Abstract
BACKGROUND: Patient-derived xenograft (PDX) models are crucial for tumor biology and therapeutic response evaluations. The metabolic, transcriptomic, and proteomic changes in PDX models derived from pancreatic ductal adenocarcinoma (PDAC) during serial passaging remain poorly understood. METHODS: We established 33 PDX models from 43 PDAC patients and collected 40 benign pancreatic tissues as controls for metabolomic analysis using (1)H NMR spectroscopy. Multi-generational PDX models (P1-P3) from six patients were analyzed for molecular characteristics. Transcriptomic and proteomic analyses were performed using RNA-seq and 4D-DIA mass spectrometry, identifying differentially expressed genes (DEGs) and proteins, which were then subjected to enrichment analysis to uncover key biological functions. A gene-protein interaction network was constructed to identify functional modules across passages. RESULTS: PDX tumors closely mirrored primary tumors in metabolic characteristics, maintaining key metabolic features of PDAC with minor variations in amino acid metabolism, particularly glutamine and aspartate levels. PDX-2 and PDX-3 showed dysregulation in aminoacyl-tRNA biosynthesis and glycine, serine, and threonine metabolism. DEGs progressively increased across passages, with later-generation PDX models exhibiting transcriptional dysregulation, including enhanced MAPK signaling activity and reduced immune-related pathway expression. Proteomic analysis identified five consistently altered proteins (ZDHHC20, ZNF644, GNL2, PDGFRB, INO80E), indicative of enhanced signal transduction, cell cycle regulation, and DNA repair. Gene-protein interaction analysis identified four core functional modules involved in protein degradation, DNA repair, ribosome biogenesis, and inflammatory response. CONCLUSION: PDX models undergo adaptive changes during serial passaging while retain primary tumor characteristics, making them a valuable tool for understanding tumor evolution and informing therapeutic strategies, provided that passage-related drift is carefully considered.