Synthetic Lethality-based Identification of Targets for Anticancer Drugs in the Human Signaling Network

Abstract

Chemotherapy agents can cause serious adverse effects by attacking both cancer tissues and normal tissues. Therefore, we proposed a synthetic lethality (SL) concept-based computational method to identify specific anticancer drug targets. First, a 3-step screening strategy (network-based, frequency-based and function-based screening) was proposed to identify the SL gene pairs by mining 697 cancer genes and the human signaling network, which had 6306 proteins and 62937 protein-protein interactions. The network-based screening was composed of a stability score constructed using a network information centrality measure (the average shortest path length) and the distance-based screening between the cancer gene and the non-cancer gene. Then, the non-cancer genes were extracted and annotated using drug-target interaction and drug description information to obtain potential anticancer drug targets. Finally, the human SL data in SynLethDB, the existing drug sensitivity data and text-mining were utilized for target validation. We successfully identified 2555 SL gene pairs and 57 potential anticancer drug targets. Among them, CDK1, CDK2, PLK1 and WEE1 were verified by all three aspects and could be preferentially used in specific targeted therapy in the future.

Figure 1

The illustration of the network. (a) The human signaling network. (b) The human cancer signaling network (HCSN). Blue nodes denote non-cancer genes; yellow nodes denote cancer genes; and edges represent protein-protein interactions. A larger node indicates a greater degree.

Figure 2

The cumulative percentage of frequency. The X-axis was the number of non-cancer genes. The Y-axis was the cumulative percentage of frequency. (122, 0.5) represented the cumulative frequency of the first highly frequent 122 genes account for 50% of the cumulative frequency of the total genes.

Figure 3

The significant enrichment pathways. Different colors denoted different pathway categories.

Figure 4

SL gene pairs. Light blue nodes denoted non-cancer genes; red nodes denoted cancer genes. Larger node indicates greater degree.

Figure 5

Illustration of our validations. (a) Anticancer drug targets validated by three aspects of the data. In the SynLethDB validation, drug sensitivity validation and text-mining validation, we validated 18, 13 and 12 anticancer drug targets, respectively. In addition, 4 targets could be validated using all three aspects. (b) The Venn diagram was drawn based on the overlap of the predicted SL gene pairs in four previous reports and our results. The methods with extremely low concordance of the results are not shown in the figure, which was drawn with the online tool http://bioinformatics.psb.ugent.be/webtools/Venn/.

Figure 6

The workflow of anticancer drug targets identification. The human cancer signaling network (HCSN) was constructed to obtain SL gene pairs using a 3-step screening strategy. The data of non-cancer genes and drug-target interactions data were obtained to identify the anticancer drug targets. Some validations were made to validate our results.