scPharm: Identifying Pharmacological Subpopulations of Single Cells for Precision Medicine in Cancers.

Abstract

Intratumour heterogeneity significantly hinders the efficacy of anticancer therapies. Compared with drug perturbation experiments, which yield pharmacological data at the bulk cell level, single-cell RNA sequencing (scRNA-seq) technology provides a means to capture molecular heterogeneity at single-cell resolution. Here, scPharm is introduced, a computational framework that integrates pharmacological profiles with scRNA-seq data to identify pharmacological subpopulations of cells within a tumour and prioritize tailored drugs. scPharm uses the normalized enrichment scores (NESs) determined from gene set enrichment analysis to assess the distribution of cell identity genes in drug response-determined gene lists. Based on the strong correlation between the NES and drug response at single-cell resolution, scPharm successfully identified the sensitive subpopulations in ER-positive and HER2-positive human breast cancer tissues, revealed dynamic changes in the resistant subpopulation of human PC9 cells treated with erlotinib, and expanded its ability to a mouse model. Its superior performance and computational efficiency are confirmed through comparative evaluations with other single-cell prediction tools. Additionally, scPharm predicted combination drug strategies by gauging compensation or booster effects between drugs and evaluated drug toxicity in healthy cells in the tumour microenvironment. Overall, scPharm provides a novel approach for precision medicine in cancers by revealing therapeutic heterogeneity at single-cell resolution.