Association between PD-L1 status and immune checkpoint inhibitor response in advanced malignancies: a systematic review and meta-analysis of overall survival data
Bibliographic record
Abstract
BACKGROUND: Targeting the programmed death ligand 1 (PD-L1) pathway has become standard for many advanced malignancies. Whether PD-L1 expression predicts response is unclear. We assessed the association between PD-L1 expression and immunotherapy response using stratified meta-analysis. METHODS: We performed a systematic review of randomized clinical trials published prior to October 2018 comparing overall survival (OS) in patients with advanced solid organ malignancies treated with immunotherapy or standard treatment. Pooled hazard ratios were calculated among patients with high and low PD-L1 levels independently. Differences between the two estimates were assessed using meta-analysis of study-level differences. Our primary analysis assessed a 1% threshold while secondary analyses utilized 5, 10 and 50%. RESULTS: 14 eligible trials reporting on 8887 patients were included. While there was a significant OS benefit for immunotherapy compared with standard treatment for all patients, the magnitude of benefit was significantly larger among those with high PD-L1 expression (P = 0.006). This finding persisted regardless of threshold used and across subgroup analyses according to PD-L1 assay type, tumor histology, line of therapy, type of inhibitor and study methodology. CONCLUSIONS: PD-L1 levels have important predictive value in determining the response to immunotherapy. However, patients with low PD-L1 levels also experience improved survival with immunotherapy compared with standard treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.023 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".