Epithelial-to-mesenchymal transition (EMT) is discussed to be centrally involved in invasion, stemness, and drug resistance. Experimental models to evaluate this process in its biological complexity are limited. To shed light on EMT impact and test drug response more reliably, we use a lung tumor test system based on a decellularized intestinal matrix showing more in vivo-like proliferation levels and enhanced expression of clinical markers and carcinogenesis-related genes. In our models, we found evidence for a correlation of EMT with drug resistance in primary and secondary resistant cells harboring KRAS^G12C or EGFR mutations, which was simulated in silico based on an optimized signaling network topology. Notably, drug resistance did not correlate with EMT status in KRAS-mutated patient-derived xenograft (PDX) cell lines, and drug efficacy was not affected by EMT induction via TGF-β. To investigate further determinants of drug response, we tested several drugs in combination with a KRAS^G12C inhibitor in KRAS^G12C mutant HCC44 models, which, besides EMT, display mutations in P53, LKB1, KEAP1, and high c-MYC expression. We identified an aurora-kinase A (AURKA) inhibitor as the most promising candidate. In our network, AURKA is a centrally linked hub to EMT, proliferation, apoptosis, LKB1, and c-MYC. This exemplifies our systemic analysis approach for clinical translation of biomarker signatures.

Pancreatic ductal adenocarcinoma (PDAC) develops chemoresistance through dynamic cell-state transitions, yet the regulatory network architecture governing drug-tolerant states remains poorly characterised, because static expression snapshots cannot capture how interaction topology evolves along treatment-induced trajectories. We established a 3D PANC-1 tissue model on decellularised porcine intestinal matrix, which reaches patient-like proliferation indices and tissue-niche-specific drug responses, and performed single-cell RNA sequencing across four conditions: control, gemcitabine (GEM), TGF-β1, and their combination. Coupling pseudotime trajectory inference to dynamic protein–protein interaction network analysis, we tracked betweenness centrality continuously across cell-state transitions rather than comparing discrete states. GEM survivors engaged a proliferative programme (E2F1, CDK1, AURKA, TPX2, TOP2A, BIRC5), whereas TGF-β1 induced an independent EMT attractor (SPOCK1, MBOAT2, COL5A1, THBS1, FN1). Where the two converged, an emergent bottleneck appeared: CDK1 betweenness centrality spiked selectively, restrained by CDKN1A (p21) and WEE1, defining an S-phase-persistence state enriched for GEM survivors. Cross-examination in a PDAC atlas of 726,107 cells from 231 patients showed 8.7-fold metastatic enrichment of triple-positive cells. This framework, linking controlled 3D tissue experimentation, single-cell systems analysis and clinical-scale patient data, is applicable to drug resistance across cancer types.