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초록

Deciphering how molecular programs are spatially organized within tissues is pivotal for understanding tumor evolution and microenvironmental interactions. Existing spatial transcriptomics tools either rely on gene-level features, ignoring the rich topology of biological pathways, or deliver black-box clusters with little mechanistic insight; thus, they limit their translational impact. A method that simultaneously leverages pathway structures and spatially matched histopathology could produce domain delineations that are both accurate and biologically interpretable. We introduce PathCLAST (Pathway-augmented Contrastive Learning with Attention for interpretable Spatial Transcriptomics), which is a framework that integrates gene expression, histopathological images, and curated pathway graphs via bi-modal contrastive learning. By embedding expression profiles into biologically structured graphs, and aligning them with local image features, PathCLAST achieves state-of-the-art spatial domain identification on multiple public datasets, while offering pathway-level attention scores for mechanistic interpretation. The pathway embedding also serves as an explicit, biology-informed dimensionality reduction scheme. PathCLAST not only uncovers domain-specific pathways and spatially organized signaling activities, but also quantifies intra-domain heterogeneity, spatial autocorrelation, and inter-domain crosstalk, providing fine-grained insights into tumor progression and tissue architecture. PathCLAST is available at https://github.com/sslim-aidrug/PathCLAST.

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