Treatment of Non-Small-Cell Lung Cancer after Progression on Nivolumab or Pembrolizumab
Bibliographic record
Abstract
Background: Although PD-1 antibodies (PD1 Ab) are the standard of care for advanced non-small-cell lung cancer (ansclc), most patients will progress. We compared survival outcomes for patients with ansclc who received systemic therapy (st) after progression and for those who did not. Additionally, clinical characteristics that predicted receipt of st after PD1 Ab failure were evaluated. Methods: All patients with ansclc in British Columbia initiated on nivolumab or pembrolizumab between June 2015 and November 2017, with subsequent progression, were identified. Eligibility criteria for additional st included an Eastern Cooperative Oncology Group (ecog) performance status (ps) of 3 or less and survival for more than 30 days from the last PD1 Ab treatment. Post-progression survival (pps) was assessed by landmark analysis. Baseline characteristics associated with pps were identified by multivariable analysis. Results: Of 94 patients meeting the eligibility criteria, 33 received st after progression. In 75.6%, a PD1 Ab was received as first- or second-line treatment. The most common sts were erlotinib (36.4%) and docetaxel (27.3%). No statistically significant difference in median pps was observed between patients who did and did not receive st within 30 days of their last PD1 Ab treatment (6.9 months vs. 3.6 months, log-rank p = 0.15.) In multivariable analysis, factors associated with increased pps included an ecog ps of 0 or 1 compared with 2 or 3 [hazard ratio (hr): 0.42; 95% confidence interval (ci): 0.24 to 0.73; p = 0.002] and any response compared with no response to PD1 Ab (hr: 0.54; 95% ci: 0.33 to 0.90; p = 0.02). Conclusions: In this cohort, only 35.1% of patients eligible for post–PD1 Ab therapy received st. Post-progression survival was not significantly affected by receipt of post-progression therapy. Prospective trials are needed to clarify the benefit of post–PD1 Ab treatments.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".