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Breath Analysis For Early Detection Of Malignant Pleural Mesothelioma: Volatile Organic Compounds (VOCs) Determination And Possible Biochemical Pathways

A. Di Gilio, A. Catino, A. Lombardi, J. Palmisani, L. Facchini, T. Mongelli, N. Varesano, R. Bellotti, D. Galetta, G. de Gennaro, S. Tangaro
Published 2020 · Medicine

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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.
This paper references
Breath analysis by gas chromatography-mass spectrometry and electronic nose to screen for pleural mesothelioma: a cross-sectional case-control study
K. Lamote (2017)
Clinical application of breath biomarkers of oxidative stress status.
T. H. Risby (1999)
An Introduction to Variable and Feature Selection
I. Guyon (2003)
Profiling Single Cancer Cells with Volatolomics Approach
Mamatha Serasanambati (2019)
Lipidomes of lung cancer and tumour-free lung tissues reveal distinct molecular signatures for cancer differentiation, age, inflammation, and pulmonary emphysema
Lars F Eggers (2017)
Incidence of malignant mesothelioma of the pleura in Québec and Canada from 1984 to 2007, and projections from 2008 to 2032.
Alfreda Krupoves (2015)
An electronic nose distinguishes exhaled breath of patients with Malignant Pleural Mesothelioma from controls.
S. Dragonieri (2012)
Release of volatile organic compounds from the lung cancer cell line NCI-H2087 in vitro.
A. Sponring (2009)
Classification and Regression by randomForest
A. Liaw (2007)
Incidence of cancer among Finnish patients with asbestos-related pulmonary or pleural fibrosis
A. Karjalainen (2004)
Emerging evidence that the ban on asbestos use is reducing the occurrence of pleural mesothelioma in Sweden
B. Järvholm (2015)
Detection and identification of sulfhydryl conjugates of rho-benzoquinone in microsomal incubations of benzene and phenol.
S. Lunte (1983)
Feature selection and analysis on correlated gas sensor data with recursive feature elimination
Ke Yan (2015)
A Random Forest based predictor for medical data classification using feature ranking
Md. Zahangir Alam (2019)
Assessment of the exhalation kinetics of volatile cancer biomarkers based on their physicochemical properties.
A. Amann (2014)
Analysis of workplace compliance measurements of asbestos by the U.S. Occupational Safety and Health Administration (1984-2011).
Dallas M. Cowan (2015)
Analysis of work place compliance measurement of asbestos by the U.S. Occupational Safety and Health Administration
D M Cowan (1984)
Understanding the Warburg Effect: The Metabolic Requirements of Cell Proliferation
M. V. Vander Heiden (2009)
Asbestos cointaining materials detection and classification by the use of hyperspectral inaging
G. Bonifazi (2018)
'Mitochondrial energy imbalance and lipid peroxidation cause cell death in Friedreich's ataxia'
R. Abeti (2016)
Diagnosis of lung cancer by the analysis of exhaled breath with a colorimetric sensor array
P. Mazzone (2007)
Incidence and survival trends for malignant pleural and peritoneal mesothelioma
M J Soeberg (1982)
Noninvasive detection of lung cancer by analysis of exhaled breath
A. Bajtarevic (2009)
Determination of volatile organic compounds in exhaled breath of patients with lung cancer using solid phase microextraction and gas chromatography mass spectrometry
M. Ligor (2009)
A breath test for malignant mesothelioma using an electronic nose
Elaine. Chapman (2011)
A computerized classification technique for screening for the presence of breath biomarkers in lung cancer.
H. O'neill (1988)
Non-occupational exposure to asbestos and risk of pleural mesothelioma: review and meta-analysis
G. Marsh (2017)
Exhaled Breath Analysis for Monitoring Response to Treatment in Advanced Lung Cancer
Inbar Nardi-Agmon (2016)
Microsomal metabolism of benzene to species irreversibly binding to microsomal protein and effects of modifications of this metabolism.
A. Tunek (1978)
Inductive and Bayesian learning in medical diagnosis
I. Kononenko (1993)
Pyruvate carboxylase is critical for non-small-cell lung cancer proliferation.
K. Sellers (2015)
A breath test for diagnosing malignant pleural mesothelioma
K. Lamote (2014)
Incidence and survival trends for malignant pleural and peritoneal mesothelioma, Australia, 1982–2009
M. Soeberg (2016)
What is a support vector machine?
William Stafford Noble (2006)
Random Forests
L. Breiman (2004)
Breath analysis in non small cell lung cancer patients after surgical tumour resection.
D. Poli (2008)
Fast analytical methodology based on mass spectrometry for the determination of volatile biomarkers in saliva.
M. Sánchez (2012)
On respiratory impairment in cancer cells.
S. Weinhouse (1956)
Diagnostic potential of breath analysis--focus on volatile organic compounds.
W. Miekisch (2004)
Free radicals, antioxidants, and human disease: where are we now?
B. Halliwell (1992)
Asbestos is not just asbestos: an unrecognised health hazard.
F. Baumann (2013)
Exhaled breath to screen for malignant pleural mesothelioma: a validation study
K. Lamote (2017)
Lactate Metabolism in Human Lung Tumors
B. Faubert (2017)
Critical Review of Volatile Organic Compound Analysis in Breath and In Vitro Cell Culture for Detection of Lung Cancer
Zhunan Jia (2019)
Biomarkers for early diagnosis of malignant mesothelioma: Do we need another moonshot?
Sabrina Lagniau (2017)
Altered regulation of metabolic pathways in human lung cancer discerned by 13C stable isotope-resolved metabolomics (SIRM)
T. W. Fan (2009)
Serum Biomarkers in Patients with Mesothelioma and Pleural Plaques and Healthy Subjects Exposed to Naturally Occurring Asbestos
Mehmet Bayram (2013)
How mitochondria produce reactive oxygen species
M. Murphy (2009)
Detection of lung, breast, colorectal, and prostate cancers from exhaled breath using a single array of nanosensors
G. Peng (2010)
Incidence of cancer among Finnish patients. Cancer Causes Control
A Karjalainen (1999)
K. Lamote (2014)
Detection of malignant pleural mesothelioma in exhaled breath by multicapillary column/ion mobility spectrometry (MCC/IMS).
K. Lamote (2016)
Alveolar gradient of pentane in normal human breath.
M. Phillips (1994)
Asbestos containing materials detection and classification by the use of hyperspectral imaging.
G. Bonifazi (2018)
Chemical characterization of exhaled breath to differentiate between patients with malignant plueral mesothelioma from subjects with similar professional asbestos exposure
G. Gennaro (2010)
Volatile organic compounds of lung cancer and possible biochemical pathways.
M. Hakim (2012)
Detection of Lung Cancer: Concomitant Volatile Organic Compounds and Metabolomic Profiling of Six Cancer Cell Lines of Different Histological Origins
Zhunan Jia (2018)
The use of the area under the ROC curve in the evaluation of machine learning algorithms
A. Bradley (1997)
A Meta-Analysis of Asbestos-Related Cancer Risk That Addresses Fiber Size and Mineral Type
D. Berman (2008)
Pleural plaques and the risk of pleural mesothelioma.
J. Pairon (2013)
Modelling cognitive loads in schizophrenia by means of new functional dynamic indexes
A. Lombardi (2019)
Breath analysis as a diagnostic and screening tool for malignant pleural mesothelioma: a systematic review.
Lisa Brusselmans (2018)
Breath Analysis: A Systematic Review of Volatile Organic Compounds (VOCs) in Diagnostic and Therapeutic Management of Pleural Mesothelioma
A. Catino (2019)
A study of the volatile organic compounds exhaled by lung cancer cells in vitro for breath diagnosis
X. Chen (2007)
Phenylketonuria: a review of current and future treatments.
Naz Al Hafid (2015)
The potential of breath analysis to improve outcome for patients with lung cancer.
S. Antoniou (2019)
Signals in asbestos related diseases in human breath - preliminary results
Y. Çakir (2014)
Screening for Malignant Pleural Mesothelioma and Lung Cancer in Individuals with a History of Asbestos Exposure
H. Roberts (2009)
The Third Italian Consensus Conference for Malignant Pleural Mesothelioma: State of the art and recommendations.
S. Novello (2016)
Non-small cell lung cancer is characterized by dramatic changes in phospholipid profiles
E. Marien (2015)
Blood/air distribution of volatile organic compounds (VOCs) in a nationally representative sample.
C. Jia (2012)
Genetic Variants Associated with Increased Risk of Malignant Pleural Mesothelioma: A Genome-Wide Association Study
G. Matullo (2013)
Temporal variation of VOC emission from solvent and water based wood stains
G. Gennaro (2015)
Is n-pentane really an index of lipid peroxidation in humans and animals? A methodological reevaluation.
D. Kohlmueller (1993)
Metabolic Heterogeneity in Human Lung Tumors
Christopher T. Hensley (2016)
Multi-step metabolic activation of benzene. Effect of superoxide dismutase on covalent binding to microsomal macromolecules, and identification of glutathione conjugates using high pressure liquid chromatography and field desorption mass spectrometry.
A. Tunek (1980)

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