Tumour- and Non-Tumour-Associated Factors That Modulate Response to PD-1/PD-L1 Inhibitors in Non-Small Cell Lung Cancer
Bibliographic record
Abstract
The interaction of programmed cell death receptor 1 (PD-1) on the surface of immune cells with its ligand, programmed cell death ligand 1 (PD-L1), expressed on tumour cells and antigen-presenting cells, leads to tumour immune evasion. Antibodies that target either PD-1 or its ligand PD-L1 have shown a favourable response in cancer patients, especially those with non-small cell lung cancer (NSCLC). However, only 15 to 25% of advanced NSCLC patients will benefit from immunotherapy. The PD-L1 tumour proportion score (TPS) is the current standard biomarker to select patients for PD-1/PD-L1 blockade therapy, as patients with a high PD-L1 TPS show better response compared to patients with a low PD-L1 TPS. However, since PD-L1 expression is a continuous variable and is an imperfect biomarker, investigation into additional predictive markers is warranted. This review focuses on tumour- and non-tumour-associated factors that have been shown to affect the response to PD-1/PD-L1 inhibitors in NSCLC. We also delve into mechanistic and clinical evidence on these potential biomarkers and their relationship to the tumour microenvironment (TME).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 0.001 |
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".