Identification of carcinogenesis and tumor progression processes in pancreatic ductal adenocarcinoma using high-throughput proteomics

Abstract

Pancreatic adenocarcinoma (PDAC) is an aggressive disease with an overall 5 year-survival rate of just 5%. A better understanding of both the carcinogenesis processes and the mechanisms of progression of PDAC disease is mandatory. Proteomics offers complementary information to genomics, measuring the direct effectors of the biological processes. From a cohort of 110 PDAC patients treated with surgery and adjuvant therapy, 52 patients, of which primary tumor and normal tissue, preneoplastic lesions (PanIN), and/or lymph node metastases were available, were selected for the study. Proteins were extracted from small punches obtained from formalin fixed, paraffin embedded tissue, and analyzed by LC-MS/MS using a data-independent acquisition approach. Protein expression data was analyzed using probabilistic graphical models, allowing functional characterization. Functional node activities were calculated as the mean of expression of those proteins related to the main function of each node. Comparisons between groups were done using linear mixed models. First, analyzing the tumor tissue, three proteomics subtypes were defined: T1 (32% of patients) presented higher activity of adhesion and complement activation nodes, T2 (34%) had higher mitochondrial and metabolic node activity, and T3 (34%) had higher nucleoplasm node activity. Second, relevant biological processes related to carcinogenesis and tumor progression were studied in each subtype. We found differences between T1 PanIN and primary tumors in adhesion, translation, mitochondria and pancreatic secretion nodes, suggesting an involvement of these processes in tumor development. Differences between tumors and lymph nodes, related to tumor progression, were identified in nucleoplasm, translation, adhesion, extracellular matrix, and complement activation nodes. Mitochondria and metabolism nodes had differential activity between T2 normal tissue, PanIN and tumors, and also between tumors and lymph nodes. T3 analyses point out that nucleoplasm, metabolism and mitochondria, and extracellular matrix nodes could be involved in T3 tumors carcinogenesis. Interestingly, identified processes were different between the three proteomics subtypes suggesting that the motor of the disease is characteristic of each subtype. Additionally, these proteomics subtypes were validated into the PDAC TCGA cohort. In this study, three PDAC proteomics subtypes were defined, an adhesion-related subtype (T1), a metabolic-related subtype (T2), and a nucleoplasm subtype (T3). We also suggest several biological processes involved in tumor development and progression exclusive of each proteomics subtype. These biological processes could be relevant as a guide to select candidates for future tailored therapeutics treatments in PDAC. Proteomics data are available via ProteomeXchange with identifier PXD032076.