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Published on: July 22, 2020
A Co-essentiality Network of Cancer Driver Genes Better Prioritizes Anticancer Drugs
Kwanghwan Lee1, Donghyo Kim1, Inhae Kim2
1Department of Life Sciences, Pohang University of Science and Technology, Pohang 790-784, Republic of Korea.
Abstract:
Diverse molecular networks have been extensively studied to discover therapeutic targets and repurpose approved drugs. However, it is necessary to select a suitable network since the performance of network medicine relies heavily on the completeness and characteristics of the selected network. Although a network using gene essentiality in cancer cells could be an effective platform for identifying anticancer targets, efforts to apply these networks to therapeutic applications have been limited. We constructed a phenotype-level network using co-essentiality relationships among genes from CRISPR screens across 769 cancer cell lines to discover therapeutic targets for diverse cancer types. By leveraging cancer driver genes and network propagation, we found that the co-essentiality network better prioritized anticancer targets and biomarkers and predicted more precise drug responses in cancer cells than other molecular networks. The co-essentiality network outperformed conventional molecular networks in drug repurposing and was validated in silico by clinical trial records. Notably, the co-essentiality network identified 30 repurposed drugs that the other networks have not yet covered, and we showcased three approved drugs repurposed for lung adenocarcinoma (atovaquone, eflornithine, and teriflunomide). Our study provides a novel network for precision oncology to improve the identification of therapeutic targets in specific cancers.
Insights
A new co-essentiality gene network from cancer cells effectively identifies cancer drug targets and repurposes drugs. This network improves precision oncology by predicting drug responses and discovering novel therapeutic strategies.
Area of Science:
- Genomics
- Systems Biology
- Computational Biology
Background:
- Molecular networks are crucial for identifying therapeutic targets and drug repurposing.
- Network performance depends on network completeness and characteristics.
- Existing gene essentiality networks have limited therapeutic applications.
Purpose of the Study:
- To construct a phenotype-level co-essentiality network from CRISPR screens across 769 cancer cells.
- To discover therapeutic targets for diverse cancer types using this novel network.
- To evaluate the network's performance in prioritizing targets, predicting drug responses, and enabling drug repurposing.
Main Methods:
- Constructed a phenotype-level network based on gene co-essentiality from CRISPR screening data across 769 cancer cell lines.
- Applied network propagation and leveraged cancer driver genes.
- Validated drug repurposing predictions using in silico clinical trial records.
Main Results:
- The co-essentiality network outperformed other molecular networks in prioritizing anticancer targets and biomarkers.
- It predicted more precise drug responses in cancer cells compared to conventional networks.
- The network identified 30 novel repurposed drugs, including three for lung adenocarcinoma (atovaquone, eflornithine, teriflunomide).
Conclusions:
- The developed co-essentiality network is a valuable tool for precision oncology.
- It enhances the identification of cancer-specific therapeutic targets and facilitates drug repurposing.
- This approach offers a novel platform for improving cancer treatment strategies.
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