Identification of Therapeutic Gene Targets in Triple-Negative Breast Cancer: A Hybrid Approach Integrating Semidefinite Programming, Boolean Simulation, and Druggability Analysis

Triple-negative breast cancer (TNBC) represents a significant clinical challenge due to the absence of well-established molecular targets and resistance to conventional therapies. Identifying synergistic combinations of therapeutic targets requires computational approaches that integrate topological network analysis, experimental validation, and clinical applicability. This work proposes a hybrid methodology that combines Semidefinite Programming (SDP) for identification of critical nodes in gene regulatory networks, Boolean simulation for quantification of perturbation efficacy, and systematic literature validation with focus on druggability and synergy. We constructed a core regulatory network of 13 genes representing key nodes in STAT3, PI3K/AKT, and p53 signaling pathways, which are frequently deregulated in TNBC. We applied three complementary SDP formulations (Max-Cut, Influence Maximization, and Spectral Clustering) to identify candidate targets, followed by stochastic Boolean simulation for calculation of the Therapeutic Index (TI). We integrated a practical applicability scoring system that considers druggability, clinical evidence, specificity, and TNBC validation. Our analysis identified the combination STAT3 + BCL2 as the most promising therapeutic pair, with an applicability score of 0.905 and average druggability of 0.85. This finding is strongly supported by extensive experimental evidence demonstrating that STAT3 directly regulates BCL2 transcription in breast cancer cells. We demonstrate a clear methodological evolution from previous approaches: from 5 genes (Tilli et al., 2016, druggability 0.32) to 3 genes ([9], druggability 0.40) and finally to 2 genes (our work, druggability 0.85), representing a 166% increase in druggability and 60% reduction in complexity. The proposed methodology offers a systematic and reproducible framework for prioritization of therapeutic targets with focus on clinical applicability, contributing to rational development of combination therapies in TNBC.