Online citations, reference lists, and bibliographies.
← Back to Search

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

Cite This
Download PDF
Analyze on Scholarcy
Share
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
10.18632/oncotarget.21335
Breath analysis by gas chromatography-mass spectrometry and electronic nose to screen for pleural mesothelioma: a cross-sectional case-control study
K. Lamote (2017)
10.1016/S0891-5849(99)00212-9
Clinical application of breath biomarkers of oxidative stress status.
T. H. Risby (1999)
10.1162/153244303322753616
An Introduction to Variable and Feature Selection
I. Guyon (2003)
10.1016/j.isci.2018.12.008
Profiling Single Cancer Cells with Volatolomics Approach
Mamatha Serasanambati (2019)
10.1038/s41598-017-11339-1
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)
10.1002/ajim.22442
Incidence of malignant mesothelioma of the pleura in Québec and Canada from 1984 to 2007, and projections from 2008 to 2032.
Alfreda Krupoves (2015)
10.1016/j.lungcan.2011.08.009
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)
10.1023/A:1008845332422
Incidence of cancer among Finnish patients with asbestos-related pulmonary or pleural fibrosis
A. Karjalainen (2004)
10.1177/1403494815596500
Emerging evidence that the ban on asbestos use is reducing the occurrence of pleural mesothelioma in Sweden
B. Järvholm (2015)
10.1016/0009-2797(83)90157-6
Detection and identification of sulfhydryl conjugates of rho-benzoquinone in microsomal incubations of benzene and phenol.
S. Lunte (1983)
10.1016/J.SNB.2015.02.025
Feature selection and analysis on correlated gas sensor data with recursive feature elimination
Ke Yan (2015)
10.1016/J.IMU.2019.100180
A Random Forest based predictor for medical data classification using feature ranking
Md. Zahangir Alam (2019)
10.1088/1752-7155/8/1/016003
Assessment of the exhalation kinetics of volatile cancer biomarkers based on their physicochemical properties.
A. Amann (2014)
10.1016/j.yrtph.2015.05.002
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)
10.1126/science.1160809
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)
10.1038/cddis.2016.111
'Mitochondrial energy imbalance and lipid peroxidation cause cell death in Friedreich's ataxia'
R. Abeti (2016)
10.1136/thx.2006.072892
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)
10.1186/1471-2407-9-348
Noninvasive detection of lung cancer by analysis of exhaled breath
A. Bajtarevic (2009)
10.1515/CCLM.2009.133
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)
10.1183/09031936.00040911
A breath test for malignant mesothelioma using an electronic nose
Elaine. Chapman (2011)
10.1093/CLINCHEM/34.8.1613
A computerized classification technique for screening for the presence of breath biomarkers in lung cancer.
H. O'neill (1988)
10.1136/oemed-2017-104383
Non-occupational exposure to asbestos and risk of pleural mesothelioma: review and meta-analysis
G. Marsh (2017)
10.1016/j.jtho.2016.02.017
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)
10.1080/08839519308949993
Inductive and Bayesian learning in medical diagnosis
I. Kononenko (1993)
10.1172/JCI72873
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)
10.1136/oemed-2015-103309
Incidence and survival trends for malignant pleural and peritoneal mesothelioma, Australia, 1982–2009
M. Soeberg (2016)
10.1038/nbt1206-1565
What is a support vector machine?
William Stafford Noble (2006)
10.1023/A:1010933404324
Random Forests
L. Breiman (2004)
Breath analysis in non small cell lung cancer patients after surgical tumour resection.
D. Poli (2008)
10.1021/ac2026892
Fast analytical methodology based on mass spectrometry for the determination of volatile biomarkers in saliva.
M. Sánchez (2012)
10.1126/science.124.3215.267
On respiratory impairment in cancer cells.
S. Weinhouse (1956)
10.1016/J.CCCN.2004.04.023
Diagnostic potential of breath analysis--focus on volatile organic compounds.
W. Miekisch (2004)
10.5555/URI:PII:002221439290284R
Free radicals, antioxidants, and human disease: where are we now?
B. Halliwell (1992)
10.1016/S1470-2045(13)70257-2
Asbestos is not just asbestos: an unrecognised health hazard.
F. Baumann (2013)
10.1183/13993003.00919-2017
Exhaled breath to screen for malignant pleural mesothelioma: a validation study
K. Lamote (2017)
10.1016/j.cell.2017.09.019
Lactate Metabolism in Human Lung Tumors
B. Faubert (2017)
10.3390/metabo9030052
Critical Review of Volatile Organic Compound Analysis in Breath and In Vitro Cell Culture for Detection of Lung Cancer
Zhunan Jia (2019)
10.18632/oncotarget.17910
Biomarkers for early diagnosis of malignant mesothelioma: Do we need another moonshot?
Sabrina Lagniau (2017)
10.1186/1476-4598-8-41
Altered regulation of metabolic pathways in human lung cancer discerned by 13C stable isotope-resolved metabolomics (SIRM)
T. W. Fan (2009)
10.1007/s00408-013-9526-9
Serum Biomarkers in Patients with Mesothelioma and Pleural Plaques and Healthy Subjects Exposed to Naturally Occurring Asbestos
Mehmet Bayram (2013)
10.1042/BJ20081386
How mitochondria produce reactive oxygen species
M. Murphy (2009)
10.1038/sj.bjc.6605810
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)
10.1093/annonc/mdu357.4
1560PA BREATH TEST FOR DIAGNOSING MALIGNANT PLEURAL MESOTHELIOMA.
K. Lamote (2014)
10.1088/1752-7155/10/4/046001
Detection of malignant pleural mesothelioma in exhaled breath by multicapillary column/ion mobility spectrometry (MCC/IMS).
K. Lamote (2016)
10.3109/10715769409145633
Alveolar gradient of pentane in normal human breath.
M. Phillips (1994)
10.1016/j.jhazmat.2017.11.056
Asbestos containing materials detection and classification by the use of hyperspectral imaging.
G. Bonifazi (2018)
10.1007/s00216-010-4238-y
Chemical characterization of exhaled breath to differentiate between patients with malignant plueral mesothelioma from subjects with similar professional asbestos exposure
G. Gennaro (2010)
10.1021/cr300174a
Volatile organic compounds of lung cancer and possible biochemical pathways.
M. Hakim (2012)
10.1021/acsomega.7b02035
Detection of Lung Cancer: Concomitant Volatile Organic Compounds and Metabolomic Profiling of Six Cancer Cell Lines of Different Histological Origins
Zhunan Jia (2018)
10.1016/S0031-3203(96)00142-2
The use of the area under the ROC curve in the evaluation of machine learning algorithms
A. Bradley (1997)
10.1080/10408440802273156
A Meta-Analysis of Asbestos-Related Cancer Risk That Addresses Fiber Size and Mineral Type
D. Berman (2008)
10.1093/jnci/djs513
Pleural plaques and the risk of pleural mesothelioma.
J. Pairon (2013)
10.1016/j.neuroimage.2019.03.055
Modelling cognitive loads in schizophrenia by means of new functional dynamic indexes
A. Lombardi (2019)
10.21037/tlcr.2018.04.09
Breath analysis as a diagnostic and screening tool for malignant pleural mesothelioma: a systematic review.
Lisa Brusselmans (2018)
10.3390/cancers11060831
Breath Analysis: A Systematic Review of Volatile Organic Compounds (VOCs) in Diagnostic and Therapeutic Management of Pleural Mesothelioma
A. Catino (2019)
10.1002/cncr.22844
A study of the volatile organic compounds exhaled by lung cancer cells in vitro for breath diagnosis
X. Chen (2007)
10.3978/j.issn.2224-4336.2015.10.07
Phenylketonuria: a review of current and future treatments.
Naz Al Hafid (2015)
10.1088/1752-7163/ab0bee
The potential of breath analysis to improve outcome for patients with lung cancer.
S. Antoniou (2019)
10.1007/s12127-014-0147-7
Signals in asbestos related diseases in human breath - preliminary results
Y. Çakir (2014)
10.1097/JTO.0b013e31819f2e0e
Screening for Malignant Pleural Mesothelioma and Lung Cancer in Individuals with a History of Asbestos Exposure
H. Roberts (2009)
10.1016/j.critrevonc.2016.05.004
The Third Italian Consensus Conference for Malignant Pleural Mesothelioma: State of the art and recommendations.
S. Novello (2016)
10.1002/ijc.29517
Non-small cell lung cancer is characterized by dramatic changes in phospholipid profiles
E. Marien (2015)
10.1016/j.scitotenv.2011.12.055
Blood/air distribution of volatile organic compounds (VOCs) in a nationally representative sample.
C. Jia (2012)
10.1371/journal.pone.0061253
Genetic Variants Associated with Increased Risk of Malignant Pleural Mesothelioma: A Genome-Wide Association Study
G. Matullo (2013)
10.1016/J.ATMOSENV.2015.04.021
Temporal variation of VOC emission from solvent and water based wood stains
G. Gennaro (2015)
10.1006/ABIO.1993.1195
Is n-pentane really an index of lipid peroxidation in humans and animals? A methodological reevaluation.
D. Kohlmueller (1993)
10.1016/j.cell.2015.12.034
Metabolic Heterogeneity in Human Lung Tumors
Christopher T. Hensley (2016)
10.1016/0009-2797(80)90040-X
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)



This paper is referenced by
Semantic Scholar Logo Some data provided by SemanticScholar