2026/03/05 by Aamer Iqbal Bhatti, Aamer Bhatti · 1 voice
Biochemistry, Genetics and Molecular Biology · #Bioinformatics and Genomic Networks #Gene Regulatory Network Analysis #Melanoma and MAPK Pathways
paper · pdf · doi:10.21203/rs.3.rs-8942485/v1
crossref issued 2026/03/05 · crossref published 2026/03/05 · openalex publication_date 2026/03/05 · crossref created 2026/03/05 · openalex created_date 2026/03/06 · crossref deposited 2026/06/15 · crossref indexed 2026/07/12 · openalex updated_date 2026/07/14
Abstract 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.