Abstract
MotivationWhile drug combination therapies are a well-established concept in cancer treatment, identifying novel synergistic combinations is challenging due to the size of combinatorial space. However, computational approaches have emerged as a time- and cost-efficient way to prioritize combinations to test, based on recently available large-scale combination screening data. Recently, Deep Learning has had an impact in many research areas by achieving new state-of-the-art model performance. However, Deep Learning has not yet been applied to drug synergy prediction, which is the approach we present here, termed DeepSynergy. DeepSynergy uses chemical and genomic information as input information, a normalization strategy to account for input data heterogeneity, and conical layers to model drug synergies.ResultsDeepSynergy was compared to other machine learning methods such as Gradient Boosting Machines, Random Forests, Support Vector Machines and Elastic Nets on the largest publicly available synergy dataset with respect to mean squared error. DeepSynergy significantly outperformed the other methods with an improvement of 7.2% over the second best method at the prediction of novel drug combinations within the space of explored drugs and cell lines. At this task, the mean Pearson correlation coefficient between the measured and the predicted values of DeepSynergy was 0.73. Applying DeepSynergy for classification of these novel drug combinations resulted in a high predictive performance of an AUC of 0.90. Furthermore, we found that all compared methods exhibit low predictive performance when extrapolating to unexplored drugs or cell lines, which we suggest is due to limitations in the size and diversity of the dataset. We envision that DeepSynergy could be a valuable tool for selecting novel synergistic drug combinations.Availability and implementationDeepSynergy is available via www.bioinf.jku.at/software/DeepSynergy.Supplementary information Supplementary data are available at Bioinformatics online.
Highlights
Administering drug combinations instead of monotherapy can lead to an increased efficacy compared to single drug treatments (Csermely et al, 2013; Jia et al, 2009)
To further characterize the predictive performance of DeepSynergy and to give comparable measures, we provide performance measures that are typical for classification tasks: area under the receiver operator characteristics curve (ROC AUC), area under the precision recall curve (PR AUC), accuracy (ACC), balanced accuracy (BACC), precision (PREC), sensitivity (TPR), specificity (TNR) and Cohen’s Kappa
We have developed a novel Deep Learning based method, DeepSynergy, that predicts synergy scores of drug combinations for cancer cell lines with high accuracy
Summary
Administering drug combinations instead of monotherapy can lead to an increased efficacy compared to single drug treatments (Csermely et al, 2013; Jia et al, 2009). Testing the complete combinatorial space with HTS is unfeasible (Goswami et al, 2015; Morris et al, 2016) Computational methods such as machine learning models offer the possibility to efficiently explore the large synergistic space. Methods range from systems biology methods (Feala et al, 2010), kinetic models (Sun et al, 2016), mixed integer linear programming methods based on the diseased gene set (Pang et al, 2014), computational methods based on gene expression profiles after treatment with single drugs and dose response curves (Goswami et al, 2015; Yang et al, 2015), to machine learning approaches including Random Forests and Naive Bayes methods (Li et al, 2015; Wildenhain et al, 2015) These methods are restricted to certain pathways, targets or cell lines, or require transcriptomic data of cell lines under compound treatment. A large HTS synergy study (O’Neil et al, 2016) with more than 20 000 synergy measurements was performed, which offers the possibility to evaluate computational methods for predicting novel drug combinations. We found that DeepSynergy can predict drug synergies of novel combinations within the space of explored drugs and cell lines with high accuracy and significantly outperforms the other
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