A Comparative Quantitative LC-MS/MS Profiling Analysis of Human Pancreatic Adenocarcinoma Adjacent-Normal Tissue and Patient-Derived Tumour Xenografts

摘要

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers worldwide; it develops in a relatively symptom-free manner, leading to rapid disease progression and metastasis, leading to a 5-year survival rate of less than 5%. A lack of dependable diagnostic markers and rapid development of resistance to conventional therapies are among the problems associated with management of the disease. A better understanding of pancreatic tumour biology and discovery of new potential therapeutic targets are important goals in pancreatic cancer research. This study describes the comparative quantitative LC-MS/MS proteomic analysis of the membrane-enriched proteome of 10 human pancreatic ductal adenocarcinomas, 9 matched adjacent-normal pancreas and patient-derived xenografts (PDXs) in mice (10 at F1 generation and 10 F2). Quantitative label-free LC-MS/MS data analysis identified 129 proteins upregulated, and 109 downregulated, in PDAC, compared to adjacent-normal tissue. In this study, analysing peptide MS/MS data from the xenografts, great care was taken to distinguish species-specific peptides definitively derived from human sequences, or from mice, which could not be distinguished. The human-only peptides from the PDXs are of particular value, since only human tumour cells survive, and stromal cells are replaced during engraftment in the mouse; this list is, therefore, enriched in tumour-associated proteins, some of which might be potential therapeutic or diagnostic targets. Using human-specific sequences, 32 proteins were found to be upregulated, and 113 downregulated in PDX F1 tumours, compared to primary PDAC. Differential expression of CD55 between PDAC and normal pancreas, and expression across PDX generations, was confirmed by Western blotting. These data indicate the value of using PDX models in PDAC research. This study is the first comparative proteomic analysis of PDAC which employs PDX models to identify patient tumour cell-associated proteins, in an effort to find robust targets for therapeutic treatment of PDAC.