Identification of lung cancer breath biomarkers based on perioperative breathomics testing: A prospective observational study

Peiyu Wang, Qi Huang, Shushi Meng, Teng Mu, Zheng Liu, Mengqi He, Qingyun Li, Song Zhao, Shaodong Wang, Mantang Qiu, Peiyu Wang, Qi Huang, Shushi Meng, Teng Mu, Zheng Liu, Mengqi He, Qingyun Li, Song Zhao, Shaodong Wang, Mantang Qiu

Abstract

Background: Breathomics testing has been considered a promising method for detection and screening for lung cancer. This study aimed to identify breath biomarkers of lung cancer through perioperative dynamic breathomics testing.

Methods: The discovery study was prospectively conducted between Sept 1, 2020 and Dec 31, 2020 in Peking University People's Hospital in China. High-pressure photon ionisation time-of-flight mass spectrometry was used for breathomics testing before surgery and 4 weeks after surgery. 28 volatile organic compounds (VOCs) were selected as candidates based on a literature review. VOCs that changed significantly postoperatively in patients with lung cancer were selected as potential breath biomarkers. An external validation was conducted to evaluate the performance of these VOCs for lung cancer diagnosis. Multivariable logistic regression was used to establish diagnostic models based on selected VOCs.

Findings: In the discovery study of 84 patients with lung cancer, perioperative breathomics demonstrated 16 VOCs as lung cancer breath biomarkers. They were classified as aldehydes, hydrocarbons, ketones, carboxylic acids, and furan. In the external validation study including 157 patients with lung cancer and 368 healthy individuals, patients with lung cancer showed elevated spectrum peak intensity of the 16 VOCs after adjusting for age, sex, smoking, and comorbidities. The diagnostic model including 16 VOCs achieved an area under the curve (AUC) of 0.952, sensitivity of 89.2%, specificity of 89.1%, and accuracy of 89.1% in lung cancer diagnosis. The diagnostic model including the top eight VOCs achieved an AUC of 0.931, sensitivity of 86.0%, specificity of 87.2%, and accuracy of 86.9%.

Interpretation: Perioperative dynamic breathomics is an effective approach for identifying lung cancer breath biomarkers. 16 lung cancer-related breath VOCs (aldehydes, hydrocarbons, ketones, carboxylic acids, and furan) were identified and validated. Further studies are warranted to investigate the underlying mechanisms of identified VOCs.

Funding: National Natural Science Foundation of China (82173386) and Peking University People's Hospital Scientific Research Development Founds (RDH2021-07).

Keywords: Breathomics; Diagnosis; Lung cancer; Volatile organic compounds.

Conflict of interest statement

We declare no competing interests.

© 2022 The Authors.

Figures

Figure 1
Figure 1
Flowchart of participant recruitment. The detection study (A) and validation study (B) are shown. LDCT: low-dose chest computed tomography.
Figure 2
Figure 2
Examples of mass spectrums. A: The mass spectrums of a patient before surgery (left) and four weeks after surgery (right). B: Identification of 16 VOCs in mass spectrum before surgery. Patient characteristics: female, 52 years, stage IA3.
Figure 3
Figure 3
Perioperative dynamic changes of 16 volatile organic compounds in exhaled breath from patients with lung cancer. P values representing the difference in peak intensities before surgery and 4 weeks after surgery are from Wilcoxon matched-pairs signed-rank sum tests. PO: postoperative.
Figure 4
Figure 4
Comparisons of spectrum peak intensity of volatile organic compounds in patients with lung cancer and healthy individuals. P values are from Mann–Whitney U tests. HI: healthy individual; LC: lung cancer.
Figure 5
Figure 5
Panels of investigations of breath volatile organic compounds (VOCs) and lung cancers. A: The volcano plot showing the fold changes and difference in breath VOC peak intensity between patients with lung cancer and healthy individuals. B,C: Correlation analysis of sixteen VOCs in healthy individuals and patients with lung cancer. D: The performance of sixteen VOCs in diagnosing patients with lung cancer from healthy individuals. E: The performance of the combined sixteen VOCs in diagnosing lung cancer. F: The performance of the combined top eight VOCs in diagnosing lung cancer. AUC: area under the curve; CI: confidence interval; NPV: negative predictive value; PPV: positive predictive value; ROC: receiver operating characteristic curve.

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