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Drug target prioritization by perturbed gene expression and network information.

Isik Z, Baldow C, Cannistraci CV, Schroeder M - Sci Rep (2015)

Bottom Line: We performed the first systematic analysis of over 500 drugs from the Connectivity Map.We introduce a novel measure, local radiality, which combines perturbed genes and functional interaction network information.The new measure outperforms other methods in target prioritization and proposes cancer-specific pathways from drugs to affected genes for the first time.

View Article: PubMed Central - PubMed

Affiliation: Bioinformatics Group, Biotechnology Center (BIOTEC), Technische Universitat Dresden, Tatzberg 47-49, 01307 Dresden, Germany.

ABSTRACT
Drugs bind to their target proteins, which interact with downstream effectors and ultimately perturb the transcriptome of a cancer cell. These perturbations reveal information about their source, i.e., drugs' targets. Here, we investigate whether these perturbations and protein interaction networks can uncover drug targets and key pathways. We performed the first systematic analysis of over 500 drugs from the Connectivity Map. First, we show that the gene expression of drug targets is usually not significantly affected by the drug perturbation. Hence, expression changes after drug treatment on their own are not sufficient to identify drug targets. However, ranking of candidate drug targets by network topological measures prioritizes the targets. We introduce a novel measure, local radiality, which combines perturbed genes and functional interaction network information. The new measure outperforms other methods in target prioritization and proposes cancer-specific pathways from drugs to affected genes for the first time. Local radiality identifies more diverse targets with fewer neighbors and possibly less side effects.

No MeSH data available.


Related in: MedlinePlus

Overview of the target prioritization method.The perturbation of a drug on a specific tissue is measured by microarray experiments. Deregulated genes are obtained by comparison of drug-treated and control samples. A network measure computes a proximity score for each protein in the biological network based on its expression value, location to the deregulated genes or topological features. The proximity scores rank the possible drug targets, i.e., proteins with higher chance of being a target ranks on top of the sorted list. The target prioritization is evaluated by checking the rank of known drug targets (obtained from STITCH) in the sorted list of all proteins. The proteins listed in the high rank levels might be new potential targets.
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f1: Overview of the target prioritization method.The perturbation of a drug on a specific tissue is measured by microarray experiments. Deregulated genes are obtained by comparison of drug-treated and control samples. A network measure computes a proximity score for each protein in the biological network based on its expression value, location to the deregulated genes or topological features. The proximity scores rank the possible drug targets, i.e., proteins with higher chance of being a target ranks on top of the sorted list. The target prioritization is evaluated by checking the rank of known drug targets (obtained from STITCH) in the sorted list of all proteins. The proteins listed in the high rank levels might be new potential targets.

Mentions: Different network centrality measures and known target data are analyzed to observe their potential for drug target prioritization. Drug perturbation data is included in the calculation of topological proximity by using either deregulated genes or expression values themselves (Fig. 1). A centrality measure computes a closeness score for each protein by employing network topological features and expression values of deregulated genes. If a protein is not present in a PPI network, it cannot be predicted as a candidate target of a drug. The candidate targets are prioritized based on the closeness scores, i.e., proteins with a higher chance of being a target ranks on the top of the sorted list. Correlated with the initial hypothesis of proximity, a known drug target is expected to be at the top of the ranked list. To eliminate as many false positive target predictions as possible, only the proteins predicted in the 1st percentile of the ranked list are suggested as potential drug targets. The proposed method was evaluated on the public CMap expression profiles.


Drug target prioritization by perturbed gene expression and network information.

Isik Z, Baldow C, Cannistraci CV, Schroeder M - Sci Rep (2015)

Overview of the target prioritization method.The perturbation of a drug on a specific tissue is measured by microarray experiments. Deregulated genes are obtained by comparison of drug-treated and control samples. A network measure computes a proximity score for each protein in the biological network based on its expression value, location to the deregulated genes or topological features. The proximity scores rank the possible drug targets, i.e., proteins with higher chance of being a target ranks on top of the sorted list. The target prioritization is evaluated by checking the rank of known drug targets (obtained from STITCH) in the sorted list of all proteins. The proteins listed in the high rank levels might be new potential targets.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4663505&req=5

f1: Overview of the target prioritization method.The perturbation of a drug on a specific tissue is measured by microarray experiments. Deregulated genes are obtained by comparison of drug-treated and control samples. A network measure computes a proximity score for each protein in the biological network based on its expression value, location to the deregulated genes or topological features. The proximity scores rank the possible drug targets, i.e., proteins with higher chance of being a target ranks on top of the sorted list. The target prioritization is evaluated by checking the rank of known drug targets (obtained from STITCH) in the sorted list of all proteins. The proteins listed in the high rank levels might be new potential targets.
Mentions: Different network centrality measures and known target data are analyzed to observe their potential for drug target prioritization. Drug perturbation data is included in the calculation of topological proximity by using either deregulated genes or expression values themselves (Fig. 1). A centrality measure computes a closeness score for each protein by employing network topological features and expression values of deregulated genes. If a protein is not present in a PPI network, it cannot be predicted as a candidate target of a drug. The candidate targets are prioritized based on the closeness scores, i.e., proteins with a higher chance of being a target ranks on the top of the sorted list. Correlated with the initial hypothesis of proximity, a known drug target is expected to be at the top of the ranked list. To eliminate as many false positive target predictions as possible, only the proteins predicted in the 1st percentile of the ranked list are suggested as potential drug targets. The proposed method was evaluated on the public CMap expression profiles.

Bottom Line: We performed the first systematic analysis of over 500 drugs from the Connectivity Map.We introduce a novel measure, local radiality, which combines perturbed genes and functional interaction network information.The new measure outperforms other methods in target prioritization and proposes cancer-specific pathways from drugs to affected genes for the first time.

View Article: PubMed Central - PubMed

Affiliation: Bioinformatics Group, Biotechnology Center (BIOTEC), Technische Universitat Dresden, Tatzberg 47-49, 01307 Dresden, Germany.

ABSTRACT
Drugs bind to their target proteins, which interact with downstream effectors and ultimately perturb the transcriptome of a cancer cell. These perturbations reveal information about their source, i.e., drugs' targets. Here, we investigate whether these perturbations and protein interaction networks can uncover drug targets and key pathways. We performed the first systematic analysis of over 500 drugs from the Connectivity Map. First, we show that the gene expression of drug targets is usually not significantly affected by the drug perturbation. Hence, expression changes after drug treatment on their own are not sufficient to identify drug targets. However, ranking of candidate drug targets by network topological measures prioritizes the targets. We introduce a novel measure, local radiality, which combines perturbed genes and functional interaction network information. The new measure outperforms other methods in target prioritization and proposes cancer-specific pathways from drugs to affected genes for the first time. Local radiality identifies more diverse targets with fewer neighbors and possibly less side effects.

No MeSH data available.


Related in: MedlinePlus