Identifying effective therapeutic targets remains a central challenge in cancer research. CRISPR-Cas9 knockout screens have provided valuable insights into gene essentiality; however, using essentiality at the level of individual genes often fails to reliably distinguish true therapeutic targets from nonfunctional candidates. To address this limitation, we developed the neighbor-correlation essentiality score (NCES), a network-augmented framework that leverages the essentialities of functionally active neighboring genes. NCES combines DepMap CERES scores, which estimate gene essentiality from CRISPR-Cas9 knockout screens, with protein-protein interaction networks. Interaction weights are assigned to network neighbors based on cell-line-specific expression correlations derived from CRISPR knockout or compound-perturbation profiles. The proposed NCES framework was systematically evaluated across 7 cancer cell lines against therapeutic target gold standards. NCES variants consistently outperformed approaches based solely on individual gene essentiality, with the CRISPR-weighted variant achieving the best performance, yielding AUROCs of 0.794 and 0.779 against the Therapeutic Target Database and DrugBank gold standards, respectively. Statistical testing demonstrated that weighted NCES variants significantly improved predictive accuracy over their unweighted counterpart. Finally, several high-ranking genes beyond current gold-standard datasets, including CCNB1, CDC7, and WEE1, were supported by the existing literature as biologically essential or therapeutically actionable. Together, these results demonstrate that NCES advances therapeutic target discovery by leveraging cell-specific, functionally relevant interactions among network neighbors within genome-scale essentiality data.
