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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

Topological characteristics of frequently predicted target classes: only LR (green triangles), common predicted (orange circles).The average degree of the known targets identified exclusively by LR is 94. For the common predicted targets, it is significantly larger (σ = 248). Similarly, the average radiality of targets identified by LR is relatively small versus the common predicted ones. These facts indicate that LR detects the targets, which represent hubs in local network modules rather than in the entire network.
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f4: Topological characteristics of frequently predicted target classes: only LR (green triangles), common predicted (orange circles).The average degree of the known targets identified exclusively by LR is 94. For the common predicted targets, it is significantly larger (σ = 248). Similarly, the average radiality of targets identified by LR is relatively small versus the common predicted ones. These facts indicate that LR detects the targets, which represent hubs in local network modules rather than in the entire network.

Mentions: Degree and radiality are two key network features that explain the behavior of drug targets in terms of network topology (Fig. 4). Common predicted targets (orange circle) have both a high degree and high radiality values versus the targets predicted by only LR (green triangle). This observation suggests that the commonly predicted targets are well-connected proteins in terms of neighbors and shortest paths. Hence, such topological features make them easily predictable. Conversely, LR can predict less obvious drug targets by integrating gene expression data and topological information. We speculate that such targets might lead to fewer side effects and provide more effective treatment results by having fewer neighbors in the network.


Drug target prioritization by perturbed gene expression and network information.

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

Topological characteristics of frequently predicted target classes: only LR (green triangles), common predicted (orange circles).The average degree of the known targets identified exclusively by LR is 94. For the common predicted targets, it is significantly larger (σ = 248). Similarly, the average radiality of targets identified by LR is relatively small versus the common predicted ones. These facts indicate that LR detects the targets, which represent hubs in local network modules rather than in the entire network.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f4: Topological characteristics of frequently predicted target classes: only LR (green triangles), common predicted (orange circles).The average degree of the known targets identified exclusively by LR is 94. For the common predicted targets, it is significantly larger (σ = 248). Similarly, the average radiality of targets identified by LR is relatively small versus the common predicted ones. These facts indicate that LR detects the targets, which represent hubs in local network modules rather than in the entire network.
Mentions: Degree and radiality are two key network features that explain the behavior of drug targets in terms of network topology (Fig. 4). Common predicted targets (orange circle) have both a high degree and high radiality values versus the targets predicted by only LR (green triangle). This observation suggests that the commonly predicted targets are well-connected proteins in terms of neighbors and shortest paths. Hence, such topological features make them easily predictable. Conversely, LR can predict less obvious drug targets by integrating gene expression data and topological information. We speculate that such targets might lead to fewer side effects and provide more effective treatment results by having fewer neighbors in the network.

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