Prediction of lung cancer using volatile biomarkers in breath.
Source
Menssana Research Inc, Fort Lee, NJ 07024-6510, USA.
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.