ANovelSerumMetabolomics-BasedDiagnosticApproachto Pancreatic Cancer

Background: To improve the prognosis of patients with pancreatic cancer, more accurate serum diagnostic methods are required. We used serum metabolomics as a diagnostic method for pancreatic cancer. Methods: Sera from patients with pancreatic cancer, healthy volunteers, and chronic pancreatitis were collectedatmultipleinstitutions.Thepancreaticcancerandhealthyvolunteers wererandomlyallocatedtothe training or the validation set. All of the chronic pancreatitis cases were included in the validation set. In each study, the subjects’ serum metabolites were analyzed by gas chromatography mass spectrometry (GC/MS) andadataprocessingsystemusinganin-houselibrary.Thediagnostic modelconstructedviamultiplelogistic regression analysis in thetraining set study was evaluated on thebasis of its sensitivity and specificity, and the results were confirmed by the validation set study. Results: In the training set study, which included 43 patients with pancreatic cancer and 42 healthy volunteers, the model possessed high sensitivity (86.0%) and specificity (88.1%) for pancreatic cancer. The use of the model was confirmed in the validation set study, which included 42 pancreatic cancer, 41 healthy volunteers,and23chronicpancreatitis;thatis,itdisplayedhighsensitivity(71.4%)andspecificity(78.1%);and furthermore,itdisplayedhighersensitivity(77.8%)inresectablepancreaticcancerandlowerfalse-positiverate (17.4%) in chronic pancreatitis than conventional markers. Conclusions: Our model possessed higher accuracy than conventional tumor markers at detecting the resectable patients with pancreatic cancer in cohort including patients with chronic pancreatitis. Impact:Itisapromising methodfor improvingtheprognosis ofpancreatic cancerviaitsearlydetection and accurate discrimination from chronic pancreatitis. Cancer Epidemiol Biomarkers Prev; 22(4); 1–9. � 2013 AACR.