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Prioritization of candidate cancer drugs based on a drug functional similarity network constructed by integrating pathway activities and drug activities.

ABSTRACT: Due to the speed, efficiency, relative risk, and lower costs compared to traditional drug discovery, the prioritization of candidate drugs for repurposing against cancers of interest has attracted the attention of experts in recent years. Herein, we present a powerful computational approach, termed prioritization of candidate drugs (PriorCD), for the prioritization of candidate cancer drugs based on a global network propagation algorithm and a drug-drug functional similarity network constructed by integrating pathway activity profiles and drug activity profiles. This provides a new approach to drug repurposing by first considering the drug functional similarities at the pathway level. The performance of PriorCD in drug repurposing was evaluated by using drug datasets of breast cancer and ovarian cancer. Cross-validation tests on the drugs approved for the treatment of these cancers indicated that our approach can achieve area under receiver-operating characteristic curve (AUROC) values greater than 0.82. Furthermore, literature searches validated our results, and comparison with other classical gene-based repurposing methods indicated that our pathway-level PriorCD is comparatively more effective at prioritizing candidate drugs with similar therapeutic effects. We hope that our study will be of benefit to the field of drug discovery. In order to expand the usage of PriorCD, a freely available R-based package, PriorCD, has been developed to prioritize candidate anticancer drugs for drug repurposing.

SUBMITTER: Di J

PROVIDER: S-EPMC6763777 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Prioritization of candidate cancer drugs based on a drug functional similarity network constructed by integrating pathway activities and drug activities.

Di Jieyi J Zheng Baotong B Kong Qingfei Q Jiang Ying Y Liu Siyao S Yang Yang Y Han Xudong X Sheng Yuqi Y Zhang Yunpeng Y Cheng Liang L Han Junwei J

Molecular oncology 20190821 10

Due to the speed, efficiency, relative risk, and lower costs compared to traditional drug discovery, the prioritization of candidate drugs for repurposing against cancers of interest has attracted the attention of experts in recent years. Herein, we present a powerful computational approach, termed prioritization of candidate drugs (PriorCD), for the prioritization of candidate cancer drugs based on a global network propagation algorithm and a drug-drug functional similarity network constructed ...[more]

PMID: 31408580

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Project description:BackgroundCurrent methods for gene-set or pathway analysis are usually designed to test the enrichment of a single gene-set. Once the analysis is carried out for each of the sets under study, a list of significant sets can be obtained. However, if one wishes to further prioritize the importance or strength of association of these sets, no such quantitative measure is available. Using the magnitude of p-value to rank the pathways may not be appropriate because p-value is not a measure for strength of significance. In addition, when testing each pathway, these analyses are often implicitly affected by the number of differentially expressed genes included in the set and/or affected by the dependence among genes.ResultsHere we propose a two-stage procedure to prioritize the pathways/gene-sets. In the first stage we develop a pathway-level measure with three properties. First, it contains all genes (differentially expressed or not) in the same set, and summarizes the collective effect of all genes per sample. Second, this pathway score accounts for the correlation between genes by synchronizing their correlation directions. Third, the score includes a rank transformation to enhance the variation among samples as well as to avoid the influence of extreme heterogeneity among genes. In the second stage, all scores are included simultaneously in a Bayesian logistic regression model which can evaluate the strength of association for each set and rank the sets based on posterior probabilities. Simulations from Gaussian distributions and human microarray data, and a breast cancer study with RNA-Seq are considered for demonstration and comparison with other existing methods.ConclusionsThe proposed summary pathway score provides for each sample an overall evaluation of gene expression in a gene-set. It demonstrates the advantages of including all genes in the set and the synchronization of correlation direction. The simultaneous utilization of all pathway-level scores in a Bayesian model not only offers a probabilistic evaluation and ranking of the pathway association but also presents good accuracy in identifying the top-ranking pathways. The resulting recommendation list of ranked pathways can be a reference for potential target therapy or for future allocation of research resources.

Dataset Information

Prioritization of candidate cancer drugs based on a drug functional similarity network constructed by integrating pathway activities and drug activities.

Publications

Prioritization of candidate cancer drugs based on a drug functional similarity network constructed by integrating pathway activities and drug activities.

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