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

Michael Phillips,Cassie Lebauer,Thomas L Bauer,Renee N Cataneo,Naren Ramakrishna,Mayur Mundada,William N Rom,Harvey I Pass,Eric Vallières,Konstantinos Kostikas

doi:10.1371/journal.pone.0142484

Abstract

BackgroundBreath 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.ConclusionsBreath 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 RegistrationClinicalTrials.gov NCT00639067

Highlights

The modern era of breath testing dawned in 1971, when Linus Pauling first reported that normal human breath contains large numbers of volatile organic compounds (VOCs) in low concentrations [1]
Model-building phase: 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
Outcome modeling: We modeled the expected effects of combining breath biomarkers with chest CT on the sensitivity and specificity of lung cancer screening

Summary

Introduction

The modern era of breath testing dawned in 1971, when Linus Pauling first reported that normal human breath contains large numbers of volatile organic compounds (VOCs) in low concentrations [1]. The resulting flood of information has necessitated use of data management tools for metabolomic analysis that were originally developed for genomics and proteomics This has been accompanied by an increased risk of false discovery of biomarkers that can arise when a multivariate model over-fits large number of candidate breath VOCs to a small number of test subjects, a pitfall that has been termed “voodoo correlations”, or “seeing faces in the clouds” [15]. 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 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

Results

Conclusion