Identification of lung cancer breath biomarkers based on perioperative breathomics testing: A prospective observational study. (May 2022)

• Record Type: Journal Article
• Authors: Wang, Peiyu; Huang, Qi; Meng, Shushi; Mu, Teng; Liu, Zheng; He, Mengqi; Li, Qingyun; Zhao, Song; Wang, Shaodong; Qiu, Mantang
• Is Part Of: EClinicalMedicine. Volume 47(2022)
• Publication Date: 2022-05
• Subjects: Lung cancer -- Breathomics -- Volatile organic compounds -- Diagnosis; Medicine -- Research -- Periodicals; Medical policy -- Periodicals; Clinical Medicine; Health Policy; Public Health; Medical policy; Medicine -- Research; Periodical; Electronic journals; Periodicals; 613
• DOI: 10.1016/j.eclinm.2022.101384
• Languages: English
• ISSNs: 2589-5370

Abstract

Summary: 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).