Abstract
BackgroundIt is necessary to evaluate the efficacy of individual drugs on patients to realize personalized medicine. Testing drugs on patients in clinical trial is the only way to evaluate the efficacy of drugs. The approach is labour intensive and requires overwhelming costs and a number of experiments. Therefore, preclinical model system has been intensively investigated for predicting the efficacy of drugs. Current computational drug sensitivity prediction approaches use general biological network modules as their prediction features. Therefore, they miss indirect effectors or the effects from tissue-specific interactions.ResultsWe developed cell line specific functional modules. Enriched scores of functional modules are utilized as cell line specific features to predict the efficacy of drugs. Cell line specific functional modules are clusters of genes, which have similar biological functions in cell line specific networks. We used linear regression for drug efficacy prediction. We assessed the prediction performance in leave-one-out cross-validation (LOOCV). Our method was compared with elastic net model, which is a popular model for drug efficacy prediction. In addition, we analysed drug sensitivity-associated functions of five drugs - lapatinib, erlotinib, raloxifene, tamoxifen and gefitinib- by our model.ConclusionsOur model can provide cell line specific drug efficacy prediction and also provide functions which are associated with drug sensitivity. Therefore, we could utilize drug sensitivity associated functions for drug repositioning or for suggesting secondary drugs for overcoming drug resistance.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1078-6) contains supplementary material, which is available to authorized users.
Highlights
It is necessary to evaluate the efficacy of individual drugs on patients to realize personalized medicine
Our model can provide cell line specific drug efficacy prediction and provide functions which are associated with drug sensitivity
Gefitinib is a first-line drug for advanced nonsmall-cell lung carcinoma (NSCLC) patients, but only 20 ~ 30 % patients are sensitive to Gefitinib (Fig. 1) [1]
Summary
It is necessary to evaluate the efficacy of individual drugs on patients to realize personalized medicine. Testing drugs on patients in clinical trial is the only way to evaluate the efficacy of drugs. Current computational drug sensitivity prediction approaches use general biological network modules as their prediction features. They miss indirect effectors or the effects from tissue-specific interactions. It is important to predict drug efficacy by genomic disease signatures for realizing personalized therapy. There are two types of methods for identifying the efficacy of a drug; clinical trials and computational methods. Clinical trial is much accurate in assessing drug efficacy and toxicity, it requires overwhelming cost and a number of tests.
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