Abstract A065: Fast and cost-effective prediction of treatment response in head and neck cancer by characterizing the tumor microenvironment from routine H&E slides

Abstract Background: Treatment response prediction in head and neck squamous cell carcinoma (HNSCC) remains a major clinical challenge due to spatial heterogeneity within the tumor microenvironment (TME). While spatial transcriptomics (ST) technology offers a powerful means to profile TME architecture, its clinical utility is constrained by high cost, tissue requirements, and limited scalability, restricting its application to large retrospective cohorts. To address these limitations, we developed an AI-framework to infer spatial gene expression profiles directly from routine H 0.4 predicted vs measured ST gene expression). Second, we employed the model to infer spatial gene expression from a large TCGA-HNSC cohort consisting of 445 HNSCC patients. Third, by spatial clustering of inferred ST expression, we identified 11 shared spatial clusters representing TME composition within each patient. Fourth, we utilized these spatially defined clusters to characterize the TME landscape of both adjuvant-treated (TCGA-HNSCC, n=226) and immunotherapy-treated patients (external cohort, n=94). Fifth, we quantified each spatial cluster's proportional representation per patient and trained two separate models using five-fold cross-validation to predict response for the adjuvant and immunotherapy-treated cohorts. The spatial cluster compositions are predictive of adjuvant therapy response, achieving an AUROC of 0.67±0.09. When combined with clinical variables (age and HPV status), performance improved further to an AUROC of 0.74±0.12. Notably, non-responders are predominantly enriched for a spatial cluster that exhibits a KRAS-driven, inflammatory-yet-immunosuppressed transcriptional signature associated with treatment resistance. Similarly, in the immunotherapy cohort, the model achieved an AUROC of 0.71±0.14. The spatial cluster having the strongest association with immunotherapy treatment response, characterized by an immunoreactive immune phenotype (T-cell-inflamed, IFN-γ-high tumor state) consistent with favorable immunotherapy outcomes. These results demonstrate that model-derived spatial features can reliably predict treatment response across diverse therapeutic modalities in HNSCC, with distinct spatial signatures distinguishing responders. Conclusions: We present the first AI-framework to derive spatial TME features from routine H 2026 Feb 18-21; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Immunol Res 2026;14(2 Suppl):Abstract nr A065.