Continuous Versus 1-Year Fixed-Duration Nivolumab in Previously Treated Advanced Non–Small-Cell Lung Cancer: CheckMate 153
Bibliographic record
Abstract
PURPOSE Limited data exist on the optimal duration of immunotherapy, including for non–small-cell lung cancer (NSCLC). We present an exploratory analysis of CheckMate 153, a largely community-based phase IIIb/IV study, to evaluate the impact of 1-year fixed-duration versus continuous therapy on the efficacy and safety of nivolumab. METHODS Patients with previously treated advanced NSCLC received nivolumab monotherapy (3 mg/kg every 2 weeks). Those still receiving treatment at 1 year, including patients perceived to be deriving benefit despite radiographic progression, were randomly assigned to continue nivolumab until disease progression or unacceptable toxicity or to stop nivolumab with the option of on-study retreatment after disease progression (1-year fixed duration). RESULTS Of 1,428 patients treated, 252 were randomly assigned to continuous (n = 127) or 1-year fixed-duration (n = 125) treatment (intent-to-treat [ITT] population). Of these, 89 and 85 patients in the continuous and 1-year fixed-duration arms, respectively, had not progressed (progression-free survival [PFS] population). With minimum post–random assignment follow-up of 13.5 months, median PFS was longer with continuous versus 1-year fixed-duration treatment (PFS population: 24.7 months v 9.4 months; hazard ratio [HR], 0.56 [95% CI, 0.37 to 0.84]). Median overall survival from random assignment was longer with continuous versus 1-year fixed-duration treatment in the PFS (not reached v 32.5 months; HR, 0.61 [95% CI, 0.37 to 0.99]) and ITT (not reached v 28.8 months; HR, 0.62 [95% CI, 0.42 to 0.92]) populations. Few new-onset treatment-related adverse events occurred. No new safety signals were identified. CONCLUSION To our knowledge, these findings from an exploratory analysis represent the first randomized data on continuous versus fixed-duration immunotherapy in previously treated advanced NSCLC and suggest that continuing nivolumab beyond 1 year improves outcomes.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".