Background-A combination of biomarkers in a multivariate model may predict disease with greater accuracy than a single biomarker employed alone. We developed a non-linear method of multivariate analysis, weighted digital analysis (WDA), and evaluated its ability to predict lung cancer employing volatile biomarkers in the breath.

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We sought biomarkers of breast cancer in the breath because the disease is accompanied by increased oxidative stress and induction of cytochrome P450 enzymes, both of which generate volatile organic compounds (VOCs) that are excreted in breath. We analyzed breath VOCs in 54 women with biopsy-proven breast cancer and 204 cancer-free controls, using gas chromatography/mass spectroscopy. Chromatograms were converted into a series of data points by segmenting them into 900 time slices (8 s duration, 4 s overlap) and determining their alveolar gradients (abundance in breath minus abundance in ambient room air). Monte Carlo simulations identified time slices with better than random accuracy as biomarkers of breast cancer by excluding random identifiers. Patients were randomly allocated to training sets or test sets in 2:1 data splits. In the training sets, time slices were ranked according their C-statistic values (area under curve of receiver operating characteristic), and the top ten time slices were combined in multivariate algorithms that were cross-validated in the test sets. Monte Carlo simulations identified an excess of correct over random time slices, consistent with non-random biomarkers of breast cancer in the breath. The outcomes of ten random data splits (mean (standard deviation)) in the training sets were sensitivity = 78.5% (6.14), specificity = 88.3% (5.47), C-statistic = 0.89 (0.03) and in the test sets, sensitivity = 75.3% (7.22), specificity = 84.8 (9.97), C-statistic = 0.83 (0.06). A breath test identified women with breast cancer, employing a combination of volatile biomarkers in a multivariate algorithm.

Viral infections cause increased oxidative stress, so a breath test for oxidative stress biomarkers (alkanes and alkane derivatives) might provide a new tool for early diagnosis. We studied 33 normal healthy human subjects receiving scheduled treatment with live attenuated influenza vaccine (LAIV). Each subject was his or her own control, since they were studied on day 0 prior to vaccination, and then on days 2, 7 and 14 following vaccination. Breath volatile organic compounds (VOCs) were collected with a breath collection apparatus, then analyzed by automated thermal desorption with gas chromatography and mass spectroscopy. A Monte Carlo simulation technique identified non-random VOC biomarkers of infection based on their C-statistic values (area under curve of receiver operating characteristic). Treatment with LAIV was followed by non-random changes in the abundance of breath VOCs. 2, 8-Dimethylundecane and other alkane derivatives were observed on all days. Conservative multivariate models identified vaccinated subjects on day 2 (C-statistic = 0.82, sensitivity = 63.6% and specificity = 88.5%); day 7 (C-statistic = 0.94, sensitivity = 88.5% and specificity = 92.3%); and day 14 (C-statistic = 0.95, sensitivity = 92.3% and specificity = 92.3%). The altered breath VOCs were not detected in live attenuated influenza vaccine, excluding artifactual contamination. LAIV vaccination in healthy humans elicited a prompt and sustained increase in breath biomarkers of oxidative stress. A breath test for these VOCs could potentially identify humans who are acutely infected with influenza, but who have not yet developed clinical symptoms or signs of disease.

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