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Genome-scale Regression Analysis Reveals a Linear Relationship for Promoters and Enhancers After Combinatorial Drug Treatment

Start Date: November 7, 2018 12:00 PM
End Date: November 7, 2018 1:00 PM

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. We 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.

Trisevgeni Rapakoulia is a Ph.D. candidate at King Abdullah University of Science and Technology supervised by Prof. Xin Gao. Broad areas of interest include bioinformatics and machine learning. Research field: study the effects of drugs and drug combinations in the transcriptome level.