title: Identification of an autoantibody panel to separate lung cancer from smokers and nonsmokers.
abstract: BACKGROUND
  Sera from lung cancer patients contain autoantibodies that react with tumor associated antigens (TAAs) that reflect genetic over-expression, mutation, or other anomalies of cell cycle, growth, signaling, and metabolism pathways.
  
  
  METHODS
  We performed immunoassays to detect autoantibodies to ten tumor associated antigens (TAAs) selected on the basis of previous studies showing that they had preferential specificity for certain cancers. Sera examined were from lung cancer patients (22); smokers with ground-glass opacities (GGOs) (46), benign solid nodules (55), or normal CTs (35); and normal non-smokers (36). Logistic regression models based on the antibody biomarker levels among the high risk and lung cancer groups were developed to identify the combinations of biomarkers that predict lung cancer in these cohorts.
  
  
  RESULTS
  Statistically significant differences in the distributions of each of the biomarkers were identified among all five groups. Using Receiver Operating Characteristic (ROC) curves based on age, c-myc, Cyclin A, Cyclin B1, Cyclin D1, CDK2, and survivin, we obtained a sensitivity = 81% and specificity = 97% for the classification of cancer vs smokers(no nodules, solid nodules, or GGO) and correctly predicted 31/36 healthy controls as noncancer.
  
  
  CONCLUSIONS
  A pattern of autoantibody reactivity to TAAs may distinguish patients with lung cancer versus smokers with normal CTs, stable solid nodules, ground glass opacities, or normal healthy never smokers.
authors.affiliation: Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Radiology, Thoracic Surgery, and Environmental Medicine, New York University School of Medicine, New York, NY 10016, USA. william.rom@nyumc.org
authors.forename: William N
authors.initials: WN
authors.lastname: Rom
authors.forename: Judith D
authors.initials: JD
authors.lastname: Goldberg
authors.forename: Doreen
authors.initials: D
authors.lastname: Addrizzo-Harris
authors.forename: Heather N
authors.initials: HN
authors.lastname: Watson
authors.forename: Michael
authors.initials: M
authors.lastname: Khilkin
authors.forename: Alissa K
authors.initials: AK
authors.lastname: Greenberg
authors.forename: David P
authors.initials: DP
authors.lastname: Naidich
authors.forename: Bernard
authors.initials: B
authors.lastname: Crawford
authors.forename: Ellen
authors.initials: E
authors.lastname: Eylers
authors.forename: Daorong
authors.initials: D
authors.lastname: Liu
authors.forename: Eng M
authors.initials: EM
authors.lastname: Tan
mesh_terms: D000368:Aged; D000951:Antigens, Neoplasm / Q000276:immunology*; D001323:Autoantibodies / Q000097:blood*; D055088:Early Detection of Cancer; D005260:Female; D006801:Humans; D007118:Immunoassay; D016015:Logistic Models; D008175:Lung Neoplasms / Q000000981:diagnostic imaging / Q000276:immunology*; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models; D012372:ROC Curve; D018570:Risk Assessment; D012307:Risk Factors; D012907:Smoking / Q000009:adverse effects / Q000276:immunology*; D014057:Tomography, X-Ray Computed
chemical_list: D000951:Antigens, Neoplasm; D001323:Autoantibodies