Predicting drug response from single-cell expression profiles (DREEP)

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 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. 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.

单细胞水平的药物反应预测是新兴研究领域，旨在提升癌症治疗的疗效与精准度。在此，我们提出DREEP（Drug Response Estimation from single-cell Expression Profiles）——一种依托公开可得的药物基因组学筛选数据与功能富集分析，从转录组数据中预测单细胞药物敏感性的计算方法。我们依托包含200余种癌细胞系的多组独立单细胞数据集，在体外环境中对DREEP进行了全面验证，证实了其准确性与鲁棒性。此外，我们将DREEP应用于分子条形码标记的乳腺癌细胞，筛选出了可选择性靶向特定细胞群的药物。DREEP提供了一套计算机模拟框架，可从肿瘤单细胞转录谱中对药物进行优先级排序，从而助力个性化治疗策略的设计与药物重定位研究的推进。DREEP的开源代码可通过https://github.com/gambalab/DREEP获取。