A Co-essentiality Network of Cancer Driver Genes Better Prioritizes Anticancer Drugs

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.