Randomized Phase II Study of Paclitaxel plus Alisertib versus Paclitaxel plus Placebo as Second-Line Therapy for SCLC: Primary and Correlative Biomarker Analyses
Bibliographic record
Abstract
INTRODUCTION: We assessed the Aurora A kinase inhibitor, alisertib, plus paclitaxel (henceforth referred to as alisertib/paclitaxel) as second-line treatment for SCLC. METHODS: In this double-blind study, patients with relapsed or refractory SCLC were stratified by relapse type (sensitive versus resistant or refractory) and brain metastases and randomized 1:1 to alisertib/paclitaxel or placebo plus paclitaxel (henceforth referred to as placebo/paclitaxel) in 28-day cycles. The primary end point was progression-free survival (PFS). Associations of c-Myc expression in tumor tissue (prespecified) and genetic alterations in circulating tumor DNA (retrospective) with clinical outcome were evaluated. RESULTS: A total of 178 patients were enrolled (89 in each arm). The median PFS was 3.32 months with alisertib/paclitaxel versus 2.17 months with placebo/paclitaxel (hazard ratio [HR] = 0.77, 95% confidence limit [CI]: 0.557-1.067, p = 0.113 in the intent-to-treat population versus HR = 0.71, 95% CI: 0.509-0.985, p = 0.038 with corrected analysis applied). Among 140 patients with genetic alternations, patients with cell cycle regulator mutations (cyclin-dependent kinase 6 gene [CDK6], retinoblastoma-like 1 gene [RBL1], retinoblastoma-like 2 gene [RBL2], and retinoblastoma 1 gene [RB1]) had significantly improved PFS with alisertib/paclitaxel versus with placebo/paclitaxel (3.68 versus 1.80 months, respectively [HR = 0.395, 95% CI: 0.239-0.654, p = 0.0003]), and overall survival (7.20 versus 4.47 months, respectively [HR = 0.427, 95% CI: 0.259-0.704, p = 0.00085]). A subset of patients with c-Myc expression showed significantly improved PFS with alisertib/paclitaxel. The incidence of grade 3 or higher drug-related adverse events was 67% (58 patients) with alisertib/paclitaxel versus 22% (25 patients) with placebo/paclitaxel. Twelve patients (14%) versus 11 (12%) died on study, including four versus zero treatment-related deaths. CONCLUSIONS: Efficacy signals were seen with alisertib/paclitaxel in relapsed or refractory SCLC. c-Myc expression and mutations in cell cycle regulators may be potential predictive biomarkers of alisertib efficacy; further prospective validations are warranted.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".