Abstract 3611: Identifying gene expression markers of anticancer drug response using large scale genomic and drug response databases established from patient derived tumors

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.

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An Improved Anticancer Drug-Response Prediction Based on an Ensemble Method Integrating Matrix Completion and Ridge Regression

Molecular Therapy — Nucleic Acids ◽

10.1016/j.omtn.2020.07.003 ◽

2020 ◽

Vol 21 ◽

pp. 676-686

Author(s):

Chuanying Liu ◽

Dong Wei ◽

Ju Xiang ◽

Fuquan Ren ◽

Li Huang ◽

...

Keyword(s):

Anticancer Drug ◽

Ridge Regression ◽

Drug Response ◽

Matrix Completion ◽

Response Prediction ◽

Ensemble Method ◽

Anticancer Drug Response

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Abstract 5240: Differential anticancer drug response by high-dimensional protein monitoring.

10.1158/1538-7445.am2013-5240 ◽

2013 ◽

Author(s):

Satoshi S. Nishizuka ◽

Kazushige Ishida ◽

Go Wakabayashi

Keyword(s):

Anticancer Drug ◽

Drug Response ◽

High Dimensional ◽

Anticancer Drug Response

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Discovering long noncoding RNA predictors of anticancer drug sensitivity beyond protein-coding genes

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1909998116 ◽

2019 ◽

Vol 116 (44) ◽

pp. 22020-22029 ◽

Cited By ~ 9

Author(s):

Aritro Nath ◽

Eunice Y. T. Lau ◽

Adam M. Lee ◽

Paul Geeleher ◽

William C. S. Cho ◽

...

Keyword(s):

Anticancer Drug ◽

Noncoding Rna ◽

Large Scale ◽

Drug Response ◽

Cancer Cell Line ◽

Systematic Evaluation ◽

Protein Coding ◽

Protein Coding Genes ◽

Clinical Biomarkers ◽

Response Predictors

Large-scale cancer cell line screens have identified thousands of protein-coding genes (PCGs) as biomarkers of anticancer drug response. However, systematic evaluation of long noncoding RNAs (lncRNAs) as pharmacogenomic biomarkers has so far proven challenging. Here, we study the contribution of lncRNAs as drug response predictors beyond spurious associations driven by correlations with proximal PCGs, tissue lineage, or established biomarkers. We show that, as a whole, the lncRNA transcriptome is equally potent as the PCG transcriptome at predicting response to hundreds of anticancer drugs. Analysis of individual lncRNAs transcripts associated with drug response reveals nearly half of the significant associations are in fact attributable to proximal cis-PCGs. However, adjusting for effects of cis-PCGs revealed significant lncRNAs that augment drug response predictions for most drugs, including those with well-established clinical biomarkers. In addition, we identify lncRNA-specific somatic alterations associated with drug response by adopting a statistical approach to determine lncRNAs carrying somatic mutations that undergo positive selection in cancer cells. Lastly, we experimentally demonstrate that 2 lncRNAs, EGFR-AS1 and MIR205HG, are functionally relevant predictors of anti-epidermal growth factor receptor (EGFR) drug response.

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