Predicting drug response from single-cell expression profiles of tumours
Simona Pellecchia1,2, Gaetano Viscido1,3, Melania Franchini1,4
1Telethon Institute of Genetics and Medicine, Naples, Italy.
Background:
Intra-tumour heterogeneity (ITH) presents a significant obstacle in formulating effective treatment strategies in clinical practice. Single-cell RNA sequencing (scRNA-seq) has evolved as a powerful instrument for probing ITH at the transcriptional level, offering an unparalleled opportunity for therapeutic intervention.
Results:
Drug response prediction at the single-cell level is an emerging field of research that aims to improve the efficacy and precision of cancer treatments. Here, we introduce DREEP (Drug Response Estimation from single-cell Expression Profiles), a computational method that leverages publicly available pharmacogenomic screens from GDSC2, CTRP2, and PRISM and functional enrichment analysis to predict single-cell drug sensitivity from transcriptomic data. We validated DREEP extensively in vitro using several independent single-cell datasets with over 200 cancer cell lines and showed its accuracy and robustness. Additionally, we also applied DREEP to molecularly barcoded breast cancer cells and identified drugs that can selectively target specific cell populations.
Conclusions:
DREEP provides an in silico framework to prioritize drugs from single-cell transcriptional profiles of tumours and thus helps in designing personalized treatment strategies and accelerating drug repurposing studies. DREEP is available at https://github.com/gambalab/DREEP .
Insights
A new computational method, DREEP, predicts single-cell drug sensitivity from gene expression data. This tool aids in developing personalized cancer treatments by identifying effective drugs for specific tumor cell populations.
Area of Science:
- Computational biology
- Genomics
- Pharmacology
Background:
- Intra-tumor heterogeneity (ITH) complicates cancer treatment strategies.
- Single-cell RNA sequencing (scRNA-seq) is a key technology for analyzing ITH.
- Understanding cellular responses to drugs is crucial for precision medicine.
Purpose of the Study:
- To develop a computational method for predicting drug response at the single-cell level.
- To leverage transcriptomic data for estimating single-cell drug sensitivity.
- To facilitate personalized cancer therapy and drug repurposing.
Main Methods:
- Developed DREEP (Drug Response Estimation from single-cell Expression Profiles) computational method.
- Utilized publicly available pharmacogenomic screens (GDSC2, CTRP2, PRISM).
- Integrated functional enrichment analysis with transcriptomic data.
Main Results:
- DREEP accurately predicts single-cell drug sensitivity from transcriptomic profiles.
- Validated DREEP in vitro across diverse datasets (over 200 cancer cell lines).
- Identified drugs selectively targeting specific cell populations in breast cancer models.
Conclusions:
- DREEP offers an in silico framework for prioritizing drugs based on single-cell tumor profiles.
- Enables personalized treatment strategies and accelerates drug repurposing.
- Provides a valuable tool for advancing precision oncology.
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