Renee N. Cataneo

Volatile organic compounds in breath as markers of lung cancer: a cross-sectional study.

BACKGROUND Many volatile organic compounds (VOCs), principally alkanes and benzene derivatives, have been identified in breath from patients with lung cancer. We investigated whether a combination of VOCs could identify such patients. METHODS We collected breath samples from 108 patients with an abnormal chest radiograph who were scheduled for bronchoscopy. The samples were collected with a portable apparatus, then assayed by gas chromatography and mass spectroscopy. The alveolar gradient of each breath VOC, the difference between the amount in breath and in air, was calculated. Forward stepwise discriminant analysis was used to identify VOCs that discriminated between patients with and without lung cancer. FINDINGS Lung cancer was confirmed histologically in 60 patients. A combination of 22 breath VOCs, predominantly alkanes, alkane derivatives, and benzene derivatives, discriminated between patients with and without lung cancer, regardless of stage (all p<0.0003). For stage 1 lung cancer, the 22 VOCs had 100% sensitivity and 81.3% specificity. Cross-validation of the combination correctly predicted the diagnosis in 71.7% patients with lung cancer and 66.7% of those without lung cancer. INTERPRETATION In patients with an abnormal chest radiograph, a combination of 22 VOCs in breath samples distinguished between patients with and without lung cancer. Prospective studies are needed to confirm the usefulness of breath VOCs for detecting lung cancer in the general population.

Prediction of lung cancer using volatile biomarkers in breath1

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.

Volatile Markers of Breast Cancer in the Breath

Abstract: Breast cancer is accompanied by increased oxidative stress and induction of polymorphic cytochrome P‐450 mixed oxidase enzymes (CYP). Both processes affect the abundance of volatile organic compounds (VOCs) in the breath because oxidative stress causes lipid peroxidation of polyunsaturated fatty acids in membranes, producing alkanes and methylalkanes which are catabolized by CYP. We performed a pilot study of breath VOCs, a potential new marker of disease in women with breast cancer. This was a combined case‐control and cross‐sectional study of women with abnormal mammograms scheduled for a breast biopsy. Breath samples were analyzed by gas chromatography and mass spectroscopy in order to determine the breath methylated alkane contour (BMAC), a three‐dimensional display of the alveolar gradients (abundance in breath minus abundance in room air) of C4–C20 alkanes and monomethylated alkanes. BMACs in women with and without breast cancer were compared using forward stepwise discriminant analysis. Two hundred one breath samples were obtained from women with abnormal mammograms and biopsies read by two pathologists. There were 51 cases of breast cancer in 198 concordant biopsies. The breath test distinguished between women with breast cancer and healthy volunteers with a sensitivity of 94.1% (48/51) and a specificity of 73.8% (31/42) (cross‐validated sensitivity 88.2% (45/51), specificity 73.8% (31/42)). Compared to women with abnormal mammograms and no cancer on biopsy, the breath test identified breast cancer with a sensitivity of 62.7% (32/51) and a specificity of 84.0% (42/50) (cross‐validated sensitivity of 60.8% (31/51), specificity of 82.0% (41/50)). The negative predictive value (NPV) of a screening breath test for breast cancer was superior to a screening mammogram (99.93% versus 99.89%); the positive predictive value (PPV) of a screening mammogram was superior to a screening breath test (4.63% versus 1.29%). A breath test for markers of oxidative stress accurately identified women with breast cancer, with an NPV superior to a screening mammogram. This breath test could potentially be employed as a primary screen for breast cancer. Confirmatory studies in larger groups are required.

Detection of lung cancer using weighted digital analysis of breath biomarkers

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. METHODS WDA generates a discriminant function to predict membership in disease vs no disease groups by determining weight, a cutoff value, and a sign for each predictor variable employed in the model. The weight of each predictor variable was the area under the curve (AUC) of the receiver operating characteristic (ROC) curve minus a fixed offset of 0.55, where the AUC was obtained by employing that predictor variable alone, as the sole marker of disease. The sign (+/-) was used to invert the predictor variable if a lower value indicated a higher probability of disease. When employed to predict the presence of a disease in a particular patient, the discriminant function was determined as the sum of the weights of all predictor variables that exceeded their cutoff values. The algorithm that generates the discriminant function is deterministic because parameters are calculated from each individual predictor variable without any optimization or adjustment. We employed WDA to re-evaluate data from a recent study of breath biomarkers of lung cancer, comprising the volatile organic compounds (VOCs) in the alveolar breath of 193 subjects with primary lung cancer and 211 controls with a negative chest CT. RESULTS The WDA discriminant function accurately identified patients with lung cancer in a model employing 30 breath VOCs (ROC curve AUC=0.90; sensitivity=84.5%, specificity=81.0%). These results were superior to multilinear regression analysis of the same data set (AUC=0.74, sensitivity=68.4, specificity=73.5%). WDA test accuracy did not vary appreciably with TNM (tumor, node, metastasis) stage of disease, and results were not affected by tobacco smoking (ROC curve AUC=0.92 in current smokers, 0.90 in former smokers). WDA was a robust predictor of lung cancer: random removal of 1/3 of the VOCs did not reduce the AUC of the ROC curve by >10% (99.7% CI). CONCLUSIONS A test employing WDA of breath VOCs predicted lung cancer with accuracy similar to chest computed tomography. The algorithm identified dependencies that were not apparent with traditional linear methods. WDA appears to provide a useful new technique for non-linear multivariate analysis of data.

Breath biomarkers of active pulmonary tuberculosis

BACKGROUND Volatile organic compounds (VOCs) in breath may contain biomarkers of active pulmonary tuberculosis derived from the infectious organism (metabolites of Mycobacterium tuberculosis) and from the infected host (products of oxidative stress). METHODS We analyzed breath VOCs in 226 symptomatic high-risk patients in USA, Philippines, and UK, using gas chromatography/mass spectroscopy. Diagnosis of disease was based on sputum culture, smear microscopy, chest radiography and clinical suspicion of tuberculosis (CSTB). Chromatograms were converted to a series of 8s overlapping time slices. Biomarkers of active pulmonary tuberculosis were identified with a Monte Carlo analysis of time-slice alveolar gradients (abundance in breath minus abundance in room air). RESULTS Breath VOCs contained apparent biomarkers of active pulmonary tuberculosis comprising oxidative stress products (alkanes and alkane derivatives) and volatile metabolites of M. tuberculosis (cyclohexane and benzene derivatives). Breath biomarkers identified active pulmonary tuberculosis with C-statistic (area under curve of receiver operating characteristic)=0.85 (i.e. 85% overall accuracy, sensitivity=84.0%, specificity=64.7%) when sputum culture, microscopy, and chest radiography were either all positive or all negative. Employing a single criterion of disease, C-statistic=0.76 (smear microscopy), 0.68 (sputum culture), 0.66 (chest radiography) and 0.65 (CSTB). CONCLUSION A breath test identified apparent biomarkers of active pulmonary tuberculosis with 85% accuracy in symptomatic high-risk subjects.

Volatile biomarkers in the breath of women with breast cancer

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.

Prediction of breast cancer using volatile biomarkers in the breath

SummaryWe evaluated a breath test for volatile organic compounds (VOCs) as a predictor of breast cancer. Breath VOCs were assayed in 51 asymptomatic women with abnormal mammograms and biopsy-proven breast cancer, and 42 age-matched healthy women. A fuzzy logic model predicted breast cancer with accuracy superior to previously reported findings. Following random assignment to a training set (64) or a prediction set (29), a model was constructed in the training set employing five breath VOCs that predicted breast cancer in the prediction set with 93.8% sensitivity and 84.6% specificity. The same model predicted no breast cancer in 16/50 (32.0%) women with abnormal mammograms and no cancer on biopsy. A two-minute breath test could potentially provide a safe, accurate and painless screening test for breast cancer, but prospective validation studies are required.

Increased oxidative stress in younger as well as in older humans

BACKGROUND The free radical theory of aging is based upon the adverse effects of oxidative stress (OS), and indices of OS generally increase with advancing age. However, since OS may also be a normal physiological response in youth, when reactive oxygen species (ROS) act as signal transducers during normal growth and development, we compared markers of OS in normal humans over a wide spectrum of different ages. METHODS Fasting breath samples were collected from 102 healthy volunteers (age 9 to 89 years) and volatile organic compounds (VOCs) were assayed by gas chromatography and mass spectroscopy. The intensity of OS in each volunteer was estimated by the breath methylated alkane contour (BMAC), a three-dimensional display of the abundance of C4-C20 alkanes and monomethylated alkanes. The collective abundance of these VOCs in a breath sample was reduced to a single value, the volume under curve (VUC), and correlated with chronological age. RESULTS Compared to subjects aged 20-40 years, the mean BMAC VUC was significantly increased in subjects aged < 20 (p < 0.0001) and >40 years (p < 0.001). A cubic function correlated BMAC VUC (x) with chronological age (y): y = 33.7 - 3.29x + 0.072x(2) - 0.0004x(3) (r = 0.48). CONCLUSIONS Breath markers of OS were significantly increased both in younger and in older subjects, compared to those aged 20-40 years. Increased OS in older subjects was consistent with previous reports, but increased OS in younger subjects aged < 20 years is a new observation; this may be a normal physiological response in youth.

Detection of an extended human volatome with comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry.

Background Comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GCxGC-TOF MS) has been proposed as a powerful new tool for multidimensional analysis of complex chemical mixtures. We investigated GCxGC-TOF MS as a new method for identifying volatile organic compounds (VOCs) in normal human breath. Methods Samples of alveolar breath VOCs and ambient room air VOC were collected with a breath collection apparatus (BCA) onto separate sorbent traps from 34 normal healthy volunteers (mean age = 40 yr, SD = 17 yr, male/female = 19/15). VOCs were separated on two serial capillary columns separated by a cryogenic modulator, and detected with TOF MS. The first and second dimension columns were non-polar and polar respectively. Results BCA collection combined with GC×GC-TOF MS analysis identified approximately 2000 different VOCs in samples of human breath, many of which have not been previously reported. The 50 VOCs with the highest alveolar gradients (abundance in breath minus abundance in ambient room air) mostly comprised benzene derivatives, acetone, methylated derivatives of alkanes, and isoprene. Conclusions Collection and analysis of breath VOCs with the BCA-GC×GC-TOF MS system extended the size of the detectable human volatile metabolome, the volatome, by an order of magnitude compared to previous reports employing one-dimensional GC-MS. The size of the human volatome has been under-estimated in the past due to coelution of VOCs in one-dimensional GC analytical systems.

Effect of age on the profile of alkanes in normal human breath

Ethane and pentane in breath are markers of oxidative stress, produced by ROS-mediated lipid peroxidation of n-3 and n-6 polyunsaturated fatty acids (PUFAs), but little is known about other n-alkanes in normal human breath. We investigated the spectrum of alkanes in normal human alveolar breath, and their variation with age. Fifty normal humans were studied (age range 23–75, median 35). Volatile organic compounds (VOCs) in alveolar breath were captured on sorbent traps and assayed by gas chromatography and mass spectroscopy. Alveolar gradients (concentration in breath minus concentration in ambient room air) of alkanes were determined. C4–C20 alkanes were observed in breath and room air. Their mean alveolar gradients were negative from C4 to C12 and positive from C13 to C20. The mean alveolar gradients of four alkanes (C5–C8) were significantly less negative in the older subjects (p < 0.05). There were no significant differences between males and females. Normal human breath contained a spectrum of alkanes which may include new markers of oxidative stress. The mean rate of clearance (via cytochrome p450) exceeded the mean rate of synthesis (by ROS-mediated oxidative stress) for C4–C12 alkanes, while synthesis was greater than clearance for C13–C20 alkanes. The elevated alkane profile in older subjects was consistent with an age-related increase in oxidative stress, though an age-related decline in alkane clearance rate may have contributed.