Table of Contents
International Journal of Proteomics
Volume 2012, Article ID 510397, 11 pages
http://dx.doi.org/10.1155/2012/510397
Research Article

Seven-Signal Proteomic Signature for Detection of Operable Pancreatic Ductal Adenocarcinoma and Their Discrimination from Autoimmune Pancreatitis

1Institute for Advanced Research, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan
2Division of Molecular Carcinogenesis, Center for Neurological Diseases and Cancer, Nagoya University Graduate School of Medicine, Nagoya 466-8550, Japan
3Division of Epidemiology and Prevention, Aichi Cancer Center, Nagoya 464-8681, Japan
4Division of Research and Development, Oncomics Co., Ltd, Nagoya 464-0858, Japan
5Division of Surgical Oncology, Department of Surgery, Nagoya University Hospital, Nagoya 466-8550, Japan
6Department of Gastroenterology, Nagoya University Hospital, Nagoya 466-8550, Japan
7Department of Gastroenterology, Aichi Cancer Center, Nagoya 464-8681, Japan

Received 27 January 2012; Accepted 9 March 2012

Academic Editor: Visith Thongboonkerd

Copyright © 2012 Kiyoshi Yanagisawa et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

There is urgent need for biomarkers that provide early detection of pancreatic ductal adenocarcinoma (PDAC) as well as discrimination of autoimmune pancreatitis, as current clinical approaches are not suitably accurate for precise diagnosis. We used mass spectrometry to analyze protein profiles of more than 300 plasma specimens obtained from PDAC, noncancerous pancreatic diseases including autoimmune pancreatitis patients and healthy subjects. We obtained 1063 proteomic signals from 160 plasma samples in the training cohort. A proteomic signature consisting of 7 mass spectrometry signals was used for construction of a proteomic model for detection of PDAC patients. Using the test cohort, we confirmed that this proteomic model had discrimination power equal to that observed with the training cohort. The overall sensitivity and specificity for detection of cancer patients were 82.6% and 90.9%, respectively. Notably, 62.5% of the stage I and II cases were detected by our proteomic model. We also found that 100% of autoimmune pancreatitis patients were correctly assigned as noncancerous individuals. In the present paper, we developed a proteomic model that was shown able to detect early-stage PDAC patients. In addition, our model appeared capable of discriminating patients with autoimmune pancreatitis from those with PDAC.