Associations with response to Poly(ADP-ribose) Polymerase (PARP) inhibitors in patients with metastatic breast cancer
Bibliographic record
Abstract
PARP inhibitors (PARPi) have modest antitumor activity in patients with advanced breast cancer and mutation in BRCA. It is unclear whether some subgroups derive greater benefit from treatment. MEDLINE and EMBASE were searched from inception to March 2021 to identify trials of PARPi in patients with metastatic breast cancer. Objective response rate (ORR) and clinical benefit rate (CBR) to PARPi were extracted and pooled in a meta-analysis using the Mantel Haenszel random effects model. Meta-regression explored the influence of patient and tumor characteristics on ORR and CBR. For randomized trials, hazard ratio comparing PARPi to control therapy were pooled using inverse variance and random effects. Analysis included 43 studies comprising 2409 patients. Among these, 1798 (75%) patients had BRCA mutations and 1146 (48%) were triple negative. In 10 studies (28%; n = 680 patients), the PARPi was given in combination with platinum-based chemotherapy. Weighted mean ORR was 45%; 64% when combined with platinum vs 37% with PARPi monotherapy (p < 0.001). Previous platinum-based chemotherapy was associated with lower ORR (p = 0.02). Compared to standard chemotherapy, progression-free survival was improved (HR 0.64, p < 0.001), but there was no difference in overall survival (HR 0.87, p = 0.06). There were no differences in ORR or CBR between BRCA1 and BRCA2 mutations. PARPi are more active in combination with platinum than as monotherapy, with lower response if given as monotherapy after platinum exposure. Significant improvements in ORR translated to modest improvement in progression-free, but not overall survival. There was no association between ORR and BRCA mutations.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.013 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".