A multimodal approach to identify asbestos and characterise clinically relevant mesothelioma biomarkers

Abstract

Malignant mesothelioma (MM) is an aggressive cancer of the mesothelium, with long latency and poor overall survival (OS), associated with occupational and environmental exposure to asbestos and other mineral fibres (MF). Considering that early diagnosis has been linked to an improvement in prognosis and treatment response, there is an urgent need for reliable MM biomarkers. Additionally, current asbestos identification techniques lack sensitivity and fail to detect shorter fibres or fibre fragments. Accurately identifying MF within MM samples is not only essential in aiding early diagnosis, but it also plays a key role in linking this diagnosis to asbestos exposure, which has high implications in legal, social, and political matters. The aim of the current project is to (1) develop MM cellular models suitable for laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) imaging, (2) identify various MF based on their metal content within MM in vitro models as well as patient samples using a combination of LA-ICP-MS and LA-ICP-time of flight (TOF)MS instrumentation, (3) investigate the MM metallome in human tissue samples using LA-ICP-MS elemental mapping, and (4) characterise a panel of emerging MM biomarkers using a multi-modal approach. For the first time, this study has developed an asbestos detection technique using LA-ICP-MS imaging to identify MF within MM samples. High-resolution and high-speed analysis suggests that LA-ICP-MS imaging has the potential to be ultimately integrated in the clinical workflow to aid the identification of patients at an earlier and more treatable stage to improve survival outcomes. Moreover, the selected panel of biomarkers was characterised in cellular models, whilst novel biomarkers were tentatively identified using matrix assisted laser desorption ionisation (MALDI)-MS imaging of tissue microarrays, setting the basis for vital future investigations, and ultimately supporting MM biomarker discovery, early diagnosis, and improved prognosis.