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

Projected outcome of chest CT combined with breath testing.

<p>These predictions employ values reported in the National Lung Screening Trial for lung cancer prevalence (1.1%) and screening chest CT (sensitivity 93.8%, specificity 73.4%) [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0142484#pone.0142484.ref018" target="_blank">18</a>]. <b>Effect of combining two tests (top left panel).</b> TP = true positives, FN = false negatives, TN = true negatives, FP = false positives. The equations demonstrate the effects on sensitivity and specificity when two tests A and B are combined. If the diagnostic criterion is a positive test result for both test A and test B, then sensitivity decreases and specificity increases, compared to either test employed alone. If the diagnostic criterion is a positive test result for either test A or test B, then sensitivity increases and specificity decreases, compared to either test employed alone. The figure demonstrates the expected outcome of lung cancer screening in one million high-risk people (smokers or former smokers aged 50 yr or older). The main limiting factor in population screening programs is the potentially overwhelming number of false-positive test results. Screening one million people with chest CT alone would result in 263,074 false positive test results, but if chest CT and breath testing are both positive, the increased specificity would reduce this number to 88,919 i.e. by 66.2%. If only one of the tests is positive, then the increased sensitivity would reduce the number of false-negatives from 682 to 198 i.e. by 71.0%. <b>Effect of parallel and series testing on sensitivity and specificity (top right panel).</b> This figure displays the expected improvement in sensitivity and specificity of chest CT for lung cancer if it is combined in parallel with a breath testing. If both tests are positive for lung cancer, then specificity increases from 73.4% to 91.49%. If either test is positive, then sensitivity increases from 93.8% to 98.15%. If the two tests are employed in series and the breath test is negative, there may be no need to proceed to chest CT because 98.15% sensitivity is greater than the sensitivity of either test employed alone. <b>Positive predictive value</b> (<b>PPV) of chest CT combined with breath testing (bottom left panel).</b> This figure displays the expected improvement in PPV of chest CT for lung cancer if combined in parallel with a breath test. Employed alone, the PPV of chest CT is 3.77%. If breath testing is employed in parallel with chest CT and both tests are positive, then the PPV increases to 7.91% i.e. it increases by a factor of 2.1. The improvement is due to the higher specificity of the combined test and the consequent reduction in false positive results. The PPV of a test depends upon the prevalence (prev) of a disease, and is computed as PPV = (sen X prev)/[(sen X prev + (1-spec) X (1-prev)]. The PPV of chest CT for lung cancer is 3.77% [i.e. 0.938 X 011/(0.938 X.011+(1–0.734 X (1–0.011)) = 0.0377]. <b>Negative predictive value (NPV) of chest CT combined with breath testing (bottom right panel).</b> If the two tests are employed in series, a negative breath test result rules out lung cancer with NPV 99.6%, which is greater than the NPV of either test employed alone. Despite the increased sensitivity of the combined test, only a modest increment in NPV is possible because the pre-test NPV based on prevalence of lung cancer is 98.9%.</p

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

Overview of study design.

<p>Overview of study design.</p

Antiproliferative effect of WA on human MPM cells.

<p>Cells were treated with vehicle (Control, denoted as 0) or indicated doses of WA for 72 h. Determination of viable/live cells was carried out by MTT assay. The data in the histograms represent means of three independent experiments; bars, S.E.</p

CFM-4 elevates expression and serine phosphorylation of podoplanin.

<p>(A) Indicated MPM cells were either untreated (Control), treated with Cisplatin, or respective CFMs as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089146#pone-0089146-g002" target="_blank">figure 2A</a>. Staining of the cells was performed using anti-podoplanin D2-40 antibody as detailed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089146#s2" target="_blank">Methods</a>. Presence of increased podoplanin is indicated by intense brown staining in the cytosol of the CFM-1, CFM-5, and CFM-4-treated cells. MPM cells were either untreated (Control) or treated with indicated agents for noted time and dose, and levels of podoplanin, vimentin, actin and α-tubulin proteins (B, C) were determined by Western blotting essentially as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089146#pone-0089146-g002" target="_blank">figure 2</a>. (D) H2373 cells were either untreated (Control) or treated with agents as indicated. The cell lysates (1 mg of protein) were first subjected to immunoprecipitation using anti-phospho-serine, anti-Ubiquitin (lys48-specific), or anti-Ubiquitin (lys63-specific) antibodies as in methods. The membranes with cell lysates (50 µg/lane; blot on the left) or the immunoprecipitates (blot on the right) were probed with anti-podoplanin D2-40 antibody as in panel C.</p

CFMs reduce viabilities of the human MPM cells.

<p>Cells were treated with vehicle (Untreated Control), indicated doses of Cisplatin, various CFMs, or a combination of Cisplatin and CFMs for 24 h (A) or 48 h (B). Determination of viable/live cells was carried out by MTT assay. The data in the histograms represent means of three independent experiments; bars, S.E. * and #, p = <0.05 relative to Untreated Control (A). Note that the Y-axis scale is different in panel B.</p

CFMs activate pro-apoptotic SAPKs in MPM cells.

<p>(A, B) Indicated MPM cells were either untreated (Control), treated with Adriamycin, Cisplatin, or respective CFMs as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089146#pone-0089146-g002" target="_blank">figure 2A</a>. Staining of the cells was performed using anti-phospho-p38 antibody as detailed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089146#s2" target="_blank">Methods</a>. Presence of p38 is indicated by intense brown staining in the nuclei and cytosol of the treated cells. MPM cells were either untreated (Control) or treated with indicated agents for noted time and dose, and levels of phosphorylated p38 (noted as p-p38), and total p38 proteins (B, C) or phosphorylated JNK (noted as p-JNK1/2), and total JNK proteins (D, E) were determined by Western blotting essentially as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0089146#pone-0089146-g002" target="_blank">figure 2</a>.</p

Withaferin A Inhibits the Proteasome Activity in Mesothelioma <em>In Vitro</em> and <em>In Vivo</em>

<div><p>The medicinal plant <em>Withania somnifera</em> has been used for over centuries in Indian Ayurvedic Medicine to treat a wide spectrum of disorders. Withaferin A (WA), a bioactive compound that is isolated from this plant, has anti-inflammatory, immuno-modulatory, anti-angiogenic, and anti-cancer properties. Here we investigated malignant pleural mesothelioma (MPM) suppressive effects of WA and the molecular mechanisms involved. WA inhibited growth of the murine as well as patient-derived MPM cells in part by decreasing the chymotryptic activity of the proteasome that resulted in increased levels of ubiquitinated proteins and pro-apoptotic proteasome target proteins (p21, Bax, IκBα). WA suppression of MPM growth also involved elevated apoptosis as evidenced by activation of pro-apoptotic p38 stress activated protein kinase (SAPK) and caspase-3, elevated levels of pro-apoptotic Bax protein and cleavage of poly-(ADP-ribose)-polymerase (PARP). Our studies including gene-array based analyses further revealed that WA suppressed a number of cell growth and metastasis-promoting genes including c-myc. WA treatments also stimulated expression of the cell cycle and apoptosis regulatory protein (CARP)-1/CCAR1, a novel transducer of cell growth signaling. Knock-down of CARP-1, on the other hand, interfered with MPM growth inhibitory effects of WA. Intra-peritoneal administration of 5 mg/kg WA daily inhibited growth of murine MPM cell-derived tumors <em>in vivo</em> in part by inhibiting proteasome activity and stimulating apoptosis. Together our <em>in vitro</em> and <em>in vivo</em> studies suggest that WA suppresses MPM growth by targeting multiple pathways that include blockage of proteasome activity and stimulation of apoptosis, and thus holds promise as an anti-MPM agent.</p> </div