Breath Analysis for Early Detection of Malignant Pleural Mesothelioma: Volatile Organic Compounds (VOCs) Determination and Possible Biochemical Pathways

Our plain-English summary of Breath Analysis for Early Detection of Malignant Pleural Mesothelioma: Volatile Organic Compounds (VOCs) Determination and Possible Biochemical Pathways, by A. Di Gilio, A. Catino, A. Lombardi, et al (Cancers, 2020).

The study

• Authors: A. Di Gilio, A. Catino, A. Lombardi, J. Palmisani, L. Facchini, T. Mongelli, N. Varesano, R. Bellotti, D. Galetta, G. de Gennaro, S. Tangaro
• Journal: Cancers
• Published: 2020;12(5)
• Cited by: 35 articles, as counted by NIH iCite
• Read it: PubMed · Publisher · Free full text

Abstract

Malignant pleural mesothelioma (MPM) is a rare neoplasm, mainly caused by asbestos exposure, with a high mortality rate. The management of patients with MPM is controversial due to a long latency period between exposure and diagnosis and because of non-specific symptoms generally appearing at advanced stage of the disease. Breath analysis, aimed at the identification of diagnostic Volatile Organic Compounds (VOCs) pattern in exhaled breath, is believed to improve early detection of MPM. Therefore, in this study, breath samples from 14 MPM patients and 20 healthy controls (HC) were collected and analyzed by Thermal Desorption-Gas Chromatography-Mass Spectrometry (TD-GC/MS). Nonparametric test allowed to identify the most weighting variables to discriminate between MPM and HC breath samples and multivariate statistics were applied. Considering that MPM is an aggressive neoplasm leading to a late diagnosis and thus the recruitment of patients is very difficult, a promising data mining approach was developed and validated in order to discriminate between MPM patients and healthy controls, even if no large population data are available. Three different machine learning algorithms were applied to perform the classification task with a leave-one-out cross-validation approach, leading to remarkable results (Area Under Curve AUC = 93%). Ten VOCs, such as ketones, alkanes and methylate derivates, as well as hydrocarbons, were able to discriminate between MPM patients and healthy controls and for each compound which resulted diagnostic for MPM, the metabolic pathway was studied in order to identify the link between VOC and the neoplasm. Moreover, five breath samples from asymptomatic asbestos-exposed persons (AEx) were exploratively analyzed, processed and tested by the validated statistical method as blinded samples in order to evaluate the performance for the early recognition of patients affected by MPM among asbestos-exposed persons. Good agreement was found between the information obtained by gold-standard diagnostic methods such as computed tomography CT and model output.

In plain English

This study in Bari, Italy, tested whether chemicals in exhaled breath could tell people with malignant pleural mesothelioma (MPM) apart from healthy people. The authors collected breath from 14 MPM patients, 20 healthy controls and 5 former asbestos-exposed people without symptoms. They identified 103 volatile organic compounds (VOCs) by gas chromatography-mass spectrometry. The article does not describe the asbestos exposure (fiber type, job or amount). Three classification methods were compared. The best, Random Forest, separated MPM patients from controls with an area under the curve of 0.93 (Naive Bayes 0.80, SVM 0.83) using ten compounds, including acetophenone, α-pinene, benzene and toluene. The five asbestos-exposed people were tested blind. One was classified as a pathological case with a probability score of 0.99 and proved to have MPM on CT scan. Two classified as controls proved healthy on CT. Two were classified as pathological, which the authors said was probably due to pleural plaques. The authors concluded that breath analysis is promising for MPM screening. They noted the small sample, groups not matched for age, and possible influence from outside VOCs.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: Cross-sectional case-control study using breath analysis and machine learning (leave-one-out cross-validation), plus an exploratory blinded test on five asbestos-exposed subjects
• Who was studied: 39 adults recruited from the Thoracic Oncology Unit, IRCCS Istituto Tumori Giovanni Paolo II, Bari, Italy (ethics approval 14 January 2019): 14 MPM patients before starting treatment (mean age 73.6), 20 healthy controls (mean age 53.6) and 5 asymptomatic former asbestos-exposed individuals (mean age 63.5)
• Exposure: VOCs in exhaled breath, measured by TD-GC/MS; 103 VOCs identified. Asbestos exposure was recorded on questionnaires, but no fiber types, jobs or doses are reported
• Main results:
  • Area under the ROC curve for discriminating MPM patients from healthy controls: Naive Bayes 0.80, SVM 0.83, Random Forest 0.93
  • The first 10 compounds ranked by the Random Forest model reached the maximum performance
  • The ten compounds were acetophenone, α-pinene, 1-hexonol-2-ethyl, p-benzoquinone, 2,2,4,6,6-pentamethyl-heptane, 1-propanol, benzonitrile, benzene, ethylbenzene and toluene
  • Among the five blinded asbestos-exposed subjects, one classified as pathological with a probability score of 0.99 proved to have MPM on CT scan
  • Two of the five asbestos-exposed subjects were classified as controls and proved healthy on CT scan
  • Two asbestos-exposed subjects were classified as pathological cases, which the authors attributed probably to pleural plaques seen on CT
• The authors' conclusion: The authors concluded that breath analysis is a promising technique for screening and early diagnosis of MPM, and that the study will be extended to an independent, large-scale, multicenter series
• Limitations the authors state:
  • The groups were not matched for age; MPM patients were older
  • External VOCs may have influenced the breath samples
  • Only five asbestos-exposed subjects were analyzed; a new model trained on more samples from an at-risk population is needed
  • The statistical approach was developed and validated on a small data sample

Topics

Medical terms in this study, explained in our Lexicon: Pleural Mesothelioma, Neoplasm, Asbestos Exposure, Latency.

Related studies

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• Assessing truck driver exposure at the World Trade Center disaster site: personal and area monitoring for particulate matter and volatile organic compounds during October 2001 and April 2002 — A.S. Geyh, S. Chillrud, et al. (2005)
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• Methylation profile landscape in mesothelioma: possible implications in early detection, disease progression, and therapeutic options — X. Zhang, N. Tang, et al. (2015)
• A task-based assessment of parental occupational exposure to organic solvents and other compounds and the risk of childhood leukemia in California — C. Metayer, G. Scelo, et al. (2016)
• Biochemical components of bronchoalveolar lavage in early experimental asbestosis of the sheep: phospholipase A2 activity, prostaglandin E2 and proteins — P. Sirois, G. Drapeau, et al. (1983)

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