Biomarkers for Predicting Anti-Programmed Cell Death-1 Antibody Treatment Effects in Head and Neck Cancer.
Katsunori TanakaHitoshi HirakawaMikio SuzukiTeruyuki HigaShinya AgenaNarumi HasegawaJunko KawakamiMasatomo ToyamaTomoyo HigaHidetoshi KinjyoNorimoto KiseShunsuke KondoHiroyuki MaedaTaro IkegamiPublished in: Current oncology (Toronto, Ont.) (2023)
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.
Keyphrases
- free survival
- end stage renal disease
- chronic kidney disease
- ejection fraction
- monoclonal antibody
- oxidative stress
- newly diagnosed
- squamous cell carcinoma
- stem cells
- prognostic factors
- mesenchymal stem cells
- replacement therapy
- cell therapy
- combination therapy
- bone marrow
- machine learning
- high resolution
- data analysis
- atomic force microscopy