PDF(Pubmed)
Abstract：
Cancer is typically treated with combinatorial therapy, and such combinations may be synergistic. However, discovery of these combinations has proven difficult as brute force combinatorial screening approaches are both logistically complex and resource-intensive. Therefore, computational approaches to augment synergistic drug discovery are of interest, but current approaches are limited by their dependencies on combinatorial drug screening training data or molecular profiling data. These dataset dependencies can limit the number and diversity of drugs for which these approaches can make inferences. Herein, we describe a novel computational framework, ReCorDE (Recurrent Correlation of Drugs with Enrichment), that uses publicly-available cell line-derived monotherapy cytotoxicity datasets to identify drug classes targeting shared vulnerabilities across multiple cancer lineages; and we show how these inferences can be used to augment synergistic drug combination discovery. Additionally, we demonstrate in preclinical models that a drug class combination predicted by ReCorDE to target shared vulnerabilities (PARP inhibitors and Aurora kinase inhibitors) exhibits class-class synergy across lineages. ReCorDE functions independently of combinatorial drug screening and molecular profiling data, using only extensive monotherapy cytotoxicity datasets as its input. This allows ReCorDE to make robust inferences for a large, diverse array of drugs. In conclusion, we have described a novel framework for the identification of drug classes targeting shared vulnerabilities using monotherapy cytotoxicity datasets, and we showed how these inferences can be used to aid discovery of novel synergistic drug combinations.
摘要：
癌症通常用联合疗法治疗，并且这样的组合可以是协同的。然而,这些组合的发现已被证明是困难的，因为蛮力组合筛选方法在逻辑上既复杂又资源密集。因此,增强协同药物发现的计算方法是令人感兴趣的，但目前的方法受限于它们对组合药物筛选训练数据或分子谱分析数据的依赖性.这些数据集依赖性可以限制这些方法可以推断的药物的数量和多样性。在这里，我们描述了一个新的计算框架，ReCorDE（药物与富集的经常性相关性），使用公开的细胞系衍生的单一疗法细胞毒性数据集来识别针对多个癌症谱系的共享漏洞的药物类别;我们展示了这些推论如何用于增强协同药物组合发现。此外,我们在临床前模型中证明,ReCorDE预测的针对共有漏洞的药物类别组合(PARP抑制剂和Aurora激酶抑制剂)在不同谱系间表现出类别协同作用.ReCorDE的功能独立于组合药物筛选和分子谱分析数据，仅使用广泛的单一疗法细胞毒性数据集作为其输入。这使得ReCorDE能够对大型的，各种各样的药物。总之,我们已经描述了一种新的框架，用于使用单一疗法的细胞毒性数据集识别针对共享漏洞的药物类别，我们展示了如何利用这些推论来帮助发现新的协同药物组合。
