Drug combinations can expand therapeutic options and address cancer’s resistance. However, the combinatorial space is enormous precluding its systematic exploration. Therefore, synergy prediction strategies are essential. We here present an approach to prioritise drug combinations in high-throughput screens and to stratify synergistic responses. At the core of our approach is the observation that the likelihood of synergy increases when targeting proteins with either strong functional similarity or dissimilarity. We estimate the similarity applying a multitask machine learning approach to basal gene expression and response to single drugs. We tested 7 protein target pairs (representing 29 combinations) and predicted their synergies in 33 breast cancer cell lines. In addition, we experimentally validated predicted synergy of the BRAF/insulin receptor combination (Dabrafenib/BMS-754807) in 48 colorectal cancer cell lines. We anticipate that our approaches can be used for prioritization of drug combinations in large scale screenings, and to maximize the efficacy of drugs already known to induce synergy, ultimately enabling patient stratification.

药物组合可以扩大治疗选择范围并解决癌症的耐药性。但是，组合空间是巨大的，无法进行系统的探索。因此，协同预测策略至关重要。在这里，我们提出了一种在高通量筛选中优先考虑药物组合并分层协同反应的方法。我们方法的核心是观察到，针对具有强大功能相似性或不相似性的蛋白质，协同作用的可能性会增加。我们估计将多任务机器学习方法应用于基础基因表达和对单一药物的反应的相似性。我们测试了7个蛋白质靶标对（代表29种组合），并预测了它们在33个乳腺癌细胞系中的协同作用。此外，我们通过实验验证了BRAF /胰岛素受体组合（Dabrafenib / BMS-754807）在48个大肠癌细胞系中的预测协同作用。我们预计，我们的方法可用于大规模筛选中药物组合的优先排序，并使已知可诱导协同作用的药物的功效最大化，从而最终实现患者分层。