title: Prediction of lung cancer using volatile biomarkers in breath.
abstract: BACKGROUND
  Normal metabolism generates several volatile organic compounds (VOCs) that are excreted in the breath (e.g. alkanes). In patients with lung cancer, induction of high-risk cytochrome p450 genotypes may accelerate catabolism of these VOCs, so that their altered abundance in breath may provide biomarkers of lung cancer.
  
  
  METHODS
  VOCs in 1.0 L alveolar breath were analyzed in 193 subjects with primary lung cancer and 211 controls with a negative chest CT. Subjects were randomly assigned to a training set or to a prediction set in a 2:1 split. A fuzzy logic model of breath biomarkers of lung cancer was constructed in the training set and then tested in subjects in the prediction set by generating their typicality scores for lung cancer.
  
  
  RESULTS
  Mean typicality scores employing a 16 VOC model were significantly higher in lung cancer patients than in the control group (p<0.0001 in all TNM stages). The model predicted primary lung cancer with 84.6% sensitivity, 80.0% specificity, and 0.88 area under curve (AUC) of the receiver operating characteristic (ROC) curve. Predictive accuracy was similar in TNM stages 1 through 4, and was not affected by current or former tobacco smoking. The predictive model achieved near-maximal performance with six breath VOCs, and was progressively degraded by random classifiers. Predictions with fuzzy logic were consistently superior to multilinear analysis. If applied to a population with 2% prevalence of lung cancer, a screening breath test would have a negative predictive value of 0.985 and a positive predictive value of 0.163 (true positive rate =0.277, false positive rate =0.029).
  
  
  CONCLUSIONS
  A two-minute breath test predicted lung cancer with accuracy comparable to screening CT of chest. The accuracy of the test was not affected by TNM stage of disease or tobacco smoking. Alterations in breath VOCs in lung cancer were consistent with a non-linear pathophysiologic process, such as an off-on switch controlling high-risk cytochrome p450 activity. Further research is needed to determine if detection of lung cancer with this test will reduce mortality.
authors.affiliation: Menssana Research Inc, Fort Lee, NJ 07024-6510, USA. mphillips@menssanaresearch.com
authors.forename: Michael
authors.initials: M
authors.lastname: Phillips
authors.forename: Nasser
authors.initials: N
authors.lastname: Altorki
authors.forename: John H M
authors.initials: JH
authors.lastname: Austin
authors.forename: Robert B
authors.initials: RB
authors.lastname: Cameron
authors.forename: Renee N
authors.initials: RN
authors.lastname: Cataneo
authors.forename: Joel
authors.initials: J
authors.lastname: Greenberg
authors.forename: Robert
authors.initials: R
authors.lastname: Kloss
authors.forename: Roger A
authors.initials: RA
authors.lastname: Maxfield
authors.forename: Muhammad I
authors.initials: MI
authors.lastname: Munawar
authors.forename: Harvey I
authors.initials: HI
authors.lastname: Pass
authors.forename: Asif
authors.initials: A
authors.lastname: Rashid
authors.forename: William N
authors.initials: WN
authors.lastname: Rom
authors.forename: Peter
authors.initials: P
authors.lastname: Schmitt
mesh_terms: D000368:Aged; D019540:Area Under Curve; D014408:Biomarkers, Tumor / Q000032:analysis*; D001944:Breath Tests; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000175:diagnosis*; D008297:Male; D015233:Models, Statistical; D009367:Neoplasm Staging; D009930:Organic Chemicals / Q000032:analysis; D012372:ROC Curve; D012680:Sensitivity and Specificity
chemical_list: D014408:Biomarkers, Tumor; D009930:Organic Chemicals