Tian Peng, Zheng Jie, Qiao Keying, Fan Yuxiao, Xu Yue, Wu Tao, Chen Shuting, Zhang Yinuo, Zhang Bingyue, Ambrogio Chiara, Wang Haiyun
Research Center for Translational Medicine, Shanghai East Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
Department of Molecular Biotechnology and Health Sciences, Molecular Biotechnology Center, University of Torino, Torino, 10126, Italy.
Adv Sci (Weinh). 2025 Jan;12(2):e2412419. doi: 10.1002/advs.202412419. Epub 2024 Nov 19.
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.
肿瘤内异质性显著阻碍了抗癌治疗的疗效。与在整体细胞水平产生药理学数据的药物扰动实验相比，单细胞RNA测序（scRNA-seq）技术提供了一种在单细胞分辨率下捕捉分子异质性的方法。在此，介绍了scPharm，这是一个将药理学图谱与scRNA-seq数据整合的计算框架，用于识别肿瘤内细胞的药理学亚群并确定定制药物的优先级。scPharm使用从基因集富集分析确定的标准化富集分数（NESs）来评估细胞身份基因在药物反应确定的基因列表中的分布。基于单细胞分辨率下NES与药物反应之间的强相关性，scPharm成功识别了雌激素受体阳性和人表皮生长因子受体2阳性的人乳腺癌组织中的敏感亚群，揭示了用厄洛替尼治疗的人PC9细胞耐药亚群的动态变化，并将其能力扩展到小鼠模型。通过与其他单细胞预测工具的比较评估，证实了其卓越的性能和计算效率。此外，scPharm通过衡量药物之间的补偿或增强作用来预测联合用药策略，并评估肿瘤微环境中健康细胞的药物毒性。总体而言，scPharm通过在单细胞分辨率下揭示治疗异质性，为癌症精准医学提供了一种新方法。
