Tissue and Blood‐Based Tumor Mutational Burden Predicts Treatment Response to <scp>PD</scp> ‐1 Blockade in Head and Neck Squamous Cell Carcinoma: A Systematic Review and Meta‐Analysis

BACKGROUND: Tumor mutational burden (TMB) is a predictive biomarker for immune checkpoint inhibitors (ICIs). Its clinical utility in head and neck squamous cell carcinoma (HNSCC) is limited by differences in detection approaches and inconsistent cut-off values. In this meta-analysis, we systematically reviewed multiple high-quality studies to assess the predictive value of tissue-based TMB (tTMB) and blood-based TMB (bTMB) for treatment response of ICIs. METHODS: This study adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We systematically searched PubMed, Web of Science, Scopus, and SpringerLink for studies published through January 2026. Eligible studies included HNSCC patients treated with ICIs, with outcomes stratified by TMB status. The primary endpoint was objective response rate (ORR), and the secondary endpoints were overall survival (OS) and progression-free survival (PFS). In addition, subgroup analyses were conducted to further explore between-study differences according to different TMB detection approaches. RESULTS: = 0%). Similarly, superior outcomes were observed for OS (hazard ratio [HR] = 0.58; 95% CI, 0.51-0.67; p < 0.001) and PFS (HR = 0.66; 95% CI, 0.57-0.77; p < 0.001). Subgroup analysis of platform-specific patterns revealed distinct cut-off tendencies: tissue-based targeted panels often clustered around a threshold of 10 mut/Mb, whereas blood-based assays required higher cut-offs (≥ 16 mut/Mb) to show predictive signals. CONCLUSIONS: TMB shows promise as a predictive biomarker for objective response and survival in HNSCC patients receiving immunotherapy. In our analysis, both tTMB and bTMB were associated with improved objective response, although evidence for bTMB remains limited. Differences between tTMB and bTMB may contribute to variability in commonly used cut-off values, which should be considered when interpreting results across studies and platforms.