title: Diagnostic markers for early detection of ovarian cancer.
abstract: OBJECTIVE
  Early detection would significantly decrease the mortality rate of ovarian cancer. In this study, we characterize and validate the combination of six serum biomarkers that discriminate between disease-free and ovarian cancer patients with high efficiency.
  
  
  METHODS
  We analyzed 362 healthy controls and 156 newly diagnosed ovarian cancer patients. Concentrations of leptin, prolactin, osteopontin, insulin-like growth factor II, macrophage inhibitory factor, and CA-125 were determined using a multiplex, bead-based, immunoassay system. All six markers were evaluated in a training set (181 samples from the control group and 113 samples from OC patients) and a test set (181 sample control group and 43 ovarian cancer).
  
  
  RESULTS
  Multiplex and ELISA exhibited the same pattern of expression for all the biomarkers. None of the biomarkers by themselves were good enough to differentiate healthy versus cancer cells. However, the combination of the six markers provided a better differentiation than CA-125. Four models with <2% classification error in training sets all had significant improvement (sensitivity 84%-98% at specificity 95%) over CA-125 (sensitivity 72% at specificity 95%) in the test set. The chosen model correctly classified 221 out of 224 specimens in the test set, with a classification accuracy of 98.7%.
  
  
  CONCLUSIONS
  We describe the first blood biomarker test with a sensitivity of 95.3% and a specificity of 99.4% for the detection of ovarian cancer. Six markers provided a significant improvement over CA-125 alone for ovarian cancer detection. Validation was performed with a blinded cohort. This novel multiplex platform has the potential for efficient screening in patients who are at high risk for ovarian cancer.
authors.affiliation: Department of Obstetrics and Gynecology, Yale University School of Medicine, New Haven, Connecticut 06520, USA.
authors.forename: Irene
authors.initials: I
authors.lastname: Visintin
authors.forename: Ziding
authors.initials: Z
authors.lastname: Feng
authors.forename: Gary
authors.initials: G
authors.lastname: Longton
authors.forename: David C
authors.initials: DC
authors.lastname: Ward
authors.forename: Ayesha B
authors.initials: AB
authors.lastname: Alvero
authors.forename: Yinglei
authors.initials: Y
authors.lastname: Lai
authors.forename: Jeannette
authors.initials: J
authors.lastname: Tenthorey
authors.forename: Aliza
authors.initials: A
authors.lastname: Leiser
authors.forename: Ruben
authors.initials: R
authors.lastname: Flores-Saaib
authors.forename: Herbert
authors.initials: H
authors.lastname: Yu
authors.forename: Masoud
authors.initials: M
authors.lastname: Azori
authors.forename: Thomas
authors.initials: T
authors.lastname: Rutherford
authors.forename: Peter E
authors.initials: PE
authors.lastname: Schwartz
authors.forename: Gil
authors.initials: G
authors.lastname: Mor
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D014408:Biomarkers, Tumor / Q000097:blood*; D018394:CA-125 Antigen / Q000097:blood; D016207:Cytokines / Q000097:blood; D042241:Early Diagnosis; D004797:Enzyme-Linked Immunosorbent Assay; D005189:False Positive Reactions; D005260:Female; D055436:Growth Differentiation Factor 15; D006801:Humans; D007335:Insulin-Like Growth Factor II / Q000032:analysis; D020738:Leptin / Q000097:blood; D008875:Middle Aged; D053495:Osteopontin / Q000097:blood; D010051:Ovarian Neoplasms / Q000097:blood* / Q000175:diagnosis*; D011388:Prolactin / Q000097:blood; D012680:Sensitivity and Specificity
chemical_list: D014408:Biomarkers, Tumor; D018394:CA-125 Antigen; D016207:Cytokines; C526140:GDF15 protein, human; D055436:Growth Differentiation Factor 15; C035170:IGF2 protein, human; D020738:Leptin; D053495:Osteopontin; D007335:Insulin-Like Growth Factor II; D011388:Prolactin