Randomized, Placebo-Controlled, Phase II Study of Veliparib in Combination with Carboplatin and Paclitaxel for Advanced/Metastatic Non–Small Cell Lung Cancer
Bibliographic record
Abstract
Abstract Purpose: PARP plays an important role in DNA repair. Veliparib, a PARP inhibitor, enhances the efficacy of platinum compounds and has been safely combined with carboplatin and paclitaxel. The primary endpoint of this phase II trial determined whether addition of veliparib to carboplatin and paclitaxel improved progression-free survival (PFS) in previously untreated patients with advanced/metastatic non–small cell lung cancer. Experimental Design: Patients were randomized 2:1 to carboplatin and paclitaxel with either veliparib or placebo. Veliparib (120 mg) or placebo was given on days 1 to 7 of each 3-week cycle, with carboplatin (AUC = 6 mg/mL/min) and paclitaxel (200 mg/m2) administered on day 3, for a maximum of 6 cycles. Results: Overall, 158 were included (median age, 63 years; male 68%, squamous histology 48%). Median PFS was 5.8 months in the veliparib group versus 4.2 months in the placebo group [HR, 0.72; 95% confidence interval (CI), 0.45–1.15; P = 0.17)]. Median overall survival (OS) was 11.7 and 9.1 months in the veliparib and placebo groups, respectively (HR, 0.80; 95% CI, 0.54–1.18; P = 0.27). In patients with squamous histology, median PFS (HR, 0.54; 95% CI, 0.26–1.12; P = 0.098) and OS (HR, 0.73; 95% CI, 0.43–1.24; P = 0.24) favored veliparib treatment. Objective response rate was similar between groups (veliparib: 32.4%; placebo: 32.1%), but duration of response favored veliparib treatment (HR, 0.47; 95% CI, 0.16–1.42; P = 0.18). Grade III/IV neutropenia, thrombocytopenia, and anemia were comparable between groups. Conclusions: Veliparib combination with carboplatin and paclitaxel was well-tolerated and demonstrated a favorable trend in PFS and OS versus chemotherapy alone. Patients with squamous histology had the best outcomes with veliparib combination. Clin Cancer Res; 23(8); 1937–44. ©2016 AACR.
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How this classification was reachedexpand
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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".