title: Autoantibody biomarkers identified by proteomics methods distinguish ovarian cancer from non-ovarian cancer with various CA-125 levels.
abstract: OBJECTIVE
  CA-125 has been a valuable marker for detecting ovarian cancer, however, it is not sensitive enough to detect early-stage disease and not specific to ovarian cancer. The purpose of our study was to identify autoantibody markers that are specific to ovarian cancer regardless of CA-125 levels.
  
  
  METHODS
  Top-down and iTRAQ quantitative proteomics methods were used to identify high-frequency autoantibodies in ovarian cancer. Protein microarrays comprising the recombinant autoantigens were screened using serum samples from various stages of ovarian cancer with diverse levels of CA-125 as well as benign and healthy controls. ROC curve and dot blot analyses were performed to validate the sensitivity and specificity of the autoantibody markers.
  
  
  RESULTS
  The proteomics methodologies identified more than 60 potential high-frequency autoantibodies in ovarian cancer. Individual serum samples from ovarian cancer stages I-IV compared to control samples that were screened on a microarray containing native recombinant autoantigens revealed a panel of stage I high-frequency autoantibodies. Preliminary ROC curve and dot blot analyses performed with the ovarian cancer samples showed higher specificity and sensitivity as compared to CA-125. Three autoantibody markers exhibited higher specificity in various stages of ovarian cancer with low and normal CA-125 levels.
  
  
  CONCLUSIONS
  Proteomics technologies are suitable for the identification of protein biomarkers and also the identification of autoantibody biomarkers when combined with protein microarray screening. Using native recombinant autoantigen arrays to screen autoantibody markers, it is possible to identify markers with higher sensitivity and specificity than CA-125 that are relevant to early detection of ovarian cancer.
authors.affiliation: Immunotope, Inc., Doylestown, PA, 18902, USA.
authors.forename: Aykan A
authors.initials: AA
authors.lastname: Karabudak
authors.forename: Julie
authors.initials: J
authors.lastname: Hafner
authors.forename: Vivekananda
authors.initials: V
authors.lastname: Shetty
authors.forename: Songming
authors.initials: S
authors.lastname: Chen
authors.forename: Angeles Alvarez
authors.initials: AA
authors.lastname: Secord
authors.forename: Michael A
authors.initials: MA
authors.lastname: Morse
authors.forename: Ramila
authors.initials: R
authors.lastname: Philip
mesh_terms: D000230:Adenocarcinoma / Q000097:blood* / Q000175:diagnosis / Q000276:immunology; D000368:Aged; D000912:Antibodies, Neoplasm / Q000097:blood*; D001323:Autoantibodies / Q000097:blood*; D001324:Autoantigens / Q000276:immunology; D014408:Biomarkers, Tumor / Q000097:blood*; D018394:CA-125 Antigen / Q000097:blood*; D016022:Case-Control Studies; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D008875:Middle Aged; D010051:Ovarian Neoplasms / Q000097:blood* / Q000175:diagnosis / Q000276:immunology; D040081:Protein Array Analysis; D040901:Proteomics; D012372:ROC Curve
chemical_list: D000912:Antibodies, Neoplasm; D001323:Autoantibodies; D001324:Autoantigens; D014408:Biomarkers, Tumor; D018394:CA-125 Antigen