Abstract
Head and neck squamous cell carcinoma (HNSC) is a prevalent malignancy associated with poor prognosis despite recent therapeutic advances. We hypothesized that a comprehensive understanding of the spatial heterogeneity and organization of the tumor microenvironment (TME) can substantially improve risk stratification and prediction of treatment response in HNSC. As spatial transcriptomics (ST) remains labor-intensive and costly, we developed HEiST (H&E-Inferred Spatial Transcriptomics), a deep learning framework that predicts spatially resolved gene expression profiles directly from routine hematoxylin and eosin (H&E)-stained histology slides. After rigorous validation across two independent external ST cohorts, we applied HEiST to infer spatial transcriptomes across 1,500 HNSC patient tumors spanning two publicly available datasets and two newly generated cohorts, one treated with concurrent chemoradiotherapy (CCRT) and one with immunotherapy. This large-scale analysis uncovered reproducible spatial clusters characterizing the HNSC TME, defining two distinct prognostic Spatiotypes , Immune - Exhausted and Immune - Activated , with significantly distinct survival outcomes. Critically, spatial cluster composition accurately predicts HPV status and yields treatment response predictors for both CCRT/radiotherapy and immunotherapy that outperform costly gene-expression and direct image-based approaches. Notably, the ST cluster–based predictor of immunotherapy response markedly surpasses the performance of commonly used FDA-approved biomarkers, including CPS, TPS, and their combination. To the best of our knowledge, this represents the first virtual spatial profiling effort and the most comprehensive large-scale spatial TME analysis in HNSC to date. HEiST thus introduces a scalable, low-cost, and spatially grounded biomarker discovery for precision oncology in HNSC.
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