Abstract
Genomic profiles of cancer patients such as gene expression have become a major source to predict responses to drugs in the era of personalized medicine. As large-scale drug screening data with cancer cell lines are available, a number of computational methods have been developed for drug response prediction. However, few methods incorporate both gene expression data and the biological network, which can harbor essential information about the underlying process of the drug response. We proposed an analysis framework called DrugGCN for prediction of Drug response using a Graph Convolutional Network (GCN). DrugGCN first generates a gene graph by combining a Protein-Protein Interaction (PPI) network and gene expression data with feature selection of drug-related genes, and the GCN model detects the local features such as subnetworks of genes that contribute to the drug response by localized filtering. We demonstrated the effectiveness of DrugGCN using biological data showing its high prediction accuracy among the competing methods.
Highlights
Papadopoulos, GustavoCancer is a disease driven by the accumulation of somatic mutations
We assumed that high prediction accuracy of a drug response was derived by the information from the highly connected genes forming a subnetwork in the Protein-Protein Interaction (PPI) network
DrugGCN incorporated PPI network and gene expression data into the Graph Convolutional Network (GCN) model to detect the local features in graphs by localized filtering
Summary
Papadopoulos, GustavoCancer is a disease driven by the accumulation of somatic mutations. Mutations on specific genes that are called cancer driver genes can affect the transcription of the genes and cause the differential expression of the genes. In the early stages of the analysis on differential gene expression in cancer, several research works focused on comparative studies between normal and cancer cells [1,3]. Since the era of precision medicine or personalized medicine, the analysis of the differential gene expression among individual patients has become popular, as researchers have observed heterogeneity for immune responses induced by the same cancer therapy due to the diverse genetic background of individuals [4,5]. A recent study suggested that only around 5% of patients benefit from precision oncology [6], which highlights the importance of improving the prediction accuracy of drug response
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