PURPOSE: Mass spectrometry-based serum peptidome profiling is a promising tool to identify novel disease-associated biomarkers, but is limited by preanalytic factors and the intricacies of complex data processing. Therefore, we investigated whether standardized sample protocols and new bioinformatic tools combined with external data validation improve the validity of peptidome profiling for the discovery of pancreatic cancer-associated serum markers. EXPERIMENTAL DESIGN: For the discovery study, two sets of sera from patients with pancreatic cancer (n = 40) and healthy controls (n = 40) were obtained from two different clinical centers. For external data validation, we collected an independent set of samples from patients (n = 20) and healthy controls (n = 20). Magnetic beads with different surface functionalities were used for peptidome fractionation followed by matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS). Data evaluation was carried out by comparing two different bioinformatic strategies. Following proteome database search, the matching candidate peptide was verified by MALDI-TOF MS after specific antibody-based immunoaffinity chromatography and independently confirmed by an ELISA assay. RESULTS: Two significant peaks (m/z 3884; 5959) achieved a sensitivity of 86.3% and a specificity of 97.6% for the discrimination of patients and healthy controls in the external validation set. Adding peak m/z 3884 to conventional clinical tumor markers (CA 19-9 and CEA) improved sensitivity and specificity, as shown by receiver operator characteristics curve analysis (AUROC(combined) = 1.00). Mass spectrometry-based m/z 3884 peak identification and following immunologic quantitation revealed platelet factor 4 as the corresponding peptide. CONCLUSIONS: MALDI-TOF MS-based serum peptidome profiling allowed the discovery and validation of platelet factor 4 as a new discriminating marker in pancreatic cancer.
PURPOSE: Mass spectrometry-based serum peptidome profiling is a promising tool to identify novel disease-associated biomarkers, but is limited by preanalytic factors and the intricacies of complex data processing. Therefore, we investigated whether standardized sample protocols and new bioinformatic tools combined with external data validation improve the validity of peptidome profiling for the discovery of pancreatic cancer-associated serum markers. EXPERIMENTAL DESIGN: For the discovery study, two sets of sera from patients with pancreatic cancer (n = 40) and healthy controls (n = 40) were obtained from two different clinical centers. For external data validation, we collected an independent set of samples from patients (n = 20) and healthy controls (n = 20). Magnetic beads with different surface functionalities were used for peptidome fractionation followed by matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS). Data evaluation was carried out by comparing two different bioinformatic strategies. Following proteome database search, the matching candidate peptide was verified by MALDI-TOF MS after specific antibody-based immunoaffinity chromatography and independently confirmed by an ELISA assay. RESULTS: Two significant peaks (m/z 3884; 5959) achieved a sensitivity of 86.3% and a specificity of 97.6% for the discrimination of patients and healthy controls in the external validation set. Adding peak m/z 3884 to conventional clinical tumor markers (CA 19-9 and CEA) improved sensitivity and specificity, as shown by receiver operator characteristics curve analysis (AUROC(combined) = 1.00). Mass spectrometry-based m/z 3884 peak identification and following immunologic quantitation revealed platelet factor 4 as the corresponding peptide. CONCLUSIONS: MALDI-TOF MS-based serum peptidome profiling allowed the discovery and validation of platelet factor 4 as a new discriminating marker in pancreatic cancer.
Authors: Katherine E Poruk; Matthew A Firpo; Luke M Huerter; Courtney L Scaife; Lyska L Emerson; Kenneth M Boucher; Kimberly A Jones; Sean J Mulvihill Journal: Cancer Epidemiol Biomarkers Prev Date: 2010-08-20 Impact factor: 4.254
Authors: Alexander Benedikt Leichtle; Uta Ceglarek; Peter Weinert; Christos T Nakas; Jean-Marc Nuoffer; Julia Kase; Tim Conrad; Helmut Witzigmann; Joachim Thiery; Georg Martin Fiedler Journal: Metabolomics Date: 2012-11-06 Impact factor: 4.290
Authors: Alexander Benedikt Leichtle; Jean-Marc Nuoffer; Uta Ceglarek; Julia Kase; Tim Conrad; Helmut Witzigmann; Joachim Thiery; Georg Martin Fiedler Journal: Metabolomics Date: 2011-09-16 Impact factor: 4.290
Authors: Yaojun Li; Yueguo Li; Tao Chen; Anna S Kuklina; Paul Bernard; Francisco J Esteva; Haifa Shen; Mauro Ferrari; Ye Hu Journal: Clin Chem Date: 2013-10-21 Impact factor: 8.327