article · Cancer Research
Tumours often contain diverse cell subpopulations that respond differently to treatments, leading to therapeutic failure and drug resistance. Translating single-cell RNA sequencing data into targeted interventions remains difficult due to a shortage of specialised drug discovery tools. To address this, a transparent computational framework named scIDUC was created to predict drug efficacy for individual cells by combining single-cell transcriptomic profiles with large pan-cancer cell line screening datasets. The tool accurately classifies cells according to their therapeutic responses across several cancer types, including rhabdomyosarcoma, pancreatic ductal adenocarcinoma, and castration-resistant prostate cancer. It successfully identified effective treatments for cell groups showing intrinsic resistance or microenvironment-driven resistance to standard therapies, matching original experimental data. Furthermore, predictions for newly derived therapy-resistant cell lines were confirmed through laboratory experiments.
Cancer treatments frequently fail because distinct cell groups within the same tumour resist standard therapies. By accurately predicting how individual cells respond to specific medicines, this approach can help scientists design treatments that target resistant subpopulations directly, potentially overcoming drug resistance and improving outcomes for patients with complex, heterogeneous cancers.
This computational tool could aid oncology drug discovery teams, biotechnology companies, and translational researchers in identifying effective drugs for resistant tumour subpopulations from single-cell data. The research appears applied and tested, having been validated against prior experimental datasets and confirmed using in vitro experiments on newly developed therapy-resistant cell lines.
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Single-cell RNA sequencing (scRNA-seq) greatly advanced the understanding of intratumoral heterogeneity by identifying distinct cancer cell subpopulations. However, translating biological differences into treatment strategies is challenging due to a lack of tools to facilitate efficient drug discovery that tackles heterogeneous tumors. Developing such approaches requires accurate prediction of drug response at the single-cell level to offer therapeutic options to specific cell subpopulations. Here, we developed a transparent computational framework (nicknamed scIDUC) to predict therapeutic efficacies on an individual cell basis by integrating single-cell transcriptomic profiles with large, data-rich pan-cancer cell line screening data sets. This method achieved high accuracy in separating cells into their correct cellular drug response statuses. In three distinct prospective tests covering different diseases (rhabdomyosarcoma, pancreatic ductal adenocarcinoma, and castration-resistant prostate cancer), the predicted results using scIDUC were accurate and mirrored biological expectations. In the first two tests, the framework identified drugs for cell subpopulations that were resistant to standard-of-care (SOC) therapies due to intrinsic resistance or tumor microenvironmental effects, and the results showed high consistency with experimental findings from the original studies. In the third test using newly generated SOC therapy-resistant cell lines, scIDUC identified efficacious drugs for the resistant line, and the predictions were validated with in vitro experiments. Together, this study demonstrates the potential of scIDUC to quickly translate scRNA-seq data into drug responses for individual cells, displaying the potential as a tool to improve the treatment of heterogenous tumors. SIGNIFICANCE: A versatile method that infers cell-level drug response in scRNA-seq data facilitates the development of therapeutic strategies to target heterogeneous subpopulations within a tumor and address issues such as treatment failure and resistance.
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DOI: 10.1158/0008-5472.can-23-3005
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