Exhaled breath to screen for malignant pleural mesothelioma: a validation study.

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.

External Validation of a Breath-Based Prediction Model for Malignant Pleural Mesothelioma.

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.

Breath analysis by gas chromatography-mass spectrometry and electronic nose to screen for pleural mesothelioma: a cross-sectional case-control study.

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.

Detection of malignant pleural mesothelioma in exhaled breath by multicapillary column/ion mobility spectrometry (MCC/IMS).

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.

Serial measurements of mesothelioma serum biomarkers in asbestos-exposed individuals: a prospective longitudinal cohort study.

INTRODUCTION: Soluble mesothelin (SM) and megakaryocyte potentiating factor (MPF) are serum biomarkers of mesothelioma. This study aims to examine the longitudinal behavior of SM and MPF in controls to gain insight in the optimal use of these biomarkers in screening. METHODS: Asbestos-exposed individuals, with no malignant disease at inclusion, were surveilled for 2 years with annual measurements of SM and MPF. Fixed thresholds were set at 2.10 nmol/L for SM and 13.10 ng/ml for MPF. Longitudinal biomarker analysis, using a random intercept model, estimated the association with age and glomerular filtration rate (GFR), and the intraclass correlation. The latter represents the proportion of total biomarker variance accounted for by the between-individual variance. RESULTS: A total of 215 participants were included, of whom 179 and 137 provided a second sample and third sample, respectively. Two participants with normal SM and MPF levels presented afterward with mesothelioma and lung cancer, respectively. Participants with elevated biomarker levels were typically older and had a lower GFR. During follow-up, biomarker levels significantly increased. Longitudinal analysis indicated that this was in part due to aging, while changes in GFR had a less pronounced effect on serial biomarker measurements. SM and MPF had a high intraclass correlation of 0.81 and 0.78, respectively, which implies that a single biomarker measurement and fixed threshold are suboptimal in screening. CONCLUSIONS: The longitudinal behavior of SM and MPF in controls indicates that a biomarker-based screening approach can benefit from the incorporation of serial measurements and individual-specific screening rules, adjusted for age and GFR. Large-scale validation remains nevertheless mandatory to elucidate whether such an approach can improve the early detection of mesothelioma.