Sammanfattning

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.
Originalspråkengelska
TidskriftNucleic Acids Research
Volym47
UtgåvaW1
Sidor (från-till)W43-W51
Antal sidor9
ISSN0305-1048
DOI
StatusPublicerad - 2 jul 2019
MoE-publikationstypA1 Tidskriftsartikel-refererad

Vetenskapsgrenar

  • 3122 Cancersjukdomar
  • 113 Data- och informationsvetenskap

Citera det här

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title = "DrugComb: an integrative cancer drug combination data portal",
abstract = "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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author = "Bulat Zagidullin and Aldahdooh, {Jehad M. F.} and Shuyu Zheng and Wenyu Wang and Yinyin Wang and Joseph Saad and Alina Malyutina and Mohieddin Jafari and Zia-ur-Rehman Tanoli and Alberto Pessia and Jing Tang",
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DrugComb : an integrative cancer drug combination data portal. / Zagidullin, Bulat; Aldahdooh, Jehad M. F.; Zheng, Shuyu; Wang, Wenyu; Wang, Yinyin; Saad, Joseph; Malyutina, Alina; Jafari, Mohieddin; Tanoli, Zia-ur-Rehman; Pessia, Alberto; Tang, Jing.

I: Nucleic Acids Research, Vol. 47, Nr. W1, 02.07.2019, s. W43-W51.

Forskningsoutput: TidskriftsbidragArtikelVetenskapligPeer review

TY - JOUR

T1 - DrugComb

T2 - an integrative cancer drug combination data portal

AU - Zagidullin, Bulat

AU - Aldahdooh, Jehad M. F.

AU - Zheng, Shuyu

AU - Wang, Wenyu

AU - Wang, Yinyin

AU - Saad, Joseph

AU - Malyutina, Alina

AU - Jafari, Mohieddin

AU - Tanoli, Zia-ur-Rehman

AU - Pessia, Alberto

AU - Tang, Jing

PY - 2019/7/2

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N2 - 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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