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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

Results for different drug target data sets.(A) Comparison of functional (FT, FT1) and physical (PT) targets for selected measures. The predictions are given only for the 1st percentile of the ranking list. Note that due to very close recall values, three random predictor curves are over plotted. The highest recall (50%) was obtained on the FT1 (limited functional targets). Half of the measures correctly predicted 15% to 22% of the FT (all functional targets). The recall values are between 5% and 9% for PT (physical targets). Although the performance of the measures is highly dependent on the target definition, LR achieved the highest recall values for all target definitions. (B) The prediction power (expressed in decibel, dB) of each measure compared to the random predictor. It shows the magnitude of recall for each predictor normalized with respect to the random predictor.
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f5: Results for different drug target data sets.(A) Comparison of functional (FT, FT1) and physical (PT) targets for selected measures. The predictions are given only for the 1st percentile of the ranking list. Note that due to very close recall values, three random predictor curves are over plotted. The highest recall (50%) was obtained on the FT1 (limited functional targets). Half of the measures correctly predicted 15% to 22% of the FT (all functional targets). The recall values are between 5% and 9% for PT (physical targets). Although the performance of the measures is highly dependent on the target definition, LR achieved the highest recall values for all target definitions. (B) The prediction power (expressed in decibel, dB) of each measure compared to the random predictor. It shows the magnitude of recall for each predictor normalized with respect to the random predictor.

Mentions: Figure 5 shows the performance of the 3 best measures on different target sets (see Supplementary Fig. 2 for all measures). The performance of these measures is highly dependent on the target set. Half of the measures correctly predict 15% to 22% of the FT set (Fig. 5a). In the PT set, the performance of the majority of the measures is between 5% and 9%. LR achieved the best prediction with 0.09 recall (average p-value < 1.2e−2, Supplementary Table 5) for the 1st percentile; overall AUC value was 0.76 (Supplementary Fig. 3c). The PT set represents physical drug target interactions, and their performance is much worse than the performance based on the FT set. One reason might be that the STRING network is more adequate for the identification of functional targets, which were obtained by text mining methods. The highest prediction rate is achieved on the FT1 set—the best ones predict 50% of known targets. LR and radiality achieved 0.497 and 0.493 recall (Supplementary Table 4) and overall AUC values of 0.929 and 0.924 (Supplementary Fig. 3b), respectively. Such a significant improvement is reasonable because text mining methods select well-studied genes with many literature references as targets that generally have many connections versus other candidate targets in the STRING network. 82% of all targets in FT1 have a degree higher than 50 (Supplementary Fig. 4). This proves the high connectivity of the targets, which are true-positives in many cases.


Drug target prioritization by perturbed gene expression and network information.

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

Results for different drug target data sets.(A) Comparison of functional (FT, FT1) and physical (PT) targets for selected measures. The predictions are given only for the 1st percentile of the ranking list. Note that due to very close recall values, three random predictor curves are over plotted. The highest recall (50%) was obtained on the FT1 (limited functional targets). Half of the measures correctly predicted 15% to 22% of the FT (all functional targets). The recall values are between 5% and 9% for PT (physical targets). Although the performance of the measures is highly dependent on the target definition, LR achieved the highest recall values for all target definitions. (B) The prediction power (expressed in decibel, dB) of each measure compared to the random predictor. It shows the magnitude of recall for each predictor normalized with respect to the random predictor.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f5: Results for different drug target data sets.(A) Comparison of functional (FT, FT1) and physical (PT) targets for selected measures. The predictions are given only for the 1st percentile of the ranking list. Note that due to very close recall values, three random predictor curves are over plotted. The highest recall (50%) was obtained on the FT1 (limited functional targets). Half of the measures correctly predicted 15% to 22% of the FT (all functional targets). The recall values are between 5% and 9% for PT (physical targets). Although the performance of the measures is highly dependent on the target definition, LR achieved the highest recall values for all target definitions. (B) The prediction power (expressed in decibel, dB) of each measure compared to the random predictor. It shows the magnitude of recall for each predictor normalized with respect to the random predictor.
Mentions: Figure 5 shows the performance of the 3 best measures on different target sets (see Supplementary Fig. 2 for all measures). The performance of these measures is highly dependent on the target set. Half of the measures correctly predict 15% to 22% of the FT set (Fig. 5a). In the PT set, the performance of the majority of the measures is between 5% and 9%. LR achieved the best prediction with 0.09 recall (average p-value < 1.2e−2, Supplementary Table 5) for the 1st percentile; overall AUC value was 0.76 (Supplementary Fig. 3c). The PT set represents physical drug target interactions, and their performance is much worse than the performance based on the FT set. One reason might be that the STRING network is more adequate for the identification of functional targets, which were obtained by text mining methods. The highest prediction rate is achieved on the FT1 set—the best ones predict 50% of known targets. LR and radiality achieved 0.497 and 0.493 recall (Supplementary Table 4) and overall AUC values of 0.929 and 0.924 (Supplementary Fig. 3b), respectively. Such a significant improvement is reasonable because text mining methods select well-studied genes with many literature references as targets that generally have many connections versus other candidate targets in the STRING network. 82% of all targets in FT1 have a degree higher than 50 (Supplementary Fig. 4). This proves the high connectivity of the targets, which are true-positives in many cases.

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