Biomarkers for Predicting Anti-Programmed Cell Death-1 Antibody Treatment Effects in Head and Neck Cancer
Bibliographic record
Abstract
In recurrent or metastatic head and neck squamous cell carcinoma (R/M-HNSCC), survival outcomes are significantly better in patients who receive anti-programmed cell death-1 (PD-1) monoclonal antibody therapy than in those who receive standard therapy. However, there is no established biomarker that can predict the anti-PD-1 antibody treatment effect and immune-related adverse events (irAEs) in these patients. This study investigated the inflammatory and nutritional status in 42 patients with R/M-HNSCC and programmed cell death ligand-1 (PD-L1) polymorphisms (rs4143815 and rs2282055) in 35 of the 42 patients. The 1- and 2-year overall survival was 59.5% and 28.6%, respectively; the 1- and 2-year first progression-free survival was 19.0% and 9.5%, respectively, and the respective second progression-free survival was 50% and 27.8%. Performance status and inflammatory and nutritional status (assessed by the geriatric nutritional risk index, modified Glasgow prognostic score, and prognostic nutritional index) were identified as significant indicators of survival outcomes in multivariate analysis. Patients with ancestral alleles in PD-L1 polymorphisms had less frequent irAEs. Performance status and inflammatory and nutritional status before treatment were closely related to survival outcomes after PD-1 therapy. These indicators can be calculated using routine laboratory data. PD-L1 polymorphisms may be biomarkers for predicting irAEs in patients receiving anti-PD-1 therapy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".