Zhou Xianxiao, Wu Ling, Wang Minghui, Wu Guojun, Zhang Bin
Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, United States.
Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, United States.
Brief Bioinform. 2024 Nov 22;26(1). doi: 10.1093/bib/bbaf054.
Combination therapy has become increasingly important for treating complex diseases which often involve multiple pathways and targets. However, experimental screening of drug combinations is costly and time-consuming. The availability of large-scale transcriptomic datasets (e.g. CMap and LINCS) from in vitro drug treatment experiments makes it possible to computationally predict drug combinations with synergistic effects. Towards this end, we developed a computational approach, termed Identification of Drug Combinations via Multi-Set Operations (iDOMO), to predict drug synergy based on multi-set operations of drug and disease gene signatures. iDOMO quantifies the synergistic effect of a pair of drugs by taking into account the combination's beneficial and detrimental effects on treating a disease. We evaluated iDOMO, in a DREAM Challenge dataset with the matched, pre- and post-treatment gene expression data and cell viability information. We further evaluated the performance of iDOMO by concordance index and Spearman correlation on predicting the Highest Single Agency (HSA) synergy scores for four most common cancer types in two large-scale drug combination databases, showing that iDOMO significantly outperformed two existing popular drug combination approaches including the Therapeutic Score and the SynergySeq Orthogonality Score. Application of iDOMO to triple-negative breast cancer (TNBC) identified drug pairs with potential synergistic effects, with the combination of trifluridine and monobenzone being the most synergistic. Our in vitro experiments confirmed that the top predicted drug combination exerted a significant synergistic effect in inhibiting TNBC cell growth. In summary, iDOMO is an effective method for the in silico screening of synergistic drug combinations and will be a valuable tool for the development of novel therapeutics for complex diseases.
联合疗法对于治疗通常涉及多种途径和靶点的复杂疾病变得越来越重要。然而，药物组合的实验筛选成本高昂且耗时。来自体外药物治疗实验的大规模转录组数据集（如CMap和LINCS）的可用性使得通过计算预测具有协同效应的药物组合成为可能。为此，我们开发了一种计算方法，称为通过多集操作识别药物组合（iDOMO），以基于药物和疾病基因特征的多集操作来预测药物协同作用。iDOMO通过考虑组合对治疗疾病的有益和有害影响来量化一对药物的协同效应。我们在一个具有匹配的治疗前和治疗后基因表达数据以及细胞活力信息的DREAM挑战数据集中评估了iDOMO。我们还通过一致性指数和Spearman相关性，在两个大规模药物组合数据库中预测四种最常见癌症类型的最高单药疗效（HSA）协同评分时，进一步评估了iDOMO的性能，结果表明iDOMO明显优于两种现有的流行药物组合方法，即治疗评分和协同序列正交性评分。将iDOMO应用于三阴性乳腺癌（TNBC），鉴定出具有潜在协同效应的药物对，其中曲氟尿苷和莫诺苯宗的组合协同性最强。我们的体外实验证实，预测排名靠前的药物组合在抑制TNBC细胞生长方面发挥了显著的协同作用。总之，iDOMO是一种用于计算机筛选协同药物组合的有效方法，将成为开发复杂疾病新型疗法的宝贵工具。
