Atezolizumab in Combination With Carboplatin and Nab-Paclitaxel in Advanced Squamous NSCLC (IMpower131): Results From a Randomized Phase III Trial
Bibliographic record
Abstract
INTRODUCTION: Cytotoxic agents have immunomodulatory effects, providing a rationale for combining atezolizumab (anti-programmed death-ligand 1 [anti-PD-L1]) with chemotherapy. The randomized phase III IMpower131 study (NCT02367794) evaluated atezolizumab with platinum-based chemotherapy in stage IV squamous NSCLC. METHODS: A total of 1021 patients were randomized 1:1:1 to receive atezolizumab+carboplatin+paclitaxel (A+CP) (n = 338), atezolizumab+carboplatin+nab-paclitaxel (A+CnP) (n = 343), or carboplatin+nab-paclitaxel (CnP) (n = 340) for four or six 21-day cycles; patients randomized to the A+CP or A+CnP arms received atezolizumab maintenance therapy until progressive disease or loss of clinical benefit. The coprimary end points were investigator-assessed progression-free survival (PFS) and overall survival (OS) in the intention-to-treat (ITT) population. The secondary end points included PFS and OS in PD-L1 subgroups and safety. The primary PFS (January 22, 2018) and final OS (October 3, 2018) for A+CnP versus CnP are reported. RESULTS: PFS improvement with A+CnP versus CnP was seen in the ITT population (median, 6.3 versus 5.6 mo; hazard ratio [HR] = 0.71, 95% confidence interval [CI]: 0.60-0.85; p = 0.0001). Median OS in the ITT population was 14.2 and 13.5 months in the A+CnP and CnP arms (HR = 0.88, 95% CI: 0.73-1.05; p = 0.16), not reaching statistical significance. OS improvement with A+CnP versus CnP was observed in the PD-L1-high subgroup (HR = 0.48, 95% CI: 0.29-0.81), despite not being formally tested. Treatment-related grade 3 and 4 adverse events and serious adverse events occurred in 68.0% and 47.9% (A+CnP) and 57.5% and 28.7% (CnP) of patients, respectively. CONCLUSIONS: Adding atezolizumab to platinum-based chemotherapy significantly improved PFS in patients with first-line squamous NSCLC; OS was similar between the arms.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".