Time-Resolved Network Entropy Analysis Identifies a CTNND1-VAV3 Subnetwork in Gefitinib-Resistant NSCLC

Acquired resistance to epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors remains the principal barrier to durable responses in non-small cell lung cancer (NSCLC), yet how the molecular interaction networks underlying this resistance reorganize over time is poorly understood. We reasoned that a systems-level signature of resistance should be visible in the way protein interaction networks remodel under sustained drug exposure, and therefore applied time-resolved network analysis to isogenic gefitinib-sensitive PC9 and gefitinib-resistant PC9R cells across 24 h of gefitinib treatment, computing network entropy and centrality measures for temporal protein interaction networks and interrogating co-expression-augmented networks for candidate resistance-associated bottleneck proteins. Network entropy rose in both phenotypes, indicating that entropic remodeling is a general response to EGFR pathway perturbation rather than a signature of resistance. In contrast, eigenvector entropy was higher in resistant cells at the earliest post-treatment time point, and resistant cells preserved giant-component connectivity and small-world topology early before fragmenting later. Temporal centrality analysis nominated BIRC3 as a resistant-cell-associated high-centrality node at 24 h, and co-expression analysis identified delta-catenin (CTNND1) as a high-betweenness bottleneck in the PC9R network, topologically bridging EGFR, VAV3, HIF3A, and NOTCH2. These findings nominate early post-treatment eigenvector entropy and a CTNND1-centered, EGFR-enriched subnetwork as candidate resistance-associated features that require validation in independent datasets and functional models.