Clinical Considerations for the Management of Advanced PD-L1 ≥50% Non-small Cell Lung Cancer In 2025: Should All Patients Be Treated the Same?
Bibliographic record
Abstract
Despite advances in the treatment of non‑small cell lung cancer (NSCLC) due to the advent of immunotherapy in the form of immune checkpoint inhibitors (ICI), NSCLC remains the leading cause of cancer-related death in Canada. In addition, multiple first‑line options exist for patients with NSCLC without a sensitizing mutation in epidermal growth factor receptor (EGFR) or anaplastic lymphoma kinase (ALK), but no head‑to‑head comparisons of first-line treatment regimens have been made in randomized controlled trials. The programmed cell death ligand 1 (PD-L1) tumour proportion score (TPS)—which is derived from immunohistochemistry analysis—emerged as an important biomarker early in the advent of ICI in NSCLC. Approximately 30% of patients with NSCLC have PD-L1 expression in at least 50% of the tumour. This ≥50% threshold was established through retrospective biomarker analyses in pivotal trials, such as the KEYNOTE-001 and KEYNOTE-024 trials, in which patients with higher PD-L1 expression demonstrated superior response rates and overall survival (OS) benefits with immunotherapy compared to chemotherapy. The KEYNOTE-001 trial first identified ≥50% PD-L1 expression as an optimal cut-off for predicting response to pembrolizumab (anti-programmed cell death protein 1 [PD-1] antibody), showing an objective response rate (ORR) of ~45% in this group. Subsequently, the KEYNOTE-024 trial confirmed that patients with PD-L1 ≥50% had significantly improved progression-free survival (PFS) and OS with pembrolizumab than those treated with chemotherapy (hazard ratio [HR] for PFS: 0.50, 95% confidence interval [CI]: 0.37–0.68). Similar findings from the IMpower110 (atezolizumab) and EMPOWER-Lung 1 trials (cemiplimab) reinforced ≥50% PD-L1 TPS as a clinically meaningful biomarker. As a result, ≥50% PD-L1 TPS became an actionable biomarker in regulatory approvals and treatment guidelines, guiding immunotherapy decisions in advanced NSCLC.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".