title: DIGRE: Drug-Induced Genomic Residual Effect Model for Successful Prediction of Multidrug Effects.
abstract: Multidrug regimens are a promising strategy for improving therapeutic efficacy and reducing side effects, especially for complex disorders such as cancer. However, the use of multidrug therapies is very challenging, due to a lack of understanding of the mechanisms of drug interactions. We herein present a novel computational approach-Drug-Induced Genomic Residual Effect (DIGRE) Computational Model-to predict drug combination effects by explicitly modeling drug response curves and gene expression changes after drug treatments. The prediction performance of DIGRE was evaluated using two datasets: (i) OCI-LY3 B-lymphoma cells treated with 14 different drugs and (ii) MCF breast cancer cells treated with combinations of gefitinib and docetaxel at different doses. In both datasets, the predicted drug combination effects significantly correlated with the experimental results. The results indicated the model was useful in predicting drug combination effects, which may greatly facilitate the discovery of new, effective multidrug therapies.
authors.affiliation: Department of Clinical Sciences, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center Dallas, Texas, USA.
authors.forename: J
authors.initials: J
authors.lastname: Yang
authors.affiliation: Department of Clinical Sciences, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center Dallas, Texas, USA.
authors.forename: H
authors.initials: H
authors.lastname: Tang
authors.affiliation: Department of Immunology, University of Texas Southwestern Medical Center Dallas, Texas, USA ; Laboratory of Disease Genomics and Individualized Medicine, Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences Beijing, China.
authors.forename: Y
authors.initials: Y
authors.lastname: Li
authors.affiliation: Department of Clinical Sciences, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center Dallas, Texas, USA.
authors.forename: R
authors.initials: R
authors.lastname: Zhong
authors.affiliation: Department of Clinical Sciences, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center Dallas, Texas, USA.
authors.forename: T
authors.initials: T
authors.lastname: Wang
authors.affiliation: Department of Systems Medicine and Bioengineering, Houston Methodist Research Institute, Weill Cornell Medical College Houston, Texas, USA.
authors.forename: Stc
authors.initials: S
authors.lastname: Wong
authors.affiliation: Department of Clinical Sciences, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center Dallas, Texas, USA.
authors.forename: G
authors.initials: G
authors.lastname: Xiao
authors.affiliation: Department of Clinical Sciences, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center Dallas, Texas, USA ; Simmons Cancer Center, University of Texas Southwestern Medical Center Dallas, Texas, USA.
authors.forename: Y
authors.initials: Y
authors.lastname: Xie