Digital Spatial Pathway Mapping Reveals Prognostic Tumor States in Head and Neck Cancer
Head and neck squamous cell carcinoma (HNSCC) is a morphologically and molecularly heterogeneous disease with limited effectiveness of genotype-informed therapies. Transcriptome-derived estimates of signaling pathway activity carry prognostic and therapeutic potential but remain inaccessible in routine diagnostics due to cost and tissue constraints. Here, we introduce Digital Spatial Pathway Mapping, an AI-based computational pathology framework that infers signaling pathway activities directly from routine hematoxylin and eosin (H&E) slides, enabling in-silico spatial molecular readouts from standard histology. Models trained on HPV-negative HNSCC from TCGA and externally validated on CPTAC robustly predicted transcriptome-derived activities in cancer-relevant signaling pathways. To achieve spatial interpretability, we applied layer-wise relevance propagation (LRP) to generate heatmaps that highlight positive versus negative evidence for pathway activation. These LRP heatmaps were technically validated by patch-flipping tests and biologically validated against pathway-relevant immunohistochemistry in an independent patient cohort. From these explanations, we derived a tumor area pathway activity score (TAPAS) quantifying the spatial fraction of activated tumor regions within a slide. Applied to a retrospective HNSCC cohort of 1,066 slides from 112 resection specimens, TAPAS captured intratumoral heterogeneity and revealed two biologically dis-tinct tumor states - an oncogenic growth phenotype with widespread pathway activation and a pathway quiescent phenotype associated with higher recurrence risk independent of clinicopathological variables. These findings establish Digital Spatial Pathway Mapping as a scalable, in-silico approach to recover systems-level molecular information from standard histopathology, enabling prognostic and mechanistically grounded patient stratification in head and neck cancer.