scPharm: identifying pharmacological subpopulations of single cells for precision medicine in cancers

Abstract

Intratumour heterogeneity is a major challenge that limits the effectiveness of anticancer therapies, thus compromising treatment outcomes. Single-cell RNA sequencing (scRNA-seq) technology offers a means to capture gene expression profiles at a single-cell resolution, while drug perturbation experiments yield valuable pharmacological data at the bulk cell level. Here, we introduce "scPharm", a computational framework to integrate large-scale pharmacogenomics profiles with scRNA-seq data, for identifying pharmacological subpopulations within a tumour and prioritizing tailored drugs. scPharm assesses the distribution of the identity genes of single cell (Cell-ID) within drug response-determined gene list, which is accomplished using the normalized enrichment score (NES) obtained from Gene Set Enrichment Analysis (GSEA) as the statistic. One key strength of scPharm is rooted in the robust positive correlation between NES statistics and drug response values at single-cell resolution. scPharm successfully identifies sensitive subpopulations in ER-positive and HER2-positive human breast cancer tissues, discovers dynamic changes in resistant subpopulation of human PC9 cells treated with Erlotinib, and expands its prediction capabilities to a mouse model. By a thoroughly comparative evaluation with other single-cell prediction tools, scPharm presents the superior predictive performance and computational efficiency. Furthermore, scPharm offers a unique feature by predicting combination strategies, gauging compensation effects or booster effects between two drugs through the Set covering method, as well as evaluating drug toxicity on healthy cells within the tumour microenvironment. Together, scPharm provides a novel approach to uncover therapeutic heterogeneity within tumours at single-cell resolution and facilitates precision medicine in cancers.