Prognostic Value of Lymphocyte-Activation Gene 3 (LAG3) in Cancer: A Meta-Analysis
Bibliographic record
Abstract
Introduction: Therapeutic targeting of inhibitors of the immune response have reached the clinical setting. Inhibitors of the novel receptor LAG3, that negatively regulates T cell activation, are under investigation. Here we explore the presence and prognostic role of LAG3 in cancer. Methods: A systematic search of electronic databases identified publications exploring the effect of LAG3 on overall survival (OS) and (for early stage cancers) disease free survival (DFS). Hazard ratios (HR) were pooled in a meta-analysis using generic inverse-variance and random effects modeling. Subgroup analyses were conducted based on disease site or tumor type. Results: Fifteen studies met the inclusion criteria. LAG3 was associated with better overall survival (HR 0.81, 95% confidence interval (CI) 0.66 - 0.99; P = 0.04) with subgroup analysis showing no significant differences between disease-site subgroups. The beneficial effect of LAG3 on OS was of greater magnitude in early-stage malignancies (HR 0.73, 95% CI 0.60 - 0.88) than in the metastatic setting (HR 1.20, 95% CI 0.70-2.05), but this difference did not meet statistical significance (subgroup difference p = 0.18). LAG3 was not associated with a significant association with DFS (HR 1.02, 95% confidence interval (CI) 0.77-1.37; P = 0.87) with subgroup analysis showing worse DFS in patients with lymphoma and improved DFS in the breast cancer. Conclusions: High expression of LAG3 associates with favorable overall survival in several solid tumors. A trend for an association in early stage disease suggests the importance of immune surveillance in this setting.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.042 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".