Glycoprotein Biomarker Panel for Pancreatic Cancer Discovered by Quantitative Proteomics Analysis

Pancreatic cancer is a lethal disease where specific early detection biomarkers would be very valuable to improve outcomes in patients. Many previous studies have compared biosamples from pancreatic cancer patients with healthy controls to find potential biomarkers. However, a range of related disease conditions can influence the performance of these putative biomarkers, including pancreatitis and diabetes. In this study, quantitative proteomics methods were applied to discover potential serum glycoprotein biomarkers that distinguish pancreatic cancer from other pancreas related conditions (diabetes, cyst, chronic pancreatitis, obstructive jaundice) and healthy controls. Aleuria aurantia lectin (AAL) was used to extract fucosylated glycoproteins and then both TMT protein-level labeling and label-free quantitative analysis were performed to analyze glycoprotein differences from 179 serum samples across the six different conditions. A total of 243 and 354 serum proteins were identified and quantified by label-free and TMT protein-level quantitative strategies, respectively. Nineteen and 25 proteins were found to show significant differences in samples between the pancreatic cancer and other conditions using the label-free and TMT strategies, respectively, with 7 proteins considered significant in both methods. Significantly different glycoproteins were further validated by lectin-ELISA and ELISA assays. Four candidates were identified as potential markers with profiles found to be highly complementary with CA 19–9 (p < 0.001). Obstructive jaundice (OJ) was found to have a significant impact on the performance of every marker protein, including CA 19–9. The combination of α-1-antichymotrypsin (AACT), thrombospondin-1 (THBS1), and haptoglobin (HPT) outperformed CA 19–9 in distinguishing pancreatic cancer from normal controls (AUC = 0.95), diabetes (AUC = 0.89), cyst (AUC = 0.82), and chronic pancreatitis (AUC = 0.90). A marker panel of AACT, THBS1, HPT, and CA 19–9 showed a high diagnostic potential in distinguishing pancreatic cancer from other conditions with OJ (AUC = 0.92) or without OJ (AUC = 0.95).

[1]  D. Pleskow,et al.  Evaluation of a serologic marker, CA19-9, in the diagnosis of pancreatic cancer. , 1989, Annals of internal medicine.

[2]  K. Lillemoe,et al.  Surgical palliation of unresectable periampullary adenocarcinoma in the 1990s. , 1999, Journal of the American College of Surgeons.

[3]  D. Winchester,et al.  Pancreatic cancer: a report of treatment and survival trends for 100,313 patients diagnosed from 1985-1995, using the National Cancer Database. , 1999, Journal of the American College of Surgeons.

[4]  E. Gold,et al.  Epidemiology of pancreatic cancer , 1984, World Journal of Surgery.

[5]  Mary Ann Comunale,et al.  Proteomic analysis of serum associated fucosylated glycoproteins in the development of primary hepatocellular carcinoma. , 2006, Journal of proteome research.

[6]  D. McMillan,et al.  Chronic inflammation and pancreatic cancer. , 2008, Best practice & research. Clinical gastroenterology.

[7]  Michelle A. Anderson,et al.  A Mouse to Human Search for Plasma Proteome Changes Associated with Pancreatic Tumor Development , 2008, PLoS medicine.

[8]  J. Maurel,et al.  CA 19-9 as a biomarker in advanced pancreatic cancer patients randomised to gemcitabine plus axitinib or gemcitabine alone , 2009, British Journal of Cancer.

[9]  Ruedi Aebersold,et al.  Targeted proteomic strategy for clinical biomarker discovery , 2009, Molecular oncology.

[10]  A. Jemal,et al.  Cancer Statistics, 2009 , 2009, CA: a cancer journal for clinicians.

[11]  Ruedi Aebersold,et al.  Mass Spectrometry Based Glycoproteomics—From a Proteomics Perspective* , 2010, Molecular & Cellular Proteomics.

[12]  Susan J Fisher,et al.  Sweetening the pot: adding glycosylation to the biomarker discovery equation. , 2010, Clinical chemistry.

[13]  M. Eloubeidi,et al.  Molecular and clinical markers of pancreas cancer. , 2010, JOP : Journal of the pancreas.

[14]  Kristin L Cheek,et al.  Depletion of abundant plasma proteins and limitations of plasma proteomics. , 2010, Journal of proteome research.

[15]  Chen Li,et al.  Identification and confirmation of biomarkers using an integrated platform for quantitative analysis of glycoproteins and their glycosylations. , 2010, Journal of proteome research.

[16]  F. DiMeco,et al.  Identification of cell surface glycoprotein markers for glioblastoma-derived stem-like cells using a lectin microarray and LC-MS/MS approach. , 2010, Journal of proteome research.

[17]  H. Matsumoto,et al.  Clinical application of a lectin-antibody ELISA to measure fucosylated haptoglobin in sera of patients with pancreatic cancer , 2010, Clinical chemistry and laboratory medicine.

[18]  Development of Serum Glycoproteomic Profiling Technique; Simultaneous Identification of Glycosylation Sites and Site-Specific Quantification of Glycan Structure Changes* , 2010, Molecular & Cellular Proteomics.

[19]  W. Kohlmann,et al.  Identification and screening of individuals at increased risk for pancreatic cancer with emphasis on known environmental and genetic factors and hereditary syndromes. , 2010, JOP : Journal of the pancreas.

[20]  J. Habermann,et al.  Serum biomarkers for improved diagnostic of pancreatic cancer: a current overview , 2011, Journal of Cancer Research and Clinical Oncology.

[21]  Yusuke Nakamura,et al.  Development of Serum Glycoproteomic Profiling Technique; Simultaneous Identification of Glycosylation Sites and Site-Specific Quantification of Glycan Structure Changes* , 2010, Molecular & Cellular Proteomics.

[22]  T. Brentnall.  Editorial: Pancreatic Cancer Surveillance: Learning As We Go , 2011, The American Journal of Gastroenterology.

[23]  Michelle A. Anderson,et al.  Mass spectrometric assay for analysis of haptoglobin fucosylation in pancreatic cancer. , 2011, Journal of proteome research.

[24]  R. Dean,et al.  Comparison of stable-isotope labeling with amino acids in cell culture and spectral counting for relative quantification of protein expression. , 2011, Rapid communications in mass spectrometry : RCM.

[25]  Damon H. May,et al.  Protein alterations associated with pancreatic cancer and chronic pancreatitis found in human plasma using global quantitative proteomics profiling. , 2011, Journal of proteome research.

[26]  R. Srinivasan,et al.  Major Molecular Markers in Pancreatic Ductal Adenocarcinoma and Their Roles in Screening, Diagnosis, Prognosis, and Treatment , 2011, Pancreas.

[27]  Jun Liu,et al.  Diabetes mellitus and risk of pancreatic cancer: A meta-analysis of cohort studies. , 2011, European journal of cancer.

[28]  K. Goh,et al.  Early detection of pancreatic cancer: A possibility in some cases but not a reality in most , 2012, Journal of digestive diseases.

[29]  C. Borrebaeck,et al.  Identification of serum biomarker signatures associated with pancreatic cancer. , 2012, Cancer research.

[30]  Pengyuan Yang,et al.  Quantitative proteomic analysis of serum proteins in patients with Parkinson's disease using an isobaric tag for relative and absolute quantification labeling, two-dimensional liquid chromatography, and tandem mass spectrometry. , 2012, The Analyst.

[31]  R. Buckanovich,et al.  Identification and confirmation of differentially expressed fucosylated glycoproteins in the serum of ovarian cancer patients using a lectin array and LC-MS/MS. , 2012, Journal of proteome research.

[32]  S. Batra,et al.  Early diagnosis of pancreatic cancer: challenges and new developments. , 2012, Biomarkers in medicine.

[33]  R. Brand,et al.  Diverse monoclonal antibodies against the CA 19‐9 antigen show variation in binding specificity with consequences for clinical interpretation , 2012, Proteomics.

[34]  W. Hwang,et al.  Relative quantification of serum proteins from pancreatic ductal adenocarcinoma patients by stable isotope dilution liquid chromatography-mass spectrometry. , 2012, Journal of proteome research.

[35]  Richard J. Giannone,et al.  Putting the Pieces Together: High-performance LC-MS/MS Provides Network-, Pathway-, and Protein-level Perspectives in Populus* , 2012, Molecular & Cellular Proteomics.

[36]  D. Simeone,et al.  Identification of glycoprotein markers for pancreatic cancer CD24+CD44+ stem-like cells using nano-LC-MS/MS and tissue microarray. , 2012, Journal of proteome research.

[37]  William S Hancock,et al.  Using lectins to harvest the plasma/serum glycoproteome , 2012, Electrophoresis.

[38]  H. Friess,et al.  iTRAQ reveals candidate pancreatic cancer serum biomarkers: influence of obstructive jaundice on their performance , 2013, British Journal of Cancer.

[39]  M. Saif,et al.  Diabetes and pancreatic cancer. , 2013, Journal of the Pancreas.

[40]  S. Nie,et al.  Isobaric protein-level labeling strategy for serum glycoprotein quantification analysis by liquid chromatography-tandem mass spectrometry. , 2013, Analytical chemistry.