Metabolic difference between patient-derived xenograft model of pancreatic ductal adenocarcinoma and corresponding primary tumor

Abstract

Abstract Background Patients-derived xenograft (PDX) model have been widely used for tumor biological and pathological studies. However, the metabolic similarity of PDX tumor to the primary cancer (PC) is still unknown. Methods In present study, we established PDX model by engrafting primary tumor of pancreatic ductal adenocarcinoma (PDAC), and then compared the tumor metabolomics of PC, the first generation of PDX tumor (PDXG1), and the third generation of PDX tumor (PDXG3) by using 1 H NMR spectroscopy. Then, we assessed the differences in response to chemotherapy between PDXG1 and PDXG3 and corresponding metabolomic differences in drug-resistant tumor tissues. To evaluate the metabolomic similarity of PDX to PC, we also compared the metabolomic difference of cell-derived xenograft (CDX) vs. PC and PDX vs. PC. Results After engraftment, PDXG1 tumor had a low level of lactate, pyruvate, citrate and multiple amino acids (AAs) compared with PC. Metabolite sets enrichment and metabolic pathway analyses implied that glycolysis metabolisms were suppressed in PDXG1 tumor, and tricarboxylic acid cycle (TCA)-associated anaplerosis pathways, such as amino acids metabolisms, were enhanced. Then, after multiple passages of PDX, the altered glycolysis and TCA-associated anaplerosis pathways were partially recovered. Although no significant difference was observed in the response of PDXG1 and PDXG3 to chemotherapy, the difference in glycolysis and amino acids metabolism between PDXG1 and PDXG3 could still be maintained. In addition, the metabolomic difference between PC and CDX models were much larger than that of PDX model and PC, indicating that PDX model still retain more metabolic characteristics of primary tumor which is more suitable for tumor-associated metabolism research. Conclusions Compared with primary tumor, PDX models have obvious difference in metabolomic level. These findings can help us design in vivo tumor metabolomics research legitimately and analyze the underlying mechanism of tumor metabolic biology thoughtfully.