From Pathway to Patient: Molecular Dysregulation as Basis for Minimal Sequential Intervention Strategy in Basal-Like Breast Cancer

Aggressive breast cancer subtypes like triple-negative and basal-like tumors exhibit widespread dysregulation across multiple signaling pathways, requiring multi-pathway therapeutic strategies. Boolean network models can integrate large-scale genomic data with mechanistic pathway knowledge, but face scalability challenges. We present BBCN118, a modular Boolean network framework comprising 118 genes decomposed across fifteen pathways including apoptosis, cell cycle, MAPK, PI3K AKTmTOR, JAK–STAT, hormone signaling, Wnt, and NFKB. Using TCGA mRNA profiles from basal-like breast cancer patients, we initialize patient-specific pathway states. Our central theoretical contribution is a Lyapunov-based decomposition theorem demonstrating that global network dynamics can be approximated by independent pathway-level analysis under mild coupling conditions, enabling scalable intervention design. The BBCN118 pipeline identifies minimal node perturbations driving each pathway toward biologically curated target states. Across deceased basal-like breast cancer patients, sequential pathway interventions achieved less than 20 percent mismatch reduction toward healthy attractors in most cases. Kernel frequency analysis revealed key intervention hubs (CDKN1A, CDKN2A, JAK2) consistent with known regulatory roles. BBCN118 provides a transparent, computationally efficient framework integrating patient omics with mechanistic modeling, advancing interpretable multi-pathway analysis for precision oncology.