Smoking History Predicts Sensitivity to PARP Inhibitor Veliparib in Patients with Advanced Non–Small Cell Lung Cancer
Bibliographic record
Abstract
INTRODUCTION: Tobacco-related NSCLC is associated with reduced survival and greater genomic instability. Veliparib, a potent poly(adenosine diphosphate-ribose) polymerase inhibitor, augments platinum-induced DNA damage. A phase 2 trial of untreated advanced NSCLC showed a trend for improved outcomes (hazard ratio [HR] = 0.80, 95% confidence interval: 0.54-1.18, p = 0.27 for overall survival and HR = 0.72, 95% CI: 0.45-1.15, p = 0.17 for progression-free survival) when veliparib was added to carboplatin/paclitaxel. Here we report an exploratory analysis by smoking history. METHODS: Patients were randomized 2:1 to receive carboplatin/paclitaxel with veliparib, 120 mg (n = 105), or placebo (n = 53). Patients were stratified by histologic subtype and smoking history (recent smokers [n = 95], former smokers [n = 42], and never-smokers [n = 21]). Plasma cotinine level was measured as a chemical index of smoking. Mutation status was assessed by whole exome sequencing (n = 38). RESULTS: Smoking history, histologic subtype, age, Eastern Cooperative Oncology Group performance status, sex, and geographic region predicted veliparib benefit in univariate analyses. In multivariate analysis, history of recent smoking was most predictive for veliparib benefit. Recent smokers treated with veliparib derived significantly greater progression-free survival and overall survival benefits (HR = 0.38 [p < 0.01] and HR = 0.43 [p < 0.01]) than former smokers (HR = 2.098 [p = 0 0208] and HR = 1.62 [p = 0.236]) and never-smokers (HR = 1.025 [p = 0.971] and HR = 1.33 [p = 0.638]). Sequencing data revealed that mutational burden was not associated with veliparib benefit. The rate of grade 3 or 4 adverse events was higher in recent smokers with veliparib treatment; all-grade and serious adverse events were similar in both treatment arms. CONCLUSIONS: Smoking history predicted for efficacy with a veliparib-chemotherapy combination; toxicity was acceptable regardless of smoking history. A prespecified analysis of recent smokers is planned for ongoing phase 3 studies of veliparib in NSCLC.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".