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A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
Published on: May 27, 2021
Synthetic Lethality-based Identification of Targets for Anticancer Drugs in the Human Signaling Network
Lei Liu1, Xiujie Chen2, Chunyu Hu1
1College of Bioinformatics Science and Technology, Harbin Medical University, 194 Xuefu Road, Harbin, 150081, China.
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.
Insights
This study introduces a computational method using synthetic lethality (SL) to find new anticancer drug targets. The approach identified 57 potential targets, including CDK1, CDK2, PLK1, and WEE1, for more precise cancer therapies.
Area of Science:
- Computational biology
- Genomics
- Pharmacology
Background:
- Chemotherapy causes severe side effects by affecting both cancerous and normal tissues.
- Targeted therapies require precise identification of cancer-specific vulnerabilities.
Purpose of the Study:
- To develop a computational method for identifying anticancer drug targets based on the synthetic lethality (SL) concept.
- To discover novel SL gene pairs and potential drug targets for targeted cancer therapy.
Main Methods:
- A 3-step screening strategy (network-based, frequency-based, function-based) was employed to identify SL gene pairs from cancer genes and the human signaling network.
- Network information centrality and distance-based screening were used.
- Drug-target interaction, drug descriptions, and text-mining were utilized for target extraction and validation.
Main Results:
- Identified 2555 synthetic lethality (SL) gene pairs.
- Discovered 57 potential anticancer drug targets.
- CDK1, CDK2, PLK1, and WEE1 were validated as promising targets through multiple methods.
Conclusions:
- The proposed computational method effectively identifies synthetic lethality (SL) gene pairs and potential anticancer drug targets.
- Validated targets like CDK1, CDK2, PLK1, and WEE1 show promise for future targeted cancer therapies.
- This approach offers a strategy to minimize chemotherapy's adverse effects on normal tissues.
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