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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

The distributions of drug targets.(A) Gene expression distribution of the 42331 known drug targets in the CMap. The significant targets reside on the right and left side of dashed lines. 97% of drug targets do not show significant expression changes due to drug perturbations. (B) The distribution of the average shortest path distances of deregulated genes to known (blue distribution) and to random (red distribution) targets. Two distributions are statistically different (Mann–Whitney, p-value < 2.2e−16). Deregulated genes are closer to known targets than any other proteins in the network. Thus, this motivates a network based target prediction.
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f2: The distributions of drug targets.(A) Gene expression distribution of the 42331 known drug targets in the CMap. The significant targets reside on the right and left side of dashed lines. 97% of drug targets do not show significant expression changes due to drug perturbations. (B) The distribution of the average shortest path distances of deregulated genes to known (blue distribution) and to random (red distribution) targets. Two distributions are statistically different (Mann–Whitney, p-value < 2.2e−16). Deregulated genes are closer to known targets than any other proteins in the network. Thus, this motivates a network based target prediction.

Mentions: Gene expression data represents mRNA activity of genes under a specific condition (i.e., control vs. drug treatment). In order to understand the capability of simple gene expression data in target prediction, the gene expression values (fold change—FC) of 42,331 known targets for the CMap drugs are analyzed (Fig. 2a). When significant targets are filtered (/FC/ ≥ 1.5, p-value ≤ 0.05), 97% of all targets do not show any expression changes due to drug perturbations. A previous study also indicated the limited regulation of drug targets at the mRNA level2. Hence, gene expression data alone can predict only 3% of known targets.


Drug target prioritization by perturbed gene expression and network information.

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

The distributions of drug targets.(A) Gene expression distribution of the 42331 known drug targets in the CMap. The significant targets reside on the right and left side of dashed lines. 97% of drug targets do not show significant expression changes due to drug perturbations. (B) The distribution of the average shortest path distances of deregulated genes to known (blue distribution) and to random (red distribution) targets. Two distributions are statistically different (Mann–Whitney, p-value < 2.2e−16). Deregulated genes are closer to known targets than any other proteins in the network. Thus, this motivates a network based target prediction.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f2: The distributions of drug targets.(A) Gene expression distribution of the 42331 known drug targets in the CMap. The significant targets reside on the right and left side of dashed lines. 97% of drug targets do not show significant expression changes due to drug perturbations. (B) The distribution of the average shortest path distances of deregulated genes to known (blue distribution) and to random (red distribution) targets. Two distributions are statistically different (Mann–Whitney, p-value < 2.2e−16). Deregulated genes are closer to known targets than any other proteins in the network. Thus, this motivates a network based target prediction.
Mentions: Gene expression data represents mRNA activity of genes under a specific condition (i.e., control vs. drug treatment). In order to understand the capability of simple gene expression data in target prediction, the gene expression values (fold change—FC) of 42,331 known targets for the CMap drugs are analyzed (Fig. 2a). When significant targets are filtered (/FC/ ≥ 1.5, p-value ≤ 0.05), 97% of all targets do not show any expression changes due to drug perturbations. A previous study also indicated the limited regulation of drug targets at the mRNA level2. Hence, gene expression data alone can predict only 3% of known targets.

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