TY - JOUR
T1 - Genome-scale regression analysis reveals a linear relationship for promoters and enhancers after combinatorial drug treatment
AU - Rapakoulia, Trisevgeni
AU - Gao, Xin
AU - Huang, Yi
AU - de Hoon, Michiel
AU - Okada-Hatakeyama, Mariko
AU - Suzuki, Harukazu
AU - Arner, Erik
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: The 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.
PY - 2017/8/14
Y1 - 2017/8/14
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/625365
UR - https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btx503/4082269/Genomescale-regression-analysis-reveals-a-linear
UR - http://www.scopus.com/inward/record.url?scp=85042081572&partnerID=8YFLogxK
U2 - 10.1093/bioinformatics/btx503
DO - 10.1093/bioinformatics/btx503
M3 - Article
C2 - 28961713
SN - 1367-4803
VL - 33
SP - 3696
EP - 3700
JO - Bioinformatics
JF - Bioinformatics
IS - 23
ER -
