Influence networks based on coexpression improve drug target discovery for the development of novel cancer therapeutics

Abstract

BackgroundThe demand for novel molecularly targeted drugs will continue to rise as we move forward toward the goal of personalizing cancer treatment to the molecular signature of individual tumors. However, the identification of targets and combinations of targets that can be safely and effectively modulated is one of the greatest challenges facing the drug discovery process. A promising approach is to use biological networks to prioritize targets based on their relative positions to one another, a property that affects their ability to maintain network integrity and propagate information-flow. Here, we introduce influence networks and demonstrate how they can be used to generate influence scores as a network-based metric to rank genes as potential drug targets.ResultsWe use this approach to prioritize genes as drug target candidates in a set of ER + breast tumor samples collected during the course of neoadjuvant treatment with the aromatase inhibitor letrozole. We show that influential genes, those with high influence scores, tend to be essential and include a higher proportion of essential genes than those prioritized based on their position (i.e. hubs or bottlenecks) within the same network. Additionally, we show that influential genes represent novel biologically relevant drug targets for the treatment of ER + breast cancers. Moreover, we demonstrate that gene influence differs between untreated tumors and residual tumors that have adapted to drug treatment. In this way, influence scores capture the context-dependent functions of genes and present the opportunity to design combination treatment strategies that take advantage of the tumor adaptation process.ConclusionsInfluence networks efficiently find essential genes as promising drug targets and combinations of targets to inform the development of molecularly targeted drugs and their use.

In-silico gene co-expression network analysis in Paracoccidioides brasiliensis with reference to haloacid dehalogenase superfamily hydrolase gene

“…[ 29 ] Similarly, the CE protein network has also been used in an integrated manner with metabolic pathways for identification of targets in the cancer therapeutics research. [ 30 31 ]…”

A Survey on the Computational Approaches to Identify Drug Targets in the Postgenomic Era

“…Beyond the above compound-centered large datasets, the accumulation of huge amount of gene expression profiles deposited in the Gene Expression Omnibus (GEO) also significantly facilitates the identification of drug targets. For example, utilizing the transcriptome profiles treated with letrozolein, the ER + breast tumors, Penrod and Moore [ 22 ] proposed an influence network approach that can not only identify promising targets but also suggest potential target combinations. The publicly available huge amount of transcriptome data is making it an attractive field to predict drug targets and reposition known drugs based on the gene expression profiles.…”

Social networks help to infer causality in the tumor microenvironment

“…The construction of a directed network of influence based on an initially undirected graph is very attractive for biologists because in biomedical research there is an abundance of high-throughput experimental data that allows the construction of undirected correlation networks connecting different types of biological entities, such as genes or proteins, but the predictive power of these networks is limited due to their lack of causality or directionality. Based on the definition of influence proposed by Hangal et al [ 3 ], Penrod et al [ 4 ] developed a method for drug target discovery in the context of cancer therapy and showed that influential genes tend to be essential for the proliferation and survival of breast cancer cells, and that gene influence differs between untreated tumors and residual tumors that have adapted to a drug treatment. In order to calculate the investments between two genes, Penrod et al [ 4 ] took the values of their partial correlation derived from expression data.…”

“…Based on the definition of influence proposed by Hangal et al [ 3 ], Penrod et al [ 4 ] developed a method for drug target discovery in the context of cancer therapy and showed that influential genes tend to be essential for the proliferation and survival of breast cancer cells, and that gene influence differs between untreated tumors and residual tumors that have adapted to a drug treatment. In order to calculate the investments between two genes, Penrod et al [ 4 ] took the values of their partial correlation derived from expression data. It is worth noting here that despite Penrod et al [ 4 ] having shown the utility of deriving the influence network from the co-expression information to identify genes essential for proliferation and survival of breast cancer cells, the underlying regulatory mechanisms involving these essential genes were not elucidated.…”

“…In order to calculate the investments between two genes, Penrod et al [ 4 ] took the values of their partial correlation derived from expression data. It is worth noting here that despite Penrod et al [ 4 ] having shown the utility of deriving the influence network from the co-expression information to identify genes essential for proliferation and survival of breast cancer cells, the underlying regulatory mechanisms involving these essential genes were not elucidated.…”