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.

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: Invasive diagnostic techniques contribute to a late diagnosis and hence poor prognosis in patients with malignant pleural mesothelioma (MPM). Blood biomarkers, in particular the less explored low abundant tissue leakage proteins released after cell death or damage, seem promising as less invasive alternatives. Aims: Define a new diagnostic MPM biomarker panel to differentiate MPM patients from different control groups. Methods: 230 plasma samples from MPM patients (n=45), lung cancer patients (n=27), (a)symptomatic professionally asbestos-exposed individuals (n=105) and healthy controls without known asbestos exposure (n=43) were processed using a unique method to enrich for tissue leakage proteins. All samples were analysed by liquid chromatography-tandem mass spectrometry. Differentially expressed proteins were selected at a false discovery rate ≤ 0.05. A random forest classifier was used to select the most performant discriminatory proteins. A ridge regression model was constructed and internally validated by leave-one-out-cross-validation. Results: Conclusions: We developed a diagnostic biomarker panel for MPM with high accuracy. Secondly, we identified a high number of low abundant intracellular proteins in the MPM group. Additional testing is necessary to verify if these intracellular proteins are more performant as diagnostic biomarkers than plasma proteins.

Introduction: The current follow-up procedure for malignant pleural mesothelioma (MPM) patients is suboptimal and predictive markers are lacking. Aims: Investigate whether valatile organic compounds (VOCs) in exhaled breath are able to discriminate treatment outcome in follow-up samples and if so, if this outcome can be predicted earlier on. Methods: At least one breath and background sample was collected from 9 MPM patients via multi-capillary column-ion mobility spectrometry. The associated CT-scans were scored as either stable (SD) or progressive (PD). After background correction, a lasso regression was performed. A discriminative model was set-up to differentiate between SD and PD in follow-up samples, while a predictive model was trained to predict the follow-up outcome based on the associated breath sample from the previous study visit. Results: We were able to discriminate between SD and PD in follow-up samples. Furthermore, a prediction of the treatment outcome based on an earlier breath sample was even more accurate (figure 1). Due to the small samples size, it is to be expected that our confidence intervals are relatively large, which is why validation in a larger population is necessary. Conclusion: We can conclude that VOCs in exhaled breath are promising in detecting and predicting treatment outcome of MPM patients. However, further research in a larger population is necessary to validate our results.

Introduction: Invasive diagnostic techniques contribute to a late diagnosis and hence poor prognosis in patients with malignant pleural mesothelioma (MPM). Blood biomarkers, in particular low abundant tissue leakage proteins released after cell death or damage, seem promising as less invasive alternatives. Aims: Validate our protocol to enrich plasma for tissue leakage proteins and identify new diagnostic biomarkers to discriminate MPM patients from healthy controls (HC). Methods: Plasma samples from 5 MPM patients and 5 HC were collected and processed using a proprietary and currently confidential method to enrich for tissue leakage proteins. All samples were analysed by liquid chromatography-tandem mass spectrometry. Only protein groups with at least three valid measurements were considered for further statistical analysis. Results: From a total of 508 identified unique proteins, 20 proteins had a significantly different expression pattern between the MPM and HC group (figure 1a). After removal of contaminants commonly found in plasma, 15 proteins remained significant (figure 1b). In future research these proteins will further be explored as diagnostic MPM biomarkers. Conclusion: We were able to differentiate MPM patients from HC based on their plasma proteome enriched for tissue leakage proteins, which are less explored and therefore hold a lot of potential as MPM biomarkers.