Global view of a drug-sensitivity gene network.

Haixiu Yang,Yunpeng Zhang,Yanjun Xu,Desi Shang,Tan Wu,Jiasheng Wang,Siyao Liu

doi:10.18632/oncotarget.23229

Abstract

An important challenge in drug development is to gain insight into the mechanism of drug sensitivity. Looking for insights into the global relationships between drugs and their sensitivity genes would be expected to reveal mechanism of drug sensitivity. Here we constructed a drug-sensitivity gene network (DSGN) based on the relationships between drugs and their sensitivity genes, using drug screened genomic data from the NCI-60 cell line panel, including 181 drugs and 1057 sensitivity genes, and 1646 associations between them. Through network analysis, we found that two drugs that share the same sensitivity genes tend to share the same Anatomical Therapeutic Chemical classification and side effects. We then found that the sensitivity genes of same drugs tend to cluster together in the human interactome and participate in the same biological function modules (pathways). Finally, we noticed that the sensitivity genes and target genes of the same drug have a significant dense distance in the human interactome network and they were functionally related. For example, target genes such as epidermal growth factor receptor gene can activate downstream sensitivity genes of the same drug in the PI3K/Akt pathway. Thus, the DSGN would provide great insights into the mechanism of drug sensitivity.

Highlights

There is compelling evidence that predictions of anticancer drug response can be refined by identifying and applying molecular biomarkers [1, 2]
One kind of node corresponded to drugs tested in the National Cancer Institute (NCI) Developmental Therapeutics Program (DTP), and the other kind of node comprised the sensitivity genes from the profiles for the 60 cell lines of the NCI DTP drug screen [11]
The drug-sensitivity gene network (DSGN) was composed of 1646 drug-gene pairs, including 1057 genes and 181 drugs that were grouped into 12 drug classes using the Anatomical Therapeutic Chemical (ATC) classification system in the DSGN

Summary

Introduction

There is compelling evidence that predictions of anticancer drug response can be refined by identifying and applying molecular biomarkers [1, 2]. David et al developed a novel approach named Multivariate Organization of Combinatorial Alterations (MOCA), combining many genomic alterations into biomarkers of drug response, and found that multi-gene features have substantially higher correlation with drug response than do singlegene features [6]. It follows that methods considering the cumulative effect of many markers would make the prediction of complex phenotypes (such as drug response) more accurate [2, 7]

Methods

Results

Conclusion