Genome-scale regression analysis reveals a linear relationship for promoters and enhancers after combinatorial drug treatment
de Hoon, Michiel
KAUST DepartmentComputational Bioscience Research Center (CBRC)
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Online Publication Date2017-08-14
Print Publication Date2017-12-01
Permanent link to this recordhttp://hdl.handle.net/10754/625365
MetadataShow full item record
AbstractMotivation: Drug combination therapy for treatment of cancers and other multifactorial diseases has the potential of increasing the therapeutic effect, while reducing the likelihood of drug resistance. In order to reduce time and cost spent in comprehensive screens, methods are needed which can model additive effects of possible drug combinations. Results: We here show that the transcriptional response to combinatorial drug treatment at promoters, as measured by single molecule CAGE technology, is accurately described by a linear combination of the responses of the individual drugs at a genome wide scale. We also find that the same linear relationship holds for transcription at enhancer elements. We conclude that the described approach is promising for eliciting the transcriptional response to multidrug treatment at promoters and enhancers in an unbiased genome wide way, which may minimize the need for exhaustive combinatorial screens.
CitationRapakoulia T, Gao X, Huang Y, de Hoon M, Okada-Hatakeyama M, et al. (2017) Genome-scale regression analysis reveals a linear relationship for promoters and enhancers after combinatorial drug treatment. Bioinformatics. Available: http://dx.doi.org/10.1093/bioinformatics/btx503.
SponsorsThe research reported in this work was supported by RIKEN CLST Center Director’s Strategic Program MNC. Additional funding was provided by King Abdullah University of Science and Technology (KAUST), JSPS KAKENHI Grant No.15KT0084 and RIKEN Epigenome and Single Cell Project Grants to MO-H.
PublisherOxford University Press (OUP)
CollectionsArticles; Computer Science Program; Computer Science Program; Computational Bioscience Research Center (CBRC); Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Except where otherwise noted, this item's license is described as This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact firstname.lastname@example.org