Journal
NUCLEIC ACIDS RESEARCH
Volume 47, Issue W1, Pages W43-W51Publisher
OXFORD UNIV PRESS
DOI: 10.1093/nar/gkz337
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Funding
- European Research Council (ERC) starting grant Drug-Comb (Informatics approaches for the rationale selection of personalized cancer drug combinations) [716063]
- European Commission [824087]
- Academy of Finland [317680]
- China Scholarship Council [201706740080]
- European Research Council (ERC) starting grant agreement DrugComb [716063]
- Finland's EDUFI Fellowship [TM-18-10928]
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Drug combination therapy has the potential to enhance efficacy, reduce dose-dependent toxicity and prevent the emergence of drug resistance. However, discovery of synergistic and effective drug combinations has been a laborious and often serendipitous process. In recent years, identification of combination therapies has been accelerated due to the advances in high-throughput drug screening, but informatics approaches for systems-level data management and analysis are needed. To contribute toward this goal, we created an open-access data portal called DrugComb (https://drugcomb.fimm.fi) where the results of drug combination screening studies are accumulated, standardized and harmonized. Through the data portal, we provided a web server to analyze and visualize users' own drug combination screening data. The users can also effectively participate a crowdsourcing data curation effect by depositing their data at DrugComb. To initiate the data repository, we collected 437 932 drug combinations tested on a variety of cancer cell lines. We showed that linear regression approaches, when considering chemical fingerprints as predictors, have the potential to achieve high accuracy of predicting the sensitivity of drug combinations. All the data and informatics tools are freely available in DrugComb to enable a more efficient utilization of data resources for future drug combination discovery.
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