SynergyFinder Plus:Toward Better Interpretation and Annotation of Drug Combination Screening Datasets

作     者：Shuyu Zheng Wenyu Wang Jehad Aldahdooh Alina Malyutina Tolou Shadbahr Ziaurrehman Tanoli Alberto Pessia Jing Tang Shuyu Zheng;Wenyu Wang;Jehad Aldahdooh;Alina Malyutina;Tolou Shadbahr;Ziaurrehman Tanoli;Alberto Pessia;Jing Tang

作者机构：Research Program in Systems OncologyFaculty of MedicineUniversity of HelsinkiHelsinki 00290Finland 

出 版 物：《Genomics, Proteomics & Bioinformatics》 (基因组蛋白质组与生物信息学报（英文版）)

年 卷 期：2022年第20卷第3期

页      面：587-596页

核心收录：

学科分类：0710[理学-生物学] 1006[医学-中西医结合] 10[医学] 100602[医学-中西医结合临床] 

基　　金：supported by the European Research Council(ERC) starting grant DrugComb (informatics approaches for the rational selection of personalized cancer drug combinations)(Grant No.716063) the European Commission H2020EOSC-life (providing an open collaborative space for digital biology in Europe)(Grant No.824087) the Academy of Finland grant (Grant No.317680) the Sigrid Juselius Foundation grant funded by the University of Helsinki through theDoctoral Program of Biomedicine (DPBM) personal grants from K.Albin Johanssons Stiftelse and Biomedicum Helsinki Foundation personal grant from K.Albin Johanssons Stiftelse funded by the University of Helsinki through the Doctoral Program of Integrative Life Science (ILS) personal grant from the Cancer Foundation Finland 

主　　题：SynergyFinder Drug combination Synergy modeling Drug discovery Drug combination sensitivity analysis 

摘      要：Combinatorial therapies have been recently proposed to improve the efficacy of anticancer treatment. The Synergy Finder R package is a software used to analyze pre-clinical drug combination datasets. Here, we report the major updates to the Synergy Finder R package for improved interpretation and annotation of drug combination screening results. Unlike the existing implementations, the updated Synergy Finder R package includes five main innovations. 1) We extend the mathematical models to higher-order drug combination data analysis and implement dimension reduction techniques for visualizing the synergy landscape. 2) We provide a statistical analysis of drug combination synergy and sensitivity with confidence intervals and P values. 3)We incorporate a synergy barometer to harmonize multiple synergy scoring methods to provide a consensus metric for synergy. 4) We evaluate drug combination synergy and sensitivity to provide an unbiased interpretation of the clinical potential. 5) We enable fast annotation of drugs and cell lines, including their chemical and target information. These annotations will improve the interpretation of the mechanisms of action of drug combinations. To facilitate the use of the R package within the drug discovery community, we also provide a web server at www.s *** as a user-friendly interface to enable a more fexible and versatile analysis of drug combination data.