Spatial transcriptomics of immune ecotypes for predicting immunotherapy outcomes in head and neck squamous cell carcinoma.
Head and neck squamous cell carcinoma (HNSCC) exhibits heterogeneous tumour-immune microenvironments that limit the utility of single biomarkers such as programmed death-ligand 1 (PD-L1) and tumour mutational burden (TMB) for guiding immune checkpoint inhibitor (ICI) therapy. This study developed and validated the Ecotype-Integrated Response Model for HNSCC (EIRM-HN), integrating single-cell states, spatial transcriptomic niches, and bulk transcriptomes to derive immune ecotypes that stratify ICI outcomes.
This retrospective multi-cohort study analysed 370 HNSCC cases (80 molecular, 210 immunotherapies, 80 control) profiled by single-cell RNA sequencing, spatial transcriptomics, and bulk RNA sequencing. Immune ecotypes were derived from integrated single-cell and spatial features, converted into weighted gene signatures, and projected into bulk ICI cohorts to train penalised Cox and logistic models and compare performance against PD-L1, tumour mutational burden, and published signatures, with external prognostic validation in the GSE65858 bulk cohort.
Among 210 ICI-treated patients, four ecotypes occurred at similar frequencies. The most suppressive ecotype showed low CD8+ T-cell abundance, high regulatory T-cell abundance, and increased stromal fraction, with median progression-free survival of 3.8 months and overall survival of 9.1 months, versus 9.8 and 20.3 months in the lymphoid-enriched ecotype. EIRM-HN achieved progression-free and overall survival concordance indices of 0.71 and 0.70, improving to 0.75 and 0.74 after adding clinical covariates, and exceeding PD-L1 and TMB. In GSE65858, overall survival concordance index was 0.67 with a hazard ratio of 1.89 for high- versus low-risk strata.
An ecotype-based, spatially anchored risk model integrating single-cell, spatial, and bulk transcriptomic data provides improved prognostic stratification of HNSCC relative to established biomarkers and generalises to an external bulk cohort.
This retrospective multi-cohort study analysed 370 HNSCC cases (80 molecular, 210 immunotherapies, 80 control) profiled by single-cell RNA sequencing, spatial transcriptomics, and bulk RNA sequencing. Immune ecotypes were derived from integrated single-cell and spatial features, converted into weighted gene signatures, and projected into bulk ICI cohorts to train penalised Cox and logistic models and compare performance against PD-L1, tumour mutational burden, and published signatures, with external prognostic validation in the GSE65858 bulk cohort.
Among 210 ICI-treated patients, four ecotypes occurred at similar frequencies. The most suppressive ecotype showed low CD8+ T-cell abundance, high regulatory T-cell abundance, and increased stromal fraction, with median progression-free survival of 3.8 months and overall survival of 9.1 months, versus 9.8 and 20.3 months in the lymphoid-enriched ecotype. EIRM-HN achieved progression-free and overall survival concordance indices of 0.71 and 0.70, improving to 0.75 and 0.74 after adding clinical covariates, and exceeding PD-L1 and TMB. In GSE65858, overall survival concordance index was 0.67 with a hazard ratio of 1.89 for high- versus low-risk strata.
An ecotype-based, spatially anchored risk model integrating single-cell, spatial, and bulk transcriptomic data provides improved prognostic stratification of HNSCC relative to established biomarkers and generalises to an external bulk cohort.