Related Experiment Video
Updated: Jun 26, 2026
Quantifying Antibody-Dependent Cellular Cytotoxicity in a Tumor Spheroid Model: Application for Drug Discovery
Published on: April 26, 2024
scTumorDrug: predicting cell-type-specific drug responses for heterogeneous tumors
Qiang Zhang1, Qing Zhang1, Chunming Guo1
1Yunnan Key Laboratory of Cell Metabolism and Diseases, Center for Life Sciences, School of Life Sciences, Yunnan University, Easter Outer Ring Road, Chenggong District, Kunming 650500, China.
Abstract:
It is important to predict cell-type-specific drug responses within the heterogeneous tumors for precision medicine. The single-cell RNA sequencing (scRNA-seq) technique together with drug responses data provides opportunities for this mission. The previous methods were mainly evaluated in datasets derived from cancer cell lines, lacking direct validations on the real tumors derived from mouse models and clinical human samples. In this work, we integrated the labeled scRNA-seq, bulk RNA, and drug response data to develop the computational tool scTumorDrug to predict cell-type-specific drug responses for heterogeneous tumors. Overall, scTumorDrug achieved accurate predictions in public datasets derived from cell lines, mouse models, and clinical human samples, and outperformed previous methods in the selected datasets. Since there is no available experimental validation on drug responses from tumor subpopulations, we established the mouse bladder tumor model to investigate the cell-type-specific drug responses for the small molecule JQ1 by performing single nucleus RNA sequencing and spatial transcriptomics. The mouse model showed that the JQ1 treatment delayed the tumor progression though it did not completely eliminate the bladder tumors. The scTumorDrug found that the Mki67+ urothelial subpopulation was sensitive to JQ1, which was cross-validated by spatial transcriptomics. This observation also provided an explanation for JQ1 efficacy. To summarize, the scTumorDrug can be used in broad application scenarios and we provided cell-type-specific drug response prediction and validation.
Insights
Predicting cell-type-specific drug responses in tumors is crucial for precision medicine. Our new tool, scTumorDrug, accurately predicts these responses across various models and validates findings in a mouse bladder tumor model.
Area of Science:
- Computational biology
- Genomics
- Pharmacology
Background:
- Precision medicine requires predicting drug responses in heterogeneous tumors.
- Single-cell RNA sequencing (scRNA-seq) offers potential but lacks validation in real tumor samples.
- Previous computational tools primarily used cell line data, limiting real-world applicability.
Purpose of the Study:
- To develop and validate a computational tool, scTumorDrug, for predicting cell-type-specific drug responses in heterogeneous tumors.
- To integrate scRNA-seq, bulk RNA, and drug response data for enhanced prediction accuracy.
- To experimentally validate predictions in mouse models and human samples.
Main Methods:
- Integrated labeled scRNA-seq, bulk RNA, and drug response data.
- Developed the scTumorDrug computational tool.
- Established a mouse bladder tumor model for experimental validation using single-nucleus RNA sequencing and spatial transcriptomics.
Main Results:
- scTumorDrug demonstrated accurate predictions on cell line, mouse model, and human sample datasets.
- The tool outperformed existing methods in selected datasets.
- Experimental validation in a mouse model identified a JQ1-sensitive Mki67+ urothelial subpopulation, explaining drug efficacy.
Conclusions:
- scTumorDrug is a versatile tool for predicting cell-type-specific drug responses in diverse tumor contexts.
- The study provides crucial experimental validation for computational predictions in complex biological systems.
- Findings advance the application of scRNA-seq in precision oncology and drug development.
