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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...

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Autores principales: Phillips, Michael, Bauer, Thomas L., Cataneo, Renee N., Lebauer, Cassie, Mundada, Mayur, Pass, Harvey I., Ramakrishna, Naren, Rom, William N., Vallières, Eric
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4689411/
https://www.ncbi.nlm.nih.gov/pubmed/26698306
http://dx.doi.org/10.1371/journal.pone.0142484
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author Phillips, Michael
Bauer, Thomas L.
Cataneo, Renee N.
Lebauer, Cassie
Mundada, Mayur
Pass, Harvey I.
Ramakrishna, Naren
Rom, William N.
Vallières, Eric
author_facet Phillips, Michael
Bauer, Thomas L.
Cataneo, Renee N.
Lebauer, Cassie
Mundada, Mayur
Pass, Harvey I.
Ramakrishna, Naren
Rom, William N.
Vallières, Eric
author_sort Phillips, Michael
collection PubMed
description 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
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spelling pubmed-46894112015-12-31 Blinded Validation of Breath Biomarkers of Lung Cancer, a Potential Ancillary to Chest CT Screening Phillips, Michael Bauer, Thomas L. Cataneo, Renee N. Lebauer, Cassie Mundada, Mayur Pass, Harvey I. Ramakrishna, Naren Rom, William N. Vallières, Eric PLoS One Research Article 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 Public Library of Science 2015-12-23 /pmc/articles/PMC4689411/ /pubmed/26698306 http://dx.doi.org/10.1371/journal.pone.0142484 Text en © 2015 Phillips et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Phillips, Michael
Bauer, Thomas L.
Cataneo, Renee N.
Lebauer, Cassie
Mundada, Mayur
Pass, Harvey I.
Ramakrishna, Naren
Rom, William N.
Vallières, Eric
Blinded Validation of Breath Biomarkers of Lung Cancer, a Potential Ancillary to Chest CT Screening
title Blinded Validation of Breath Biomarkers of Lung Cancer, a Potential Ancillary to Chest CT Screening
title_full Blinded Validation of Breath Biomarkers of Lung Cancer, a Potential Ancillary to Chest CT Screening
title_fullStr Blinded Validation of Breath Biomarkers of Lung Cancer, a Potential Ancillary to Chest CT Screening
title_full_unstemmed Blinded Validation of Breath Biomarkers of Lung Cancer, a Potential Ancillary to Chest CT Screening
title_short Blinded Validation of Breath Biomarkers of Lung Cancer, a Potential Ancillary to Chest CT Screening
title_sort blinded validation of breath biomarkers of lung cancer, a potential ancillary to chest ct screening
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4689411/
https://www.ncbi.nlm.nih.gov/pubmed/26698306
http://dx.doi.org/10.1371/journal.pone.0142484
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