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

Blinded Validation of Breath Biomarkers of Lung Cancer, a Potential Ancillary to Chest CT Screening.

BACKGROUND:Breath volatile organic compounds (VOCs) have been reported as biomarkers of lung cancer, but it is not known if biomarkers identified in one group can identify disease in a separate independent cohort. Also, it is not known if combining breath biomarkers with chest CT has the potential to improve the sensitivity and specificity of lung cancer screening. METHODS:Model-building phase (unblinded): Breath VOCs were analyzed with gas chromatography mass spectrometry in 82 asymptomatic smokers having screening chest CT, 84 symptomatic high-risk subjects with a tissue diagnosis, 100 without a tissue diagnosis, and 35 healthy subjects. Multiple Monte Carlo simulations identified breath VOC mass ions with greater than random diagnostic accuracy for lung cancer, and these were combined in a multivariate predictive algorithm. Model-testing phase (blinded validation): We analyzed breath VOCs in an independent cohort of similar subjects (n = 70, 51, 75 and 19 respectively). The algorithm predicted discriminant function (DF) values in blinded replicate breath VOC samples analyzed independently at two laboratories (A and B). Outcome modeling: We modeled the expected effects of combining breath biomarkers with chest CT on the sensitivity and specificity of lung cancer screening. RESULTS:Unblinded model-building phase. The algorithm identified lung cancer with sensitivity 74.0%, specificity 70.7% and C-statistic 0.78. Blinded model-testing phase: The algorithm identified lung cancer at Laboratory A with sensitivity 68.0%, specificity 68.4%, C-statistic 0.71; and at Laboratory B with sensitivity 70.1%, specificity 68.0%, C-statistic 0.70, with linear correlation between replicates (r = 0.88). In a projected outcome model, breath biomarkers increased the sensitivity, specificity, and positive and negative predictive values of chest CT for lung cancer when the tests were combined in series or parallel. CONCLUSIONS:Breath VOC mass ion biomarkers identified lung cancer in a separate independent cohort, in a blinded replicated study. Combining breath biomarkers with chest CT could potentially improve the sensitivity and specificity of lung cancer screening. TRIAL REGISTRATION:ClinicalTrials.gov NCT00639067

Comparison of 1D and 2D chromatograms containing hexane.

<p>The bottom panel shows the 1D chromatogram of breath VOCs in a single subject (inset) with detail around the hexane peak at 7.23 minutes (peak a). The ion fragmentation spectrum of the peak is displayed on the right in red. The top panel shows six peaks (b-h) in the 2D chromatogram that coeluted with hexane (c) on the non-polar column but with different retention times on the polar column. These peaks were identified by Chroma-TOF and the NIST library as (b) 1,3-pentadiene (c) hexane, (d) dimethyl selenide, (e) 4H-pyrazole, 3-tert-butylsulfanyl-4,4-bistrifluoromethyl- (f) butanal, (g) methyl vinyl ketone and (h) 3,5-dihydroxybenzamide. The ion fragmentation spectrum of each peak is displayed on the right. All intensities in this figure are plotted on a logarithmic scale.</p

Chromatogram displaying analysis of breath VOCs in a typical normal human subject.

<div><p>The x-axis (horizontal) displays retention time (sec) on the non-polar primary column, and the z-axis (front to rear) displays retention time (sec) on the secondary polar column. The y-axis (vertical) represents the intensity of the peak and varies with the abundance of a VOC and the molecule specific (but not currently described) sensitivity of the method to each analyte.</p> <p><i>Panel 1: Zero rotation about x-axis</i>. In this view, the z-axis is not visible, and the chromatogram resembles a conventional 1D GC MS chromatogram displaying approximately 150-200 peaks.</p> <p><i>Panels 2 and 3: 30 and 60 degrees rotation about x-axis</i>. As the chromatogram rotates, peaks that appeared apparently single on the x-axis in Panel 1 are resolved into several subsidiary peaks on the z-axis.</p> <p><i>Panel 4: 90 degrees rotation about x-axis</i>. Each dot represents an individual VOC in the chromatogram. TOF-MS identified approximately 2,000 different VOC peaks in this chromatogram. This provides a more sensitive depiction of the chromatographic data because it displays VOCs whose peaks are too small to be visible in the other panels. Several different categories of chemical species were observed, including terpenes, alcohols, ketones, alkanes, alkenes, esters, aldehydes, furans, benzene derivatives, and sulfides. Contour plot displays of GC×GC peaks can potentially separate breath VOCs into “chemical islands”. For example, alkanes constitute the majority of the VOCs in the oval areas outlined in the figure. Groups of similar VOCs, differing by a methyl group for example, are resolved by this technique.</p></div

Rapid Point-Of-Care Breath Test for Biomarkers of Breast Cancer and Abnormal Mammograms

BACKGROUND: Previous studies have reported volatile organic compounds (VOCs) in breath as biomarkers of breast cancer and abnormal mammograms, apparently resulting from increased oxidative stress and cytochrome p450 induction. We evaluated a six-minute point-of-care breath test for VOC biomarkers in women screened for breast cancer at centers in the USA and the Netherlands. METHODS: 244 women had a screening mammogram (93/37 normal/abnormal) or a breast biopsy (cancer/no cancer 35/79). A mobile point-of-care system collected and concentrated breath and air VOCs for analysis with gas chromatography and surface acoustic wave detection. Chromatograms were segmented into a time series of alveolar gradients (breath minus room air). Segmental alveolar gradients were ranked as candidate biomarkers by C-statistic value (area under curve [AUC] of receiver operating characteristic [ROC] curve). Multivariate predictive algorithms were constructed employing significant biomarkers identified with multiple Monte Carlo simulations and cross validated with a leave-one-out (LOO) procedure. RESULTS: Performance of breath biomarker algorithms was determined in three groups: breast cancer on biopsy versus normal screening mammograms (81.8% sensitivity, 70.0% specificity, accuracy 79% (73% on LOO) [C-statistic value], negative predictive value 99.9%); normal versus abnormal screening mammograms (86.5% sensitivity, 66.7% specificity, accuracy 83%, 62% on LOO); and cancer versus no cancer on breast biopsy (75.8% sensitivity, 74.0% specificity, accuracy 78%, 67% on LOO). CONCLUSIONS: A pilot study of a six-minute point-of-care breath test for volatile biomarkers accurately identified women with breast cancer and with abnormal mammograms. Breath testing could potentially reduce the number of needless mammograms without loss of diagnostic sensitivity

Breath VOC sample analysis.

<p><b>Total ion chromatogram of breath VOCs (upper panel)</b> [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0142484#pone.0142484.ref003" target="_blank">3</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0142484#pone.0142484.ref024" target="_blank">24</a>]. VOCs are thermally desorbed from the sorbent trap, separated by gas chromatography, and injected into a mass sensitive detector where they are bombarded with energetic electrons in a vacuum and degraded into a set of ionic fragments, each with its own mass/charge (m/z) ratio. This figure displays the total ion current as a function of time, as a series of VOCs enter the detector sequentially. The total ion current from a peak containing toluene is marked, and the mass spectrum of the constituent mass ions is shown in the lower panel. A typical total ion chromatogram derived from a sample of human breath VOCs usually displays ~150 to 200 separate peaks. <b>Mass spectrum of ions in a chromatograph peak (lower panel).</b> The mass spectrum of ions derived from toluene (shown in the middle panel) comprises a characteristic pattern of fragments. Matching this pattern to a similar mass spectrum in a computer-based library enables identification of the chemical structure of the source VOC. In complex mixtures like breath, identification is usually tentative because biomarkers may be misidentified if co-eluting VOCs contaminate a mass spectrum, and if the spectral pattern matches inexactly with a library standard. However, individual mass ions from a VOC can be identified with confidence and provide robust biomarkers even when the identity of the parent VOC biomarker is uncertain.</p

Unblinded development of predictive algorithm.

<p><b>Monte Carlo statistical analysis of mass ions (top panel)</b> A list of more than 70,000 candidate mass ion biomarkers of lung cancer was obtained from a series of 5 sec segments in aligned chromatograms. The diagnostic accuracy of each mass ion was quantified by its C-statistic i.e. by the area under curve (AUC) of its associated receiver operating characteristic (ROC) curve (the “Correct assignment” curve). In order to exclude false biomarkers, the ‘‘Random assignment” curve employed multiple Monte Carlo simulations comprising 40 random assignments of diagnosis (“cancer” or “cancer-free”) to determine the random behavior of each candidate mass ion. The cutoff point in the “Correct assignment” curve was taken as the vertical intercept of the point where the number of mass ions in the ‘‘Random assignment” curve declined to zero (at C-statistic = 0.63). At this point, the vertical distance between the two curves indicated that 544 mass ions identified lung cancer with greater than random accuracy, and the separation between the curves exceeded 5 sigma. <b>Linear clustering of mass ion biomarkers (middle panel).</b> This figure displays vertical and horizontal linear clustering in a group of mass ion biomarkers of lung cancer with retention times between 1,500 and 2,500 sec. These mass ions were identified by Monte Carlo statistical analysis (upper panel) as having C-statistic values that were greater than random. M/z is the mass divided by the charge number of an ion, and the retention time indicates when a VOC eluted from the GC column and entered the MS detector where it was bombarded with electrons and converted to mass ion fragments. Vertical linear clusters indicate mass ions with similar retention times. These groupings are consistent with one or more breath VOCs entering the MS detector simultaneously, prior to breakdown to mass ions. This observation suggests that a comparatively small number of parent breath VOCs may account for several of the mass ion biomarkers of lung cancer. Horizontal linear clusters with m/z values of 43 and 57 are consistent with breakdown products of alkanes and methylated alkanes. <b>Receiver operating characteristic (ROC) curve (bottom panel).</b> The AUC of a ROC curve (or its C-statistic) indicates the overall accuracy of a test, and may vary from 0.5 (a straight line from bottom left to top right of the graph) to 1.0 (a right angle with its apex at the top left of the graph). A C-statistic of 0.5 indicates that the test performance was no better than random e.g. flipping a coin, while a C-statistic of 1.0 indicates a perfect test with 100% sensitivity and specificity. In clinical practice, a C-statistic of 0.78 is generally regarded as clinically useful.</p

Blinded prediction of lung cancer.

<p><b>Inter-laboratory concordance of discriminant functions (DF) in replicate samples (top panel).</b> DF values of chromatograms analyzed at laboratory A were plotted as a function of the DF value of the duplicate sample analyzed at laboratory B. There was a linear relationship between the two sets of DF values (r = 0.88, 95% confidence intervals shown). <b>Predicted sensitivity and specificity in subjects with biopsy-proven lung cancer and chest CT negative for lung cancer (middle panel).</b> The DF value derived from the predictive algorithm provides a variable cutoff point for the breath test. Test results greater than a DF value were scored as positive for lung cancer while those less than the DF were scored as negative. When DF = 0, the test has 100% sensitivity because all results are scored as positive for lung cancer, but zero specificity because no results are scored as negative. The sum of sensitivity plus specificity is maximal at the point where the two curves intersect, and was therefore selected as the optimal DF cutoff value for a binary test (i.e. cancer versus no cancer). In this graph (results from Laboratory A), the curves intersected at DF = 22, with sensitivity 68.0% and specificity 68.4%. <b>ROC curves (lower panel).</b> The ROC curves of the predicted outcomes of the breath test are shown for samples analyzed at laboratories A and B. The overall accuracy (C-statistic) of the lung cancer predictions was similar at both sites.</p