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.
BMC Medicine
|December 2, 2023
Summary
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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