Targeting the PD-1/PD-L1 Axis for the Treatment of Non-Small-Cell Lung Cancer
Bibliographic record
Abstract
Lung cancer is the leading cause of cancer-specific death among Canadians, with non-small-cell lung cancer (nsclc) being the most common histologic variant. Despite advances in the understanding of the molecular biology of nsclc, the survival rate for this malignancy is still poor. It is now understood that, to evade detection and immune clearance, nsclc tumours overexpress the immunosuppressive checkpoint protein programmed death ligand 1 (PD-L1). Inhibiting the PD-1/PD-L1 axis with monoclonal antibodies has significantly changed the treatment landscape in nsclc during the last 5 years. Despite evidence of clinical response in some patients, only approximately 20% of patients obtain any durable benefit, and many of the patients who do respond ultimately relapse with drug-resistant disease. The identification of patients who are most likely to benefit from such therapy is therefore important. In the present review, we cover the basics of the PD-1/PD-L1 axis and its clinical significance in nsclc, biomarkers that are predictive of treatment response, relevant clinical trials of PD-1/PD-L1 blockade completed to date, and proposed mechanisms of acquired therapeutic resistance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.002 |
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".