Real-world outcomes for metastatic non-small cell lung cancer patients treated with checkpoint inhibitor immunotherapy in Canada.
Bibliographic record
Abstract
54 Background: The majority of patients with non-small cell lung cancer (NSCLC) are diagnosed with incurable, metastatic disease. Immunotherapy (IO) agents have improved overall, long term survival. There is a need to better understand how these drugs are utilized and perform in the real world to further inform physicians and policymakers. Methods: In this retrospective study, we analyzed characteristics, treatment patterns, and outcomes of patients with metastatic NSCLC treated with checkpoint inhibitors nivolumab or pembrolizumab at five Canadian cancer centres, with a focus on patients who received these treatments in the 2nd (2L) or 3rd line (3L) setting. We excluded patients who were in blinded clinical trials, exposed to multiple IO drugs, or who had tumors with EGFR or ALK mutations. Primary endpoints were overall survival (OS) and progression free survival (PFS) from start of IO. Secondary endpoints included immune-related toxicities. Results: Across all sites, we screened 322 patients and included 230 who met criteria: 49 (21%) from Alberta; 60 (26%) from British Columbia; and 121 (53%) from Ontario. Patients were diagnosed with metastatic NSCLC from 2009 to 2018, but the majority were diagnosed after 2015. Median age at diagnosis was 66.6 years, 54% were female, and 86% were either current or former smokers and 67% had stage IV disease. About 88% received nivolumab; 87% (n=200) received IO in 2L (n=111) or 3L (n=89). The median OS from start of IO was 10.9 (8.7–15.6) months, and were similar for 2L vs 3L (9.3 vs 12.0 months, p = 0.2). Median PFS was 5.7 (4.4–8.1) months. Pneumonitis was the most frequently reported toxicity affecting 7% of patients. Thyroiditis was reported in 4%; colitis, while dermatitis and hepatitis were each reported in 3%, and nephritis in 1%. Conclusions: In the real world, IO for advanced NSCLC was well-tolerated and had outcomes that were comparable to landmark clinical trials.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".