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PLoS One

Title:		"Blinded Validation of Breath Biomarkers of Lung Cancer, a Potential Ancillary to Chest CT Screening"
Author(s):		Phillips M; Bauer TL; Cataneo RN; Lebauer C; Mundada M; Pass HI; Ramakrishna N; Rom WN; Vallieres E;
Address:		"Breath Research Laboratory, Menssana Research Inc, 211 Warren St, Newark, NJ, 07103, United States of America. Department of Medicine, New York Medical College, Valhalla, NY, United States of America. Christiana Care Health System, Newark, DE, United States of America. Schmitt & Associates, 211 Warren Street, Newark, NJ, 07103, United States of America. New York University Langone Medical Center, New York, NY, United States of America. University of Florida Health Cancer Center at Orlando Health, Orlando, FL, United States of America. Swedish Cancer Institute, 1101 Madison Suite 900, Seattle, WA, 98104, United States of America"
Journal Title:		PLoS One
Year:		2015
Volume:		20151223
Issue:		12
Page Number:		e0142484 -
DOI:		10.1371/journal.pone.0142484
ISSN/ISBN:		1932-6203 (Electronic) 1932-6203 (Linking)
Abstract:		"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"
Keywords:		"Aged Algorithms Biomarkers, Tumor/analysis *Breath Tests Cohort Studies Early Detection of Cancer/*methods Female Gas Chromatography-Mass Spectrometry Humans Lung Neoplasms/*diagnosis Male Middle Aged Monte Carlo Method Sensitivity and Specificity Volatil;"
Notes:		"MedlinePhillips, Michael Bauer, Thomas L Cataneo, Renee N Lebauer, Cassie Mundada, Mayur Pass, Harvey I Ramakrishna, Naren Rom, William N Vallieres, Eric eng R44 HL070411/HL/NHLBI NIH HHS/ 9R44HL070411-04A1/HL/NHLBI NIH HHS/ Research Support, N.I.H., Extramural Validation Study 2015/12/25 PLoS One. 2015 Dec 23; 10(12):e0142484. doi: 10.1371/journal.pone.0142484. eCollection 2015"