Breath carbonyl compounds as biomarkers of lung cancer

Mingxiao Li, Dake Yang, Guy Brock, Ralph J. Knipp, Michael Bousamra, Michael H. Nantz, Xiao An Fu

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

Objective: Lung cancer dysregulations impart oxidative stress which results in important metabolic products in the form of volatile organic compounds (VOCs) in exhaled breath. The objective of this work is to use statistical classification models to determine specific carbonyl VOCs in exhaled breath as biomarkers for detection of lung cancer. Materials and methods: Exhaled breath samples from 85 patients with untreated lung cancer, 34 patients with benign pulmonary nodules and 85 healthy controls were collected. Carbonyl compounds in exhaled breath were captured by silicon microreactors and analyzed by Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS). The concentrations of carbonyl compounds were analyzed using a variety of statistical classification models to determine which compounds best differentiated between the patient sub-populations. Predictive accuracy of each of the models was assessed on a separate test data set. Results: Six carbonyl compounds (C<inf>4</inf>H<inf>8</inf>O, C<inf>5</inf>H<inf>10</inf>O, C<inf>2</inf>H<inf>4</inf>O<inf>2</inf>, C<inf>4</inf>H<inf>8</inf>O<inf>2</inf>, C<inf>6</inf>H<inf>10</inf>O<inf>2</inf>, C<inf>9</inf>H<inf>16</inf>O<inf>2</inf>) had significantly elevated concentrations in lung cancer patients vs. controls. A model based on counting the number of elevated compounds out of these six achieved an overall classification accuracy on the test data of 97% (95% CI 92%-100%), 95% (95% CI 88%-100%), and 89% (95% CI 79%-99%) for classifying lung cancer patients vs. non-smokers, current smokers, and patients with benign nodules, respectively. These results were comparable to benchmarking based on established statistical and machine-learning methods. The sensitivity in each case was 96% or higher, with specificity ranging from 64% for benign nodule patients to 86% for smokers and 100% for non-smokers. Conclusion: A model based on elevated levels of the six carbonyl VOCs effectively discriminates lung cancer patients from healthy controls as well as patients with benign pulmonary nodules.

Original languageEnglish (US)
Pages (from-to)92-97
Number of pages6
JournalLung Cancer
Volume90
Issue number1
DOIs
StatePublished - Oct 1 2015
Externally publishedYes

Fingerprint

Lung Neoplasms
Biomarkers
Volatile Organic Compounds
Statistical Models
Cyclotrons
Benchmarking
Lung
Silicon
Fourier Analysis
Mass Spectrometry
Oxidative Stress
Ions
Population

Keywords

  • Biomarker
  • Carbonyl compound
  • Exhaled breath
  • Lung cancer
  • Statistical model

ASJC Scopus subject areas

  • Oncology
  • Pulmonary and Respiratory Medicine
  • Cancer Research

Cite this

Li, M., Yang, D., Brock, G., Knipp, R. J., Bousamra, M., Nantz, M. H., & Fu, X. A. (2015). Breath carbonyl compounds as biomarkers of lung cancer. Lung Cancer, 90(1), 92-97. https://doi.org/10.1016/j.lungcan.2015.07.005

Breath carbonyl compounds as biomarkers of lung cancer. / Li, Mingxiao; Yang, Dake; Brock, Guy; Knipp, Ralph J.; Bousamra, Michael; Nantz, Michael H.; Fu, Xiao An.

In: Lung Cancer, Vol. 90, No. 1, 01.10.2015, p. 92-97.

Research output: Contribution to journalArticle

Li, M, Yang, D, Brock, G, Knipp, RJ, Bousamra, M, Nantz, MH & Fu, XA 2015, 'Breath carbonyl compounds as biomarkers of lung cancer', Lung Cancer, vol. 90, no. 1, pp. 92-97. https://doi.org/10.1016/j.lungcan.2015.07.005
Li M, Yang D, Brock G, Knipp RJ, Bousamra M, Nantz MH et al. Breath carbonyl compounds as biomarkers of lung cancer. Lung Cancer. 2015 Oct 1;90(1):92-97. https://doi.org/10.1016/j.lungcan.2015.07.005
Li, Mingxiao ; Yang, Dake ; Brock, Guy ; Knipp, Ralph J. ; Bousamra, Michael ; Nantz, Michael H. ; Fu, Xiao An. / Breath carbonyl compounds as biomarkers of lung cancer. In: Lung Cancer. 2015 ; Vol. 90, No. 1. pp. 92-97.
@article{07817e6b84994fd9be0f18ffb4bcc6b9,
title = "Breath carbonyl compounds as biomarkers of lung cancer",
abstract = "Objective: Lung cancer dysregulations impart oxidative stress which results in important metabolic products in the form of volatile organic compounds (VOCs) in exhaled breath. The objective of this work is to use statistical classification models to determine specific carbonyl VOCs in exhaled breath as biomarkers for detection of lung cancer. Materials and methods: Exhaled breath samples from 85 patients with untreated lung cancer, 34 patients with benign pulmonary nodules and 85 healthy controls were collected. Carbonyl compounds in exhaled breath were captured by silicon microreactors and analyzed by Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS). The concentrations of carbonyl compounds were analyzed using a variety of statistical classification models to determine which compounds best differentiated between the patient sub-populations. Predictive accuracy of each of the models was assessed on a separate test data set. Results: Six carbonyl compounds (C4H8O, C5H10O, C2H4O2, C4H8O2, C6H10O2, C9H16O2) had significantly elevated concentrations in lung cancer patients vs. controls. A model based on counting the number of elevated compounds out of these six achieved an overall classification accuracy on the test data of 97{\%} (95{\%} CI 92{\%}-100{\%}), 95{\%} (95{\%} CI 88{\%}-100{\%}), and 89{\%} (95{\%} CI 79{\%}-99{\%}) for classifying lung cancer patients vs. non-smokers, current smokers, and patients with benign nodules, respectively. These results were comparable to benchmarking based on established statistical and machine-learning methods. The sensitivity in each case was 96{\%} or higher, with specificity ranging from 64{\%} for benign nodule patients to 86{\%} for smokers and 100{\%} for non-smokers. Conclusion: A model based on elevated levels of the six carbonyl VOCs effectively discriminates lung cancer patients from healthy controls as well as patients with benign pulmonary nodules.",
keywords = "Biomarker, Carbonyl compound, Exhaled breath, Lung cancer, Statistical model",
author = "Mingxiao Li and Dake Yang and Guy Brock and Knipp, {Ralph J.} and Michael Bousamra and Nantz, {Michael H.} and Fu, {Xiao An}",
year = "2015",
month = "10",
day = "1",
doi = "10.1016/j.lungcan.2015.07.005",
language = "English (US)",
volume = "90",
pages = "92--97",
journal = "Lung Cancer",
issn = "0169-5002",
publisher = "Elsevier Ireland Ltd",
number = "1",

}

TY - JOUR

T1 - Breath carbonyl compounds as biomarkers of lung cancer

AU - Li, Mingxiao

AU - Yang, Dake

AU - Brock, Guy

AU - Knipp, Ralph J.

AU - Bousamra, Michael

AU - Nantz, Michael H.

AU - Fu, Xiao An

PY - 2015/10/1

Y1 - 2015/10/1

N2 - Objective: Lung cancer dysregulations impart oxidative stress which results in important metabolic products in the form of volatile organic compounds (VOCs) in exhaled breath. The objective of this work is to use statistical classification models to determine specific carbonyl VOCs in exhaled breath as biomarkers for detection of lung cancer. Materials and methods: Exhaled breath samples from 85 patients with untreated lung cancer, 34 patients with benign pulmonary nodules and 85 healthy controls were collected. Carbonyl compounds in exhaled breath were captured by silicon microreactors and analyzed by Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS). The concentrations of carbonyl compounds were analyzed using a variety of statistical classification models to determine which compounds best differentiated between the patient sub-populations. Predictive accuracy of each of the models was assessed on a separate test data set. Results: Six carbonyl compounds (C4H8O, C5H10O, C2H4O2, C4H8O2, C6H10O2, C9H16O2) had significantly elevated concentrations in lung cancer patients vs. controls. A model based on counting the number of elevated compounds out of these six achieved an overall classification accuracy on the test data of 97% (95% CI 92%-100%), 95% (95% CI 88%-100%), and 89% (95% CI 79%-99%) for classifying lung cancer patients vs. non-smokers, current smokers, and patients with benign nodules, respectively. These results were comparable to benchmarking based on established statistical and machine-learning methods. The sensitivity in each case was 96% or higher, with specificity ranging from 64% for benign nodule patients to 86% for smokers and 100% for non-smokers. Conclusion: A model based on elevated levels of the six carbonyl VOCs effectively discriminates lung cancer patients from healthy controls as well as patients with benign pulmonary nodules.

AB - Objective: Lung cancer dysregulations impart oxidative stress which results in important metabolic products in the form of volatile organic compounds (VOCs) in exhaled breath. The objective of this work is to use statistical classification models to determine specific carbonyl VOCs in exhaled breath as biomarkers for detection of lung cancer. Materials and methods: Exhaled breath samples from 85 patients with untreated lung cancer, 34 patients with benign pulmonary nodules and 85 healthy controls were collected. Carbonyl compounds in exhaled breath were captured by silicon microreactors and analyzed by Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS). The concentrations of carbonyl compounds were analyzed using a variety of statistical classification models to determine which compounds best differentiated between the patient sub-populations. Predictive accuracy of each of the models was assessed on a separate test data set. Results: Six carbonyl compounds (C4H8O, C5H10O, C2H4O2, C4H8O2, C6H10O2, C9H16O2) had significantly elevated concentrations in lung cancer patients vs. controls. A model based on counting the number of elevated compounds out of these six achieved an overall classification accuracy on the test data of 97% (95% CI 92%-100%), 95% (95% CI 88%-100%), and 89% (95% CI 79%-99%) for classifying lung cancer patients vs. non-smokers, current smokers, and patients with benign nodules, respectively. These results were comparable to benchmarking based on established statistical and machine-learning methods. The sensitivity in each case was 96% or higher, with specificity ranging from 64% for benign nodule patients to 86% for smokers and 100% for non-smokers. Conclusion: A model based on elevated levels of the six carbonyl VOCs effectively discriminates lung cancer patients from healthy controls as well as patients with benign pulmonary nodules.

KW - Biomarker

KW - Carbonyl compound

KW - Exhaled breath

KW - Lung cancer

KW - Statistical model

UR - http://www.scopus.com/inward/record.url?scp=84940957058&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84940957058&partnerID=8YFLogxK

U2 - 10.1016/j.lungcan.2015.07.005

DO - 10.1016/j.lungcan.2015.07.005

M3 - Article

VL - 90

SP - 92

EP - 97

JO - Lung Cancer

JF - Lung Cancer

SN - 0169-5002

IS - 1

ER -