A Progression-Associated Tumor-Reactive T-Cell Signature Predicts Immunotherapy Response and Reveals ICOS-High CD8...

Immune checkpoint blockade has improved outcomes for patients with head and neck squamous cell carcinoma (HNSCC), but reliable biomarkers for predicting therapeutic response remain limited. Tumor-reactive T-cell states are increasingly recognized as critical determinants of antitumor immunity; however, progression-associated T-cell programs that reflect tumor evolution and predict immunotherapy benefit remain insufficiently characterized. We integrated single-cell transcriptomic and T-cell receptor (TCR) sequencing data from HPV-negative HNSCC progression models to identify T-cell genes associated with tumor progression and TCR-supported immune states. Candidate genes were further refined using pretreatment immunotherapy cohorts, and a five-gene signature (HNSC-TstcSig) was established using survival and response-associated analyses. The predictive performance of HNSC-TstcSig was evaluated across multiple immunotherapy cohorts, including HNSCC and melanoma data sets. Single-cell transcriptomic analyses, spatial transcriptomic analysis, and multiplex immunofluorescence were performed to characterize the biological features of HNSC-TstcSig-associated immune states. In addition, machine learning-based survival modeling was conducted to identify genes shared between immunotherapy response and natural prognosis. HNSC-TstcSig, consisting of GBP2, HAVCR2, ETV7, ICOS, and IL2RA, showed consistent associations with immunotherapy response across multiple cohorts and reflected an immune-inflamed tumor microenvironment characterized by enhanced immune infiltration, T-cell activation, and immune checkpoint-related programs. Single-cell analyses revealed that HNSC-TstcSig genes were predominantly enriched in activated CD8 T-cell populations and were associated with cytotoxic and inflammatory immune states. Among the signature genes, ICOS showed consistent associations with CD8 T-cell activation, cytotoxicity, and immune checkpoint-related features. Network perturbation analysis further suggested that ICOS was connected with transcriptional programs involved in T-cell functional regulation. Spatial transcriptomic analysis and multiplex immunofluorescence further supported the association between ICOS-related immune features and tumor immune activity at the tissue level. Furthermore, a machine learning-derived natural prognostic model identified ICOS as a shared gene linking immunotherapy response and survival outcomes. We developed a progression-associated T-cell signature, HNSC-TstcSig, that predicts immunotherapy response and reflects immune-active tumor states in HNSCC. Our findings suggest that ICOS is associated with a tumor-reactive CD8 T-cell state characterized by cytotoxic activity and immune checkpoint-related features, providing a potential biomarker for immune stratification and a framework for understanding T-cell states associated with therapeutic benefit.