Abstract
BackgroundAccurate prediction of anticancer drug responses in cell lines is a crucial step to accomplish the precision medicine in oncology. Although many popular computational models have been proposed towards this non-trivial issue, there is still room for improving the prediction performance by combining multiple types of genome-wide molecular data.ResultsWe first demonstrated an observation on the CCLE and GDSC datasets, i.e., genetically similar cell lines always exhibit higher response correlations to structurally related drugs. Based on this observation we built a cell line-drug complex network model, named CDCN model. It captures different contributions of all available cell line-drug responses through cell line similarities and drug similarities. We executed anticancer drug response prediction on CCLE and GDSC independently. The result is significantly superior to that of some existing studies. More importantly, our model could predict the response of new drug to new cell line with considerable performance. We also divided all possible cell lines into “sensitive” and “resistant” groups by their response values to a given drug, the prediction accuracy, sensitivity, specificity and goodness of fit are also very promising.ConclusionCDCN model is a comprehensive tool to predict anticancer drug responses. Compared with existing methods, it is able to provide more satisfactory prediction results with less computational consumption.
Highlights
Accurate prediction of anticancer drug responses in cell lines is a crucial step to accomplish the precision medicine in oncology
We selected 491 cancer cell lines from Cell Line Encyclopedia (CCLE), downloaded the chemical structure files of 23 drugs from PubChem Compound, and obtained a cell line-drug response matrix consisting of 11,293 entries, of which 423 (3.75%) are missing values
It is anticipated because both Cell line similarity network (CSN) and Drug similarity network (DSN) models use less information compared with our model
Summary
Accurate prediction of anticancer drug responses in cell lines is a crucial step to accomplish the precision medicine in oncology. Many popular computational models have been proposed towards this non-trivial issue, there is still room for improving the prediction performance by combining multiple types of genome-wide molecular data. The inherent heterogeneity of cancers always makes the same cancer patients exhibiting different anticancer drug responses, which is a major difficulty in cancer treatment. It is critical to accurately predict the therapy responses of patients based on their molecular and clinical profiles [1, 2]. It supplies a golden opportunity to translate massive data into knowledge of tumor biology and improve anticancer drug response prediction. Many computational methods have greatly contributed to this non-trivial issue [3,4,5,6]. Supervised learning technique is one of the most widely used
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