title: Use of proteomic patterns in serum to identify ovarian cancer.
abstract: BACKGROUND
  New technologies for the detection of early-stage ovarian cancer are urgently needed. Pathological changes within an organ might be reflected in proteomic patterns in serum. We developed a bioinformatics tool and used it to identify proteomic patterns in serum that distinguish neoplastic from non-neoplastic disease within the ovary.
  
  
  METHODS
  Proteomic spectra were generated by mass spectroscopy (surface-enhanced laser desorption and ionisation). A preliminary "training" set of spectra derived from analysis of serum from 50 unaffected women and 50 patients with ovarian cancer were analysed by an iterative searching algorithm that identified a proteomic pattern that completely discriminated cancer from non-cancer. The discovered pattern was then used to classify an independent set of 116 masked serum samples: 50 from women with ovarian cancer, and 66 from unaffected women or those with non-malignant disorders.
  
  
  RESULTS
  The algorithm identified a cluster pattern that, in the training set, completely segregated cancer from non-cancer. The discriminatory pattern correctly identified all 50 ovarian cancer cases in the masked set, including all 18 stage I cases. Of the 66 cases of non-malignant disease, 63 were recognised as not cancer. This result yielded a sensitivity of 100% (95% CI 93--100), specificity of 95% (87--99), and positive predictive value of 94% (84--99).
  
  
  CONCLUSIONS
  These findings justify a prospective population-based assessment of proteomic pattern technology as a screening tool for all stages of ovarian cancer in high-risk and general populations.
authors.affiliation: Food and Drug Administration/National Institutes of Health Clinical Proteomics Program, Department of Therapeutic Proteins/Center for Biologics Evaluation and Research, Food and Drug Administration, Bethesda, MD, USA. petricoin@cber.fda.gov
authors.forename: Emanuel F
authors.initials: EF
authors.lastname: Petricoin
authors.forename: Ali M
authors.initials: AM
authors.lastname: Ardekani
authors.forename: Ben A
authors.initials: BA
authors.lastname: Hitt
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Levine
authors.forename: Vincent A
authors.initials: VA
authors.lastname: Fusaro
authors.forename: Seth M
authors.initials: SM
authors.lastname: Steinberg
authors.forename: Gordon B
authors.initials: GB
authors.lastname: Mills
authors.forename: Charles
authors.initials: C
authors.lastname: Simone
authors.forename: David A
authors.initials: DA
authors.lastname: Fishman
authors.forename: Elise C
authors.initials: EC
authors.lastname: Kohn
authors.forename: Lance A
authors.initials: LA
authors.lastname: Liotta
mesh_terms: D018394:CA-125 Antigen / Q000097:blood; D005260:Female; D006801:Humans; D008403:Mass Screening / Q000379:methods; D010051:Ovarian Neoplasms / Q000097:blood* / Q000175:diagnosis; D011237:Predictive Value of Tests; D020543:Proteome / Q000302:isolation & purification*
chemical_list: D018394:CA-125 Antigen; D020543:Proteome