantar erythrodysesthesia syndrome as compared with monochemotherapy. No significant differences in other AEs and treatment discontinuation rate were observed. Patients treated with PARPi experienced a significant delayed time to QoL deterioration (HR 0.40 (95% CI 0.29 to 0.54)). Single-agent PARPi showed to be an effective, well tolerated and useful treatment in maintaining QoL of patients with BRCA -mutated HER2-negative metastatic breast cancer.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.031 |
| Bibliometrics | 0.005 | 0.006 |
| 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) | 0.004 | 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".