Genome-scale regression analysis reveals a linear relationship for promoters and enhancers after combinatorial drug treatment

T. Rapakoulia, X. Gao, Y. Huang, M. de Hoon, M. Okada-Hatakeyama, H. Suzuki, E. Arner
Bioinformatics, btx503, (2017)

Genome-scale regression analysis reveals a linear relationship for promoters and enhancers after combinatorial drug treatment

Keywords

Genome-scale, Regression analysis, Linear relationship, Promoters, Enhancers after combinatorial, Drug treatment

Abstract

Motivation: 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.

Code

https://doi.org/10.1093/bioinformatics/btx503

Sources

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