STADS: Spatial Transcriptomics to Aid Drug-reposition Recommendation

Drug repurposing is a promising strategy to find new usage for existing drugs outside of their original purpose. We have recently developed a computational framework ASGARD that employs single cell RNA-sequencing (scRNA-seq) to repurpose drugs. However, scRNA-seq lacks the spatial information of the tissue, an important determinant of the heterogeneity within the tissue micro-environment. In this paper, we propose Spatial Transcriptomics to Aid Drug-reposition Recommendation (STADS) framework, a new drug repurposing computational framework that employs Spatial Transcriptomics (ST) datasets with gene expression in situ. STADS reduces the ST gene expression by spatially-aware embedding, integrates multiple healthy and diseased samples, identifies the matched spatial domains between disease and controls, then repurposes drugs by a novel drug score that considers differentially expressed genes between disease and control samples among all matched spatial domains. We apply STADS to a ST dataset of patients with Hepatocellular Carcinoma and demonstrate its superior performance to ASGARD. STADS is a promising personalized drug repurposing prediction method using ST data.