Computational Pipeline for Anticancer Drug Repurposing via Dimensionality Reduction

Drug repurposing refers to the systematic identification of new therapeutic uses for existing drugs. Unlike traditional de novo drug discovery, which is expensive and time-consuming, repurposing leverages compounds with already established safety, pharmacokinetic, and pharmacodynamic profiles. In this study, we propose a drug repositioning model based on low-dimensional transcriptomic representations to investigate the relationship between known anticancer drugs and non-anticancer compounds. We analyzed LINCS L1000 data (1170 drugs; 824 anticancer, 346 non-anticancer). Data were projected with UMAP, PCA, and t-SNE. For each anticancer drug and for each method, we retrieved the k = 5 nearest non-anticancer neighbors and ranked candidates by recurrence frequency across all anticancer queries. We identified Ergometrine, Mupirocin, and (S)-blebbistatin among the most frequent non-anticancer drugs with a close association with drugs known to be anticancer. In addition, we performed a local neighborhood enrichment around the three candidates. Regarding Ergometrine (DB01253), in UMAP, 44/50 neighbors were anticancer (88.0% vs. global baseline 70.5%; hypergeometric BH-adjusted p = 0.0039). Considering (S)-blebbistatin (DB01944) in UMAP, 41/50 neighbors were anticancer (82.0% vs. 70.5%; BH-adjusted p = 0.0435). Mupirocin (DB00410) in UMAP had 44/50 neighbors as anticancer (88.0% vs. 70.5%; BH-adjusted p = 0.0039). Future research should explore the three drugs with in vivo models, investigating their possible synergies.