DrugComb: an integrative cancer drug combination data portal

Research output: Contribution to journalArticleScientificpeer-review

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.
Original languageEnglish
JournalNucleic Acids Research
ISSN0305-1048
DOIs
Publication statusPublished - 8 May 2019
MoE publication typeA1 Journal article-refereed

Fields of Science

  • 3122 Cancers
  • 113 Computer and information sciences

Cite this

@article{2655f78a96a64733a732ac10bdb017bd,
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.",
keywords = "3122 Cancers, 113 Computer and information sciences",
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",
year = "2019",
month = "5",
day = "8",
doi = "10.1093/nar/gkz337",
language = "English",
journal = "Nucleic Acids Research",
issn = "0305-1048",
publisher = "Oxford University Press",

}

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 - 2019/5/8

Y1 - 2019/5/8

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.

AB - 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.

KW - 3122 Cancers

KW - 113 Computer and information sciences

U2 - 10.1093/nar/gkz337

DO - 10.1093/nar/gkz337

M3 - Article

JO - Nucleic Acids Research

JF - Nucleic Acids Research

SN - 0305-1048

ER -