Drug repurposing against breast cancer by integrating drug-exposure expression profiles and drug–drug links based on graph neural network

Abstract

Motivation Breast cancer is one of the leading causes of cancer deaths among women worldwide. It is necessary to develop new breast cancer drugs because of the shortcomings of existing therapies. The traditional discovery process is time-consuming and expensive. Repositioning of clinically approved drugs has emerged as a novel approach for breast cancer therapy. However, serendipitous or experiential repurposing cannot be used as a routine method. Results In this study, we proposed a graph neural network model GraphRepur based on GraphSAGE for drug repurposing against breast cancer. GraphRepur integrated two major classes of computational methods, drug network-based and drug signature-based. The differentially expressed genes of disease, drug-exposure gene expression data, and the drug-drug links information were collected. By extracting the drug signatures and topological structure information contained in the drug relationships, GraphRepur can predict new drugs for breast cancer, outperforming previous state-of-the-art approaches and some classic machine learning methods. The high-ranked drugs have indeed been reported as new uses for breast cancer treatment recently. Availability The source code of our model and datasets are available at: https://github.com/cckamy/GraphRepur and https://figshare.com/articles/software/GraphRepur_Breast_Cancer_Drug_Repurposing/14220050 Supplementary information Supplementary data are available at Bioinformatics online.

Drug Repositioning with GraphSAGE and Clustering Constraints Based on Drug and Disease Networks

“…We also evaluated the sampling number of network neighbors. Similar to Cui et al (2021 ), we tested 4 cases, where is {3, 5, 10, 15} and finally determined that it is better to take the nearest 5 neighbor nodes as aggregation nodes. Figure 4 shows the distribution of AUC values for a total of 25 times in 5 cross-validations.…”

NMSDR: Drug repurposing approach based on transcriptome data and network module similarity

“…Drug signature and gene expression of breast cancer are obtained from LINCS and GEO, respectively. After developing an adaptation of the GraphSAGE network, 10 drugs were predicted as new candidates for breast cancer treatment [36]. One of them (sunitinib) is the same with our predicted drugs.…”

Artificial intelligence in cancer target identification and drug discovery

“…For example, Cui et al 249 proposed GraphRepur, a model for drug repurposing prediction based on graph neural networks. Firstly, the authors collected the drug-induced gene expression data from the LINCS project 250 as well as the drug-drug links information from the STITCH database 251 .…”