Background: Intraductal papillary mucinous neoplasm (IPMNs) of the pancreas may evolve from low- to high-grade dysplasia to invasive cancer. Accurate discrimination of IPMN-associated grade of dysplasia is an unmet clinical need for appropriate patient management and treatment. This study used an integrated metabolomics and lipidomics approach, aiming to define the metabolic profiles of IPMNs.
Methods: Metabolomic and lipidomic profiles of peri-operative pancreatic cyst fluid and pre-operative fasted plasma from IPMN and serous cystic neoplasm (SCN) patients were determined single-blinded in this pancreas resection cohort (n=31). Targeted (semi)quantitative analysis of 100 metabolites from 24 classes and >1000 lipid species spanning 13 classes were performed. The datasets were correlated against histological diagnosis and clinical parameters after correction for confounding factors. Group classification and model performance was done with Partial Least Squares Discriminant Analysis (PLS-DA) and a Leave-One-Out cross validation (LOO-CV) strategy, respectively. Variable Importance in Projection (VIP) ranking scores were used to select the best explanatory molecules.
Findings: Over 1000 different compounds were identified in plasma and cyst fluid. The IPMN biofluid profiles showed significant lipid pathway alterations compared to SCN controls. Integrated metabolomics and lipidomics data modeling allowed accurate discrimination between IPMN and SCN and could determine the IPMN-associated grade of dysplasia. Correlations were found between plasma lipid compounds of free fatty acids, ceramides, and triacylglycerol classes and the circulating levels of CA19-9, albumin and bilirubin (r > 0.6, p < 0.05).
Interpretation: An integrated metabolomic and lipidomic analysis of plasma or cyst fluid can improve discrimination of IPMN from SCN and within PMNs predict the grade of dysplasia, which is highly relevant for pancreatic surgery management.