title: Proteomic classification of pancreatic adenocarcinoma tissue using protein chip technology.
abstract: OBJECTIVE
  Pancreatic adenocarcinoma is a most devastating cancer that presents late and is rapidly progressive. This study aimed to identify unique, tissue-specific protein biomarkers capable of differentiating pancreatic adenocarcinoma (PC) from adjacent uninvolved pancreatic tissue (AP), benign pancreatic disease (B), and nonmalignant tumor tissue (NM).
  
  
  METHODS
  Tissue samples representing PC (n = 31), AP (n = 44), and B (n = 19) tissue were analyzed on hydrophobic protein chip arrays by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry. Training models were developed using logistic regression and validated using the 10-fold cross-validation approach.
  
  
  RESULTS
  The hydrophobic protein chip array revealed 13 protein peaks differentially expressed between PC and AP (receiver operating characteristic [ROC] area under the curve [AUC], 0.64-0.85), 8 between PC and B (ROC AUC, 0.67-0.78), and 12 between PC and NM tissue (ROC AUC, 0.63-0.81). Logistic regression and cross-validation identified overlapping panels of peaks to develop a training model that distinguished PC from AP (77.4% sensitivity, 84.1% specificity), B (83.9% sensitivity, 78.9% specificity), and NM tissue (58.1% sensitivity, 90.5% specificity). The final panels selected correctly classified 80.6% of PC and 88.6% of AP samples (ROC AUC, 0.92), 93.5% of PC and 89.5% of B samples (ROC AUC, 0.99), and 71.0% of PC and 92.1% of NM samples (ROC AUC, 0.91).
  
  
  CONCLUSIONS
  This study used surface-enhanced laser desorption/ionization time-of-flight mass spectrometry to discover a number of protein panels that can distinguish effectively between pancreatic adenocarcinoma, benign, and adjacent pancreatic tissue. Identification of these proteins will add to our understanding of the biology of pancreatic cancer. Furthermore, these protein panels may have important diagnostic implications.
authors.affiliation: Department of Surgery, University of Sydney, Royal North Shore Hospital, St Leonards, New South Wales, Australia.
authors.forename: Christopher J
authors.initials: CJ
authors.lastname: Scarlett
authors.forename: Ross C
authors.initials: RC
authors.lastname: Smith
authors.forename: Alex
authors.initials: A
authors.lastname: Saxby
authors.forename: Aiqun
authors.initials: A
authors.lastname: Nielsen
authors.forename: Jaswinder S
authors.initials: JS
authors.lastname: Samra
authors.forename: Susan R
authors.initials: SR
authors.lastname: Wilson
authors.forename: Robert C
authors.initials: RC
authors.lastname: Baxter
mesh_terms: D000230:Adenocarcinoma / Q000473:pathology* / Q000601:surgery; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000704:Analysis of Variance; D014408:Biomarkers, Tumor / Q000032:analysis*; D001707:Biopsy, Needle; D016022:Case-Control Studies; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D007150:Immunohistochemistry; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D010190:Pancreatic Neoplasms / Q000473:pathology* / Q000601:surgery; D050500:Pancreatitis, Chronic / Q000473:pathology* / Q000601:surgery; D011379:Prognosis; D040901:Proteomics / Q000145:classification*; D012372:ROC Curve; D012016:Reference Values; D018570:Risk Assessment; D012494:Sampling Studies; D012680:Sensitivity and Specificity; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; D018709:Statistics, Nonparametric
chemical_list: D014408:Biomarkers, Tumor