NCES: A Cell-Specific Network-Augmented Essentiality Framework for Cancer Therapeutic Target Discovery

Area of Science:

• Oncology
• Computational Biology
• Genomics

Background:

• Identifying effective cancer therapeutic targets is a significant challenge in research.
• Individual gene essentiality from CRISPR-Cas9 screens often yields non-specific candidates.
• A need exists for improved methods to predict reliable therapeutic targets.

Purpose of the Study:

• To develop and validate a novel network-augmented framework, the neighbor-correlation essentiality score (NCES), for enhanced therapeutic target discovery.
• To improve the prediction accuracy of essential genes as potential cancer drug targets.

Main Methods:

• Developed NCES, integrating DepMap CERES scores (CRISPR-Cas9 essentiality) with protein-protein interaction networks.
• Assigned interaction weights based on cell-line-specific expression correlations from CRISPR knockout or compound-perturbation data.
• Evaluated NCES performance across 7 cancer cell lines against established therapeutic target databases.

Main Results:

• NCES variants consistently outperformed individual gene essentiality approaches.
• The CRISPR-weighted NCES variant achieved high performance (AUROCs of 0.794 and 0.779) against gold standards.
• Weighted NCES variants showed statistically significant improvements in predictive accuracy over unweighted versions.

Conclusions:

• NCES advances therapeutic target discovery by incorporating cell-specific, functionally relevant gene interactions.
• The framework effectively leverages genome-scale essentiality data and network information.
• Identified potential targets like CCNB1, CDC7, and WEE1 supported by existing literature.