AbstractDespite the widespread use of metabolomics in the plight for new disease biomarkers, there still remains a lack of progress in the area of thoracic disease diagnostics, specifically relating to pleural effusions. The diagnostic capability of pleural fluid has scarcely changed over the past four decades, and remains an underutilised biological sample. This research investigates potential reasons for the lack of new diagnostics biomarkers for pleural disease and how this can be rectified. Initial findings were that there was a distinct lack of validation for the vast majority of biomarker studies, further, within pleural fluid collection and extraction methods for metabolomics, a further lack of standardisation was apparent. Therefore, primary research was conducted to test a wide variety of sample handling methods to determine how these deviations could result in different data outputs, via untargeted mass spectrometry. Further, metabolomic assessment was conducted and validated on the largest collection of pleural fluid samples to date, in an attempt to create diagnostic models for a variety of pleural fluid based diseases. It was found that both differences in sample collection and extraction protocols resulted in many metabolite levels being altered due to methodological differences alone. Further, diagnostics models were created, resulting in high sensitivity (>92%) for all diagnostic classes assessed; organ failure, infection, and malignancy, along with high Area Under Curve (AUC) values (>0.86). From these experiments, an optimised sample handling method was described. This method can reduce costs, time, and resources required for the specific application of metabolomics to pleural fluid, while also resulting in more reproducible data via the use of standardise protocols in the future. This is the first time that method optimisation studies have been carried out on pleural fluid, specifically for metabolomic analysis, adding a wealth of information to the field which will help drive forward standardisation, and thus discovery of more robust and translational biomarkers, ultimately
improving patient care and treatments.
|Date of Award||2023|
|Supervisor||Manfred Beckmann (Supervisor) & Luis Mur (Supervisor)|