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Single-cell RNA sequencing enables molecular heterogeneity to be captured at single-cell resolution. scPharm introduces a computational framework that integrates pharmacological profiles with scRNA-seq data to discover pharmacological subpopulations within tumours and prioritize tailored drugs. By using normalized enrichment scores from gene set enrichment analysis, it identifies sensitive and resistant cellular states in ER+ and HER2+ breast cancer tissue and tracks resistant dynamics in PC9 cells treated with erlotinib. The method also extends to a mouse model, outperforms competing single-cell prediction tools, predicts combination strategies via compensation or booster effects, and evaluates drug toxicity in healthy tumour microenvironment cells.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/scpharm-identifying-pharmacological-subpopulations-of-single-cells-for-precision-medicine-in-cancers/332949/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/scpharm-identifying-pharmacological-subpopulations-of-single-cells-for-precision-medicine-in-cancers/332949.png","ImageObject",300,407,{"name":92,"@type":93},"Aurelia","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-02","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does scPharm address in cancer precision medicine?","Question",{"text":113,"@type":114},"scPharm targets intratumour heterogeneity, which reduces the efficacy of anticancer therapies. It aims to identify pharmacological subpopulations at single-cell resolution to better match tailored drugs.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does scPharm connect drug response with single-cell transcriptomes?",{"text":118,"@type":114},"scPharm integrates pharmacological profiles with scRNA-seq data and uses normalized enrichment scores (NESs) from gene set enrichment analysis. It evaluates how distribution of cell identity genes relates to drug response–determined gene lists.",{"name":120,"@type":111,"acceptedAnswer":121},"What validations and downstream tasks does scPharm support?",{"text":122,"@type":114},"scPharm identifies sensitive subpopulations in ER+ and HER2+ breast cancer tissues, reveals resistant subpopulation dynamics in PC9 cells with erlotinib, and extends to a mouse model. It also compares performance against other tools, predicts combination drug strategies using compensation/booster effects, and evaluates drug toxicity in healthy tumour microenvironment cells.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},332949,1790099443,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":56,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},1099514068365,"https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1794309523707217728","RESEARCH ARTICLE  \n[www.advancedscience.com](www.advancedscience.com)  \nscPharm: Identifying Pharmacological Subpopulations of Single Cells for Precision Medicine in Cancers  \nPeng Tian, Jie Zheng, Keying Qiao, Yuxiao Fan, Yue Xu, Tao Wu, Shuting Chen, Yinuo Zhang, Bingyue Zhang, Chiara Ambrogio, and Haiyun Wang*  \nIntratumour 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.  \n1. Introduction  \nThe intrinsic heterogeneity of individual tumours poses a significant challenge in cancer treatment, often leading to the failure of anticancer therapies and patient death. Recent advances in single-cell sequencing technology have created opportunities for investigating molecular heterogeneity atthe single-cell level and have shown promise in precision cancer therapy. In recent years, technologies such as Perturb-seq,[1] expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing (ECCITE-seq)[2] and sci-Plex[3] have integrated single-cell sequencing with the compound- or clustered regularly interspaced short palindromic repeats (CRISPR)-mediated gene perturbation experiments, allowing the assessment of single-cell perturbation responses at the transcriptome or surface protein level. Despite their potential, these biotechnological platforms are still in their infancy.  \nNotably, the application of perturbationbased screening of bulk cells of cancer cell lines in vivo over the past decade has proven valuable in identifying cancer cell targets. Moreover, this screening method  \nP. Tian, J. Zheng, K. Qiao, Y. Fan, Y. Xu, T. Wu, S. Chen, Y. Zhang,  \nB. Zhang, H. Wang  \nResearch Center for Translational Medicine Shanghai East Hospital  \nSchool of Life Sciences and Technology Tongji University  \nShanghai 200092, China [E-mail:](E-mail: wanghaiyun@tongji.edu.cn)[ wanghaiyun@tongji.edu.cn](E-mail: wanghaiyun@tongji.edu.cn)  \nC. Ambrogio  \nDepartment of Molecular Biotechnology and Health Sciences Molecular Biotechnology Center  \nUniversity of Torino Torino 10126, Italy  \nThe ORCID identification number(s) for the author(s) of this article  \ncan be found under [https://doi.org/10.1002/advs.202412419](https://doi.org/10.1002/advs.202412419)[ ](https://doi.org/10.1002/advs.202412419)© 2024 The Author(s). Advanced Science published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \nDOI: 10.1002/ad","cbCairTYA9zI1JWE","https://ap.wps.com/l/cbCairTYA9zI1JWE","pdf",3803272,"English","# Introduction\n## Intratumour heterogeneity and limits of current therapies\n## Integration of single-cell sequencing with perturbation platforms\n## Bulk perturbation screening and pharmacogenomic resources\n## Need for computational pipelines linking single-cell and pharmacogenomics\n## Existing single-cell drug response prediction methods","[{\"question\":\"What problem does scPharm address in cancer precision medicine?\",\"answer\":\"scPharm targets intratumour heterogeneity, which reduces the efficacy of anticancer therapies. It aims to identify pharmacological subpopulations at single-cell resolution to better match tailored drugs.\"},{\"question\":\"How does scPharm connect drug response with single-cell transcriptomes?\",\"answer\":\"scPharm integrates pharmacological profiles with scRNA-seq data and uses normalized enrichment scores (NESs) from gene set enrichment analysis. It evaluates how distribution of cell identity genes relates to drug response–determined gene lists.\"},{\"question\":\"What validations and downstream tasks does scPharm support?\",\"answer\":\"scPharm identifies sensitive subpopulations in ER+ and HER2+ breast cancer tissues, reveals resistant subpopulation dynamics in PC9 cells with erlotinib, and extends to a mouse model. It also compares performance against other tools, predicts combination drug strategies using compensation/booster effects, and evaluates drug toxicity in healthy tumour microenvironment cells.\"}]","scPharm - Identifying Pharmacological Subpopulations of Single Cells for Precision Medicine in Cancers | PDF",1790000100,48]