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Calcineurin inhibitors (CNIs) are regarded as a corner stone in immunosuppressive therapy after solid organ transplantation. However, neurotoxicity is a common side effect of CNIs, resulting in a wide range of neurological symptoms such as headache, tremor and seizures. In this case report, we describe a patient who developed severe motor and sensory neuron dysfunction related to CNIs after bilateral lung transplantation, which resolved after halting CNI and switching to a mammalian Target of Rapamycin-inhibitor. [ABSTRACT FROM AUTHOR]

Pleural mesothelioma (PM) is an aggressive cancer of the serosal lining of the thoracic cavity, predominantly caused by asbestos exposure. Due to nonspecific symptoms, PM is characterized by an advanced-stage diagnosis, resulting in a dismal prognosis. However, early diagnosis improves patient outcome. Currently, no diagnostic biomarkers or screening tools are available. Therefore, exhaled breath was explored as this can easily be obtained and contains volatile organic compounds, which are considered biomarkers for multiple (patho)physiological processes. A breath test, which differentiates asbestos-exposed (AEx) individuals from PM patients with 87% accuracy, was developed. However, before being implemented as a screening tool, the clinical utility of the test must be determined. Occupational AEx individuals underwent annual breath tests using multicapillary column/ion mobility spectrometry. A baseline breath test was taken and their individual risk of PM was estimated. PM patients were included as controls. In total, 112 AEx individuals and six PM patients were included in the first of four screening rounds. All six PM patients were correctly classified as having mesothelioma (100% sensitivity) and out of 112 AEx individuals 78 were classified by the breath-based model as PM patients (30% specificity). Given the large false positive outcome, the breath test will be repeated annually for three more consecutive years to adhere to the 'test, re-test' principle and improve the false positivity rate. A low-dose computed tomography scan in those with two consecutive positive tests will correlate test positives with radiological findings and the possible growth of a pleural tumor. Finally, the evaluation of the clinical value of a breath-based prediction model may lead to the initiation of a screening program for early detection of PM in Aex individuals, which is currently lacking. This clinical study received approval from the Antwerp University Hospital Ethics Committee (B300201837007)., (© 2023 IOP Publishing Ltd.)

In this study, we aimed to study the expression of SARS-CoV-2-related surface proteins in non-small-cell lung cancer (NSCLC) cells and identify clinicopathological characteristics that are related to increased membranous (m)ACE2 protein expression and soluble (s)ACE2 levels, with a particular focus on standard of care (SOC) therapies. ACE2 ( n = 107), TMPRSS2, and FURIN ( n = 38) protein expression was determined by immunohistochemical (IHC) analysis in NSCLC patients. sACE2 levels ( n = 64) were determined in the serum of lung cancer patients collected before, during, or after treatment with SOC therapies. Finally, the TCGA lung adenocarcinoma (LUAD) database was consulted to study the expression of ACE2 in EGFR- and KRAS-mutant samples and ACE2 expression was correlated with EGFR/HER, RAS, BRAF, ROS1, ALK, and MET mRNA expression. Membranous (m)ACE2 was found to be co-expressed with mFURIN and/or mTMPRSS2 in 16% of the NSCLC samples and limited to the adenocarcinoma subtype. TMPRSS2 showed predominantly atypical cytoplasmic expression. mACE2 and sACE2 were more frequently expressed in mutant EGFR patients, but not mutant-KRAS patients. A significant difference was observed in sACE2 for patients treated with targeted therapies, but not for chemo- and immunotherapy. In the TCGA LUAD cohort, ACE2 expression was significantly higher in EGFR-mutant patients and significantly lower in KRAS-mutant patients. Finally, ACE2 expression was positively correlated with ERBB2-4 and ROS1 expression and inversely correlated with KRAS, NRAS, HRAS, and MET mRNA expression. We identified a role for EGFR pathway activation in the expression of mACE2 in NSCLC cells, associated with increased sACE2 levels in patients. Therefore, it is of great interest to study SARS-CoV-2-infected EGFR-mutated NSCLC patients in greater depth in order to obtain a better understanding of how mACE2, sACE2, and SOC TKIs can affect the course of COVID-19.

Background: Irritable bowel syndrome (IBS) is a chronic gastrointestinal disorder for which no diagnostic tools are currently available. Patients are diagnosed using the Rome IV criteria and subtyped into a diarrhea, constipation, or mixed phenotype based on their dominant stool pattern. A recent development in the biomarker area is the analysis of volatile organic compounds (VOCs). The aim of this study was to evaluate the potential of VOCs as diagnostic and phenotypic biomarkers for IBS in breath and fecal samples., Materials and Methods: Breath and fecal samples from IBS patients and healthy asymptomatic controls (HC) were analyzed with multicapillary column/ion mobility spectrometry (MCC/IMS) and classification models were created based upon VOCs and clinical characteristics., Discussion: Irritable bowel syndrome patients were differentiated from HC by means of volatile profiling in both breath and fecal samples with area under the curve (AUCs) of respectively 0.62 and 0.80. Patient subtypes could also be differentiated from each other with AUCs ranging between 0.65 and 0.78. Furthermore, VOC models could differentiate IBS patients based on clinical characteristics like psychological comorbidities and microbiota-influencing therapies., Conclusion: This study is the first to demonstrate the use of VOC profiling with the help of MCC/IMS to differentiate IBS patients. Furthermore, the importance of clinical characteristics beside the dominant stool pattern in the differentiation of IBS patients was emphasized., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Van Malderen, Hanning, Lambrechts, Haverhals, Van Marcke, Ceuleers, De Man, De Winter, Lamote and De Schepper.)

During the past decade, volatile organic compounds (VOCs) in exhaled breath have emerged as promising biomarkers for malignant pleural mesothelioma (MPM). However, as these biomarkers lack external validation, no breath test for MPM has been implemented in clinical practice. To address this issue, we performed the first external validation of a VOC-based prediction model for MPM. The external validation cohort was prospectively recruited, consisting of 47 MPM patients and 76 asbestos-exposed (AEx) controls. The predictive performance of the previously developed model was assessed by determining the degree of agreement between the predicted and actual outcome of the participants (patient/control). Additionally, to optimise the performance, the model was updated by refitting it to the validation cohort. External validation revealed a poor performance of the original model as the accuracy was estimated at only 41%, indicating poor generalisability. However, subsequent updating of the model improved the differentiation between MPM patients and AEx controls significantly (73% accuracy, 92% sensitivity, and 92% negative predictive value), substantiating the validity of the original predictors. This updated model will be more generalisable to the target population and exhibits key characteristics of a potential screening test for MPM, which could significantly impact MPM management.

Introduction: Malignant pleural mesothelioma (MPM) is a lethal cancer for which early-stage diagnosis remains a major challenge. Volatile organic compounds (VOCs) in breath proved to be potential biomarkers for MPM diagnosis, but translational studies are needed to elucidate which VOCs originate from the tumor itself and thus are specifically related to MPM cell metabolism., Methods: An in vitro model was set-up to characterize the headspace VOC profiles of six MPM and two lung cancer cell lines using thermal desorption-gas chromatography-mass spectrometry. A comparative analysis was carried out to identify VOCs that could discriminate between MPM and lung cancer, as well as between the histological subtypes within MPM (epithelioid, sarcomatoid and biphasic)., Results: VOC profiles were identified capable of distinguishing MPM (subtypes) and lung cancer cells with high accuracy. Alkanes, aldehydes, ketones and alcohols represented many of the discriminating VOCs. Discrepancies with clinical findings were observed, supporting the need for studies examining breath and tumor cells of the same patients and studying metabolization and kinetics of in vitro discovered VOCs in a clinical setting., Conclusion: While the relationship between in vitro and in vivo VOCs is yet to be established, both could complement each other in generating a clinically useful breath model for MPM., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Janssens, Mol, Vandermeersch, Lagniau, Vermaelen, van Meerbeeck, Walgraeve, Marcq and Lamote.)

Low-dose volume CT screening for lung cancer leads to a significant decrease in lung-cancer-related mortality. However, optimisation of the post-screening protocol will be crucial for optimal healthcare. https://bit.ly/2ZkJPpH., Competing Interests: Conflict of interest: E. Schillebeeckx has nothing to disclose. Conflict of interest: K. Lamote reports grants from the Foundation Against Cancer, and from Stand up to Cancer (the Flemish Cancer Society), outside the submitted work., (Copyright ©ERS 2022.)

Malignant pleural mesothelioma (MPM) is characterised by late-stage diagnosis and poor prognosis. Currently, no screening tool is advocated and diagnosis is based on invasive techniques, which are not well tolerated. Non-invasive diagnostic biomarkers have shown potential and could have a huge clinical benefit. However, despite extensive research, there is no consensus yet on their clinical use, with many articles reporting contradicting results, limiting their clinical implementation. The aim of this systematic review is therefore to explore the different semi- and non-invasive diagnostic markers in several human matrices and identify those that might clinically be relevant. A total of 100 articles were selected through Web of Science and PubMed, with 56 articles included in the quantitative analysis. Although many studies have reported on the diagnostic accuracy of MPM biomarkers such as serum mesothelin and high-mobility group box protein 1 and plasma fibulin-3, none have resulted in a validated test for early detection. Future research should focus on external validation, combinations into biomarker panels, the inclusion of early stage MPM patients and a combination of different biomarker matrices, as well as new markers., Competing Interests: Conflict of interest: E. Schillebeeckx has nothing to disclose. Conflict of interest: J.P. van Meerbeeck has nothing to disclose. Conflict of interest: K. Lamote has nothing to disclose., (Copyright ©The authors 2021.)

BACKGROUNDSARS-CoV-2 infection induces mucin overexpression, further promoting disease. Given that mucins are critical components of innate immunity, unraveling their expression profiles that dictate the course of disease could greatly enhance our understanding and management of COVID-19.METHODSUsing validated RT-PCR assays, we assessed mucin mRNA expression in the blood of patients with symptomatic COVID-19 compared with symptomatic patients without COVID-19 and healthy controls and correlated the data with clinical outcome parameters. Additionally, we analyzed mucin expression in mucus and lung tissue from patients with COVID-19 and investigated the effect of drugs for COVID-19 treatment on SARS-CoV-2-induced mucin expression in pulmonary epithelial cells.RESULTSWe identified a dynamic blood mucin mRNA signature that clearly distinguished patients with symptomatic COVID-19 from patients without COVID-19 based on expression of MUC1, MUC2, MUC4, MUC6, MUC13, MUC16, and MUC20 (AUCROC of 91.8%; sensitivity and specificity of 90.6% and 93.3%, respectively) and that discriminated between mild and critical COVID-19 based on the expression of MUC16, MUC20, and MUC21 (AUCROC of 89.1%; sensitivity and specificity of 90.0% and 85.7%, respectively). Differences in the transcriptional landscape of mucins in critical cases compared with mild cases identified associations with COVID-19 symptoms, respiratory support, organ failure, secondary infections, and mortality. Furthermore, we identified different mucins in the mucus and lung tissue of critically ill COVID-19 patients and showed the ability of baricitinib, tocilizumab, favipiravir, and remdesivir to suppress expression of SARS-CoV-2-induced mucins.CONCLUSIONThis multifaceted blood mucin mRNA signature showed the potential role of mucin profiling in diagnosing, estimating severity, and guiding treatment options in patients with COVID-19.FUNDINGThe Antwerp University Research and the Research Foundation Flanders COVID-19 funds.

Malignant mesothelioma is an aggressive cancer type linked to asbestos exposure. Because of several intrinsic challenges, mesothelioma is often diagnosed in an advanced disease stage. Therefore, there is a need for diagnostic biomarkers that may contribute to early detection. Recently, the epigenome of tumors is being extensively investigated to identify biomarkers. This manuscript is a systematic review summarizing the state-of-the-art research investigating DNA methylation in mesothelioma. Four literature databases (PubMed, Scopus, Web of Science, MEDLINE) were systematically searched for studies investigating DNA methylation in mesothelioma up to October 16, 2020. A meta-analysis was performed per gene investigated in at least two independent studies. A total of 53 studies investigated DNA methylation of 97 genes in mesothelioma and are described in a qualitative overview. Furthermore, ten studies investigating 13 genes (APC, CDH1, CDKN2A, DAPK, ESR1, MGMT, miR-34b/c, PGR, RARβ, RASSF1, SFRP1, SFRP4, WIF1) were included in the quantitative meta-analysis. In this meta-analysis, the APC gene is significantly hypomethylated in mesothelioma, whereas CDH1, ESR1, miR-34b/c, PGR, RARβ, SFRP1, and WIF1 are significantly hypermethylated in mesothelioma. The three genes that are the most appropriate candidate biomarkers from this meta-analysis are APC, miR-34b/c, and WIF1. Nevertheless, both study number and study objects comprised in this meta-analysis are too low to draw final conclusions on their clinical applications. The elucidation of the genome-wide DNA methylation profile of mesothelioma is desirable in the future, using a standardized genome-wide methylation analysis approach. The most informative CpG sites from this signature could then form the basis of a panel of highly sensitive and specific biomarkers that can be used for the diagnosis of mesothelioma and even for the screening of an at high-risk population of asbestos-exposed individuals., (Copyright © 2021 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.)

Volatile organic compounds (VOCs) have shown potential as non-invasive breath biomarkers for lung cancer, but their unclear biological origin currently limits clinical applications. This systematic review explores headspace analysis of VOCs in patient-derived body fluids and lung cancer cell lines to pinpoint lung cancer-specific VOCs and uncover their biological origin. A search was performed in the databases MEDLINE and Web of Science. Twenty-two articles were included in this systematic review. Since there is no standardised approach to analyse VOCs, a plethora of techniques and matrices/cell lines were explored, which is reflected in the various VOCs identified. However, comparing VOCs in the headspace of urine, blood and pleural effusions from patients and lung cancer cell lines showed some overlapping VOCs, indicating their potential use in lung cancer diagnosis. This review demonstrates that VOCs are promising biomarkers for lung cancer. However, due to lack of inter-matrix consensus, standardised prospective trials will have to be conducted to validate clinically relevant biomarkers., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Head and neck neoplasms have a poor prognosis because of their late diagnosis. Finding a biomarker to detect these tumors in an early phase could improve the prognosis and survival rate. This literature review provides an overview of biomarkers, covering the different -omics fields to diagnose head and neck neoplasms in the early phase. To date, not a single biomarker, nor a panel of biomarkers for the detection of head and neck tumors has been detected with clinical applicability. Limitations for the clinical implementation of the investigated biomarkers are mainly the heterogeneity of the study groups (e.g., small population in which the biomarker was tested, and/or only including high-risk populations) and a low sensitivity and/or specificity of the biomarkers under study. Further research on biomarkers to diagnose head and neck neoplasms in an early stage, is therefore needed., (Copyright © 2020 Konings, Stappers, Geens, De Winter, Lamote, van Meerbeeck, Specenier, Vanderveken and Ledeganck.)

Volatile organic compounds (VOCs) are produced by the human metabolism, inflammation and gut microbiota and form the basis of innovative volatomics research. VOCs detected through breath and faecal analysis hence serve as attractive, non-invasive biomarkers for diagnosing and monitoring irritable bowel syndrome (IBS) and inflammatory bowel disease (IBD). This review describes the clinical applicability of volatomics in discriminating between IBS, IBD and healthy volunteers with acceptable accuracy in breath (70%-100%) and faecal (58%-85%) samples. Promising compounds are propan-1-ol for diagnosing and monitoring of IBD patients, and 1-methyl-4-propan-2-ylcyclohexa-1,4-diene as biomarker for IBS diagnosis. However, these VOCs often seem to be related to inflammation and probably will need to be used in conjunction with other clinical evidence. Furthermore, three interventional studies underlined the potential of VOCs in predicting treatment outcome and patient follow-up. This shows great promise for future use of VOCs as non-invasive breath and faecal biomarkers in personalised medicine. However, properly designed studies that correlate VOCs to IBD/IBS pathogenesis, while taking microbial influences into account, are still key before clinical implementation can be expected., Competing Interests: Declaration of Competing Interest The authors declare no personal conflict of interests., (Copyright © 2020 The Author(s). Published by Elsevier B.V. All rights reserved.)

Lung cancer is very common and the most common cause of cancer death worldwide. Despite recent progress in the systemic treatment of lung cancer (checkpoint inhibitors and tyrosine kinase inhibitors), each year, >1.5 million people die due to this disease. Most lung cancer patients already have advanced disease at the time of diagnosis. Computed tomography screening of high-risk individuals can detect lung cancer at an earlier stage but at a cost of false-positive findings. Biomarkers could lead towards a reduction of these false-positive findings and earlier lung cancer diagnosis, and have the potential to improve outcomes and treatment monitoring. To date, there is a lack of such biomarkers for lung cancer and other thoracic malignancies, although electronic nose (e-nose)-derived biomarkers are of interest. E-nose techniques using exhaled breath component measurements can detect lung cancer with a sensitivity ranging from 71% to 96% and specificity from 33 to 100%. In some case series, such results have been validated but this is mostly using internal validation and hence, more work is needed. Furthermore, standardised sampling and analysis methods are lacking, impeding interstudy comparison and clinical implementation. In this narrative review, we provide an overview of the currently available data on E-nose technology for lung cancer detection., Competing Interests: Conflict of interest: Dr. van Geffen reports a grants from Novartis, outside the submitted work. Conflict of interest: K. Lamote reports grants from Kom op tegen Kanker (Stand up to Cancer), the Flemish cancer society, during the writing of this article. Conflict of interest: A. Costantini has nothing to disclose. Conflict of interest: L.E.L. Hendriks reports fees paid to her institution for advisory board participation from Boehringer Ingelheim and BMS, travel support from Roche and BMS, research funding paid to her institution from Roche, Boehringer Ingelheim and AstraZeneca, a mentorship programme with key opinion leaders funded by AstraZeneca, and personal fees for educational webinars from Quadia, outside the submitted work. Conflict of interest: N.M. Rahman has nothing to disclose. Conflict of interest: T.G. Blum has nothing to disclose. Conflict of interest: J. van Meerbeeck has nothing to disclose., (Copyright ©ERS 2019.)

Current clinical management of lung nodule patients is inefficient and therefore causes patient misclassification, which increases healthcare expenses. A precise and robust lung nodule classifier could minimise healthcare costs and discomfort for patients. http://bit.ly/2oMIEwQ., Competing Interests: Conflict of interest: N. I. Kanellakis has nothing to disclose. Conflict of interest: K. Lamote reports grants from Kom op tegen Kanker (Stand up to Cancer), the Flemish cancer society, during the conduct of the study., (Copyright ©ERS 2019.)

Malignant pleural mesothelioma (MPM) is a tumour related to a historical exposure to asbestos fibres. Currently, the definite diagnosis is made only by the histological examination of a biopsy obtained through an invasive thoracoscopy. However, diagnosis is made too late for curative treatment because of non-specific symptoms mainly appearing at advanced stage disease. Hence, due to its biologic aggressiveness and the late diagnosis, survival rate is low and the patients' outcome poor. In addition, radiological imaging, like computed tomographic scans, and blood biomarkers are found not to be sensitive enough to be used as an early diagnostic tool. Detection in an early stage is assumed to improve the patients' outcome but is hampered due to non-specific and late symptomology. Hence, there is a need for a new screening and diagnostic test which could improve the patients' outcome. Despite extensive research has focused on blood biomarkers, not a single has been shown clinically useful, and therefore research recently shifted to "breathomics" techniques to recognize specific volatile organic compounds (VOCs) in the breath of the patient as potential non-invasive biomarkers for disease. In this review, we summarize the acquired knowledge about using breath analysis for diagnosing and monitoring MPM and asbestos-related disorders (ARD). Gas chromatography-mass spectrometry (GC-MS), the gold standard of breath analysis, appears to be the method with the highest accuracy (97%) to differentiate MPM patients from at risk asbestos-exposed subjects. There have already been found some interesting biomarkers that are significantly elevated in asbestosis (NO, 8-isoprostane, leukotriene B4, α-Pinene…) and MPM (cyclohexane) patients. Regrettably, the different techniques and the plethora of studies suffer some limitations. Most studies are pilot studies with the inclusion of a limited number of patients. Nevertheless, given the promising results and easy sampling methods, we can conclude that breath analysis may become a useful tool in the future to screen for MPM, but further research is warranted., Competing Interests: Conflicts of Interest: The authors have no conflicts of interest to declare.

Malignant pleural mesothelioma (MPM) is predominantly caused by asbestos exposure and has a poor prognosis. Breath contains volatile organic compounds (VOCs) and can be explored as an early detection tool. Previously, we used multicapillary column/ion mobility spectrometry (MCC/IMS) to discriminate between patients with MPM and asymptomatic high-risk persons with a high rate of accuracy. Here, we aim to validate these findings in different control groups.Breath and background samples were obtained from 52 patients with MPM, 52 healthy controls without asbestos exposure (HC), 59 asymptomatic former asbestos workers (AEx), 41 patients with benign asbestos-related diseases (ARD), 70 patients with benign non-asbestos-related lung diseases (BLD) and 56 patients with lung cancer (LC).After background correction, logistic lasso regression and receiver operating characteristic (ROC) analysis, the MPM group was discriminated from the HC, AEx, ARD, BLD and LC groups with 65%, 88%, 82%, 80% and 72% accuracy, respectively. Combining AEx and ARD patients resulted in 94% sensitivity and 96% negative predictive value (NPV). The most important VOCs selected were P1, P3, P7, P9, P21 and P26.We discriminated MPM patients from at-risk subjects with great accuracy. The high sensitivity and NPV allow breath analysis to be used as a screening tool for ruling out MPM., Competing Interests: Conflict of interest: None declared., (Copyright ©ERS 2017.)

Rationale: Malignant pleural mesothelioma (MPM) is mainly caused by previous exposure to asbestos fibers and has a poor prognosis. Due to a long latency period between exposure and diagnosis, MPM incidence is expected to peak between 2020-2025. Screening of asbestos-exposed individuals is believed to improve early detection and hence, MPM management. Recent developments focus on breath analysis for screening since breath contains volatile organic compounds (VOCs) which reflect the cell's metabolism., Objectives: The goal of this cross-sectional, case-control study is to identify VOCs in exhaled breath of MPM patients with gas chromatography-mass spectrometry (GC-MS) and to assess breath analysis to screen for MPM using an electronic nose (eNose)., Methods: Breath and background samples were taken from 64 subjects: 16 healthy controls (HC), 19 asymptomatic former asbestos-exposed (AEx) individuals, 15 patients with benign asbestos-related diseases (ARD) and 14 MPM patients. Samples were analyzed with both GC-MS and eNose., Results: Using GC-MS, AEx individuals were discriminated from MPM patients with 97% accuracy, with diethyl ether, limonene, nonanal, methylcyclopentane and cyclohexane as important VOCs. This was validated by eNose analysis. MPM patients were discriminated from AEx+ARD participants by GC-MS and eNose with 94% and 74% accuracy, respectively. The sensitivity, specificity, positive and negative predictive values were 100%, 91%, 82%, 100% for GC-MS and 82%, 55%, 82%, 55% for eNose, respectively., Conclusion: This study shows accurate discrimination of patients with MPM from asymptomatic asbestos-exposed persons at risk by GC-MS and eNose analysis of exhaled VOCs and provides proof-of-principle of breath analysis for MPM screening ., Competing Interests: CONFLICTS OF INTEREST None of the authors declare to have conflicts of interest. This study was presented at the ERS International Congress, 3-7 September 2016, London, UK.

Early diagnosis of malignant pleural mesothelioma (MPM) is a challenge for clinicians. The disease is usually detected in an advanced stage which precludes curative treatment. We assume that only new and non-invasive biomarkers allowing earlier detection will result in better patient management and outcome. Many efforts have already been made to find suitable biomarkers in blood and pleural effusions, but have not yet resulted in a valid and reproducible diagnostic one. In this review, we will highlight the strengths and shortcomings of blood and fluid based biomarkers and highlight the potential of breath analysis as a non-invasive screening tool for MPM. This method seems very promising in the early detection of diverse malignancies, because exhaled breath contains valuable information on cell and tissue metabolism. Research that focuses on breath biomarkers in MPM is in its early days, but the few studies that have been performed show promising results. We believe a breathomics-based biomarker approach should be further explored to improve the follow-up and management of asbestos exposed individuals., Competing Interests: CONFLICTS OF INTEREST None.

Malignant pleural mesothelioma (MPM) is predominantly caused by previous asbestos exposure. Diagnosis often happens in advanced stages restricting any therapeutic perspectives. Early stage detection via breath analysis was explored using multicapillary column/ion mobility spectrometry (MCC/IMS) to detect volatile organic compounds (VOCs) in the exhaled breath of MPM patients in comparison to former occupational asbestos-exposed and non-exposed controls. Breath and background samples of 23 MPM patients, 22 asymptomatic former asbestos (AEx) workers and 21 healthy non-asbestos exposed persons were taken for analysis. After background correction, we performed a logistic least absolute shrinkage and selection operator (lasso) regression to select the most important VOCs, followed by receiver operating characteristic (ROC) analysis. MPM patients were discriminated from both controls with 87% sensitivity, 70% specificity and respective positive and negative predictive values of 61% and 91%. The overall accuracy was 76% and the area under the ROC-curve was 0.81. AEx individuals could be discriminated from MPM patients with 87% sensitivity, 86% specificity and respective positive and negative predictive values of 87% and 86%. The overall accuracy was 87% with an area under the ROC-curve of 0.86. Breath analysis by MCC/IMS allows MPM patients to be discriminated from controls and holds promise for further investigation as a screening tool for former asbestos-exposed persons at risk of developing MPM.

Past and present asbestos use will reflect in increasing numbers of mesothelioma cases in the next decades, diagnosed at a late stage and with a dismal prognosis. This stresses the need for early detection tools, which could improve patients' survival. Recently, breath analysis as a noninvasive and fast diagnostic tool has found its way into biomedical research. High-throughput breathomics uses spectrometric, chromatographic, and sensor techniques to diagnose asbestos-related pulmonary diseases based upon volatile organic compounds (VOC) in breath. This article reviews the state-of-the-art available breath analyzing techniques and provides the insight in the current use of VOCs as early diagnostic or prognostic biomarkers of mesothelioma to stimulate further research in this field. Cancer Epidemiol Biomarkers Prev; 23(6); 898-908. ©2014 AACR., (©2014 American Association for Cancer Research.)