title: Serum proteomic fingerprinting discriminates between clinical stages and predicts disease progression in melanoma patients.
abstract: OBJECTIVE
  Currently known serum biomarkers do not predict clinical outcome in melanoma. S100-beta is widely established as a reliable prognostic indicator in patients with advanced metastatic disease but is of limited predictive value in tumor-free patients. This study was aimed to determine whether molecular profiling of the serum proteome could discriminate between early- and late-stage melanoma and predict disease progression.
  
  
  METHODS
  Two hundred five serum samples from 101 early-stage (American Joint Committee on Cancer [AJCC] stage I) and 104 advanced stage (AJCC stage IV) melanoma patients were analyzed by matrix-assisted laser desorption/ionisation (MALDI) time-of-flight (ToF; MALDI-ToF) mass spectrometry utilizing protein chip technology and artificial neural networks (ANN). Serum samples from 55 additional patients after complete dissection of regional lymph node metastases (AJCC stage III), with 28 of 55 patients relapsing within the first year of follow-up, were analyzed in an attempt to predict disease recurrence. Serum S100-beta was measured using a sandwich immunoluminometric assay.
  
  
  RESULTS
  Analysis of 205 stage I/IV serum samples, utilizing a training set of 94 of 205 and a test set of 15 of 205 samples for 32 different ANN models, revealed correct stage assignment in 84 (88%) of 96 of a blind set of 96 of 205 serum samples. Forty-four (80%) of 55 stage III serum samples could be correctly assigned as progressors or nonprogressors using random sample cross-validation statistical methodologies. Twenty-three (82%) of 28 stage III progressors were correctly identified by MALDI-ToF combined with ANN, whereas only six (21%) of 28 could be detected by S100-beta.
  
  
  CONCLUSIONS
  Validation of these findings may enable proteomic profiling to become a valuable tool for identifying high-risk melanoma patients eligible for adjuvant therapeutic interventions.
authors.affiliation: Interdisciplinary Biomedical Research Centre, School of Science, Nottingham Trent University, Clifton, Nottingham NG11 8NS, UK.
authors.forename: Shahid
authors.initials: S
authors.lastname: Mian
authors.forename: Selma
authors.initials: S
authors.lastname: Ugurel
authors.forename: Erika
authors.initials: E
authors.lastname: Parkinson
authors.forename: Iris
authors.initials: I
authors.lastname: Schlenzka
authors.forename: Ian
authors.initials: I
authors.lastname: Dryden
authors.forename: Lee
authors.initials: L
authors.lastname: Lancashire
authors.forename: Graham
authors.initials: G
authors.lastname: Ball
authors.forename: Colin
authors.initials: C
authors.lastname: Creaser
authors.forename: Robert
authors.initials: R
authors.lastname: Rees
authors.forename: Dirk
authors.initials: D
authors.lastname: Schadendorf
mesh_terms: D018450:Disease Progression; D006801:Humans; D013058:Mass Spectrometry; D008545:Melanoma / Q000473:pathology*; D009364:Neoplasm Recurrence, Local; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011379:Prognosis; D040081:Protein Array Analysis; D040901:Proteomics; D012307:Risk Factors; D012680:Sensitivity and Specificity; D012878:Skin Neoplasms / Q000473:pathology*