BRCA-Monet: A breast cancer specific drug treatment mode-of-action network for treatment effective prediction using large scale microarray database

Chifeng Ma, Hung I H Chen, Mario Flores, Yufei Huang, Yidong Chen

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Original languageEnglish
Article numberS5
JournalBMC Systems Biology
Volume7
Issue numberSUPPL 5
DOIs
StatePublished - Dec 9 2013

Fingerprint

Drug therapy
Microarrays
Breast Cancer
Microarray
Drugs
Databases
Breast Neoplasms
Connectivity
Prediction
Pharmaceutical Preparations
Treatment Effects
Cells
MCF-7 Cells
Cell Line
Therapeutics
Line
Cell
World Wide Web
Quality control
Websites

ASJC Scopus subject areas

  • Molecular Biology
  • Structural Biology
  • Applied Mathematics
  • Modeling and Simulation
  • Computer Science Applications

Cite this

BRCA-Monet : A breast cancer specific drug treatment mode-of-action network for treatment effective prediction using large scale microarray database. / Ma, Chifeng; Chen, Hung I H; Flores, Mario; Huang, Yufei; Chen, Yidong.

In: BMC Systems Biology, Vol. 7, No. SUPPL 5, S5, 09.12.2013.

Research output: Contribution to journalArticle

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title = "BRCA-Monet: A breast cancer specific drug treatment mode-of-action network for treatment effective prediction using large scale microarray database",
abstract = "Background: Connectivity map (cMap) is a recent developed dataset and algorithm for uncovering and understanding the treatment effect of small molecules on different cancer cell lines. It is widely used but there are still remaining challenges for accurate predictions.Method: Here, we propose BRCA-MoNet, a network of drug mode of action (MoA) specific to breast cancer, which is constructed based on the cMap dataset. A drug signature selection algorithm fitting the characteristic of cMap data, a quality control scheme as well as a novel query algorithm based on BRCA-MoNet are developed for more effective prediction of drug effects.Result: BRCA-MoNet was applied to three independent data sets obtained from the GEO database: Estrodial treated MCF7 cell line, BMS-754807 treated MCF7 cell line, and a breast cancer patient microarray dataset. In the first case, BRCA-MoNet could identify drug MoAs likely to share same and reverse treatment effect. In the second case, the result demonstrated the potential of BRCA-MoNet to reposition drugs and predict treatment effects for drugs not in cMap data. In the third case, a possible procedure of personalized drug selection is showcased.Conclusions: The results clearly demonstrated that the proposed BRCA-MoNet approach can provide increased prediction power to cMap and thus will be useful for identification of new therapeutic candidates.Website: The web based application is developed and can be access through the following link http://compgenomics.utsa.edu/BRCAMoNet/.",
keywords = "Breast Cancer Mode of Action Network (BRCA-MoNet), Connectivity Map, Mode of Action (MoA)",
author = "Chifeng Ma and Chen, {Hung I H} and Mario Flores and Yufei Huang and Yidong Chen",
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T2 - A breast cancer specific drug treatment mode-of-action network for treatment effective prediction using large scale microarray database

AU - Ma, Chifeng

AU - Chen, Hung I H

AU - Flores, Mario

AU - Huang, Yufei

AU - Chen, Yidong

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N2 - Background: Connectivity map (cMap) is a recent developed dataset and algorithm for uncovering and understanding the treatment effect of small molecules on different cancer cell lines. It is widely used but there are still remaining challenges for accurate predictions.Method: Here, we propose BRCA-MoNet, a network of drug mode of action (MoA) specific to breast cancer, which is constructed based on the cMap dataset. A drug signature selection algorithm fitting the characteristic of cMap data, a quality control scheme as well as a novel query algorithm based on BRCA-MoNet are developed for more effective prediction of drug effects.Result: BRCA-MoNet was applied to three independent data sets obtained from the GEO database: Estrodial treated MCF7 cell line, BMS-754807 treated MCF7 cell line, and a breast cancer patient microarray dataset. In the first case, BRCA-MoNet could identify drug MoAs likely to share same and reverse treatment effect. In the second case, the result demonstrated the potential of BRCA-MoNet to reposition drugs and predict treatment effects for drugs not in cMap data. In the third case, a possible procedure of personalized drug selection is showcased.Conclusions: The results clearly demonstrated that the proposed BRCA-MoNet approach can provide increased prediction power to cMap and thus will be useful for identification of new therapeutic candidates.Website: The web based application is developed and can be access through the following link http://compgenomics.utsa.edu/BRCAMoNet/.

AB - Background: Connectivity map (cMap) is a recent developed dataset and algorithm for uncovering and understanding the treatment effect of small molecules on different cancer cell lines. It is widely used but there are still remaining challenges for accurate predictions.Method: Here, we propose BRCA-MoNet, a network of drug mode of action (MoA) specific to breast cancer, which is constructed based on the cMap dataset. A drug signature selection algorithm fitting the characteristic of cMap data, a quality control scheme as well as a novel query algorithm based on BRCA-MoNet are developed for more effective prediction of drug effects.Result: BRCA-MoNet was applied to three independent data sets obtained from the GEO database: Estrodial treated MCF7 cell line, BMS-754807 treated MCF7 cell line, and a breast cancer patient microarray dataset. In the first case, BRCA-MoNet could identify drug MoAs likely to share same and reverse treatment effect. In the second case, the result demonstrated the potential of BRCA-MoNet to reposition drugs and predict treatment effects for drugs not in cMap data. In the third case, a possible procedure of personalized drug selection is showcased.Conclusions: The results clearly demonstrated that the proposed BRCA-MoNet approach can provide increased prediction power to cMap and thus will be useful for identification of new therapeutic candidates.Website: The web based application is developed and can be access through the following link http://compgenomics.utsa.edu/BRCAMoNet/.

KW - Breast Cancer Mode of Action Network (BRCA-MoNet)

KW - Connectivity Map

KW - Mode of Action (MoA)

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