A new technological approach in diagnostic pathology: mass spectrometry imaging-based metabolomics for biomarker detection in urachal cancer

Abstract

Urachal adenocarcinomas (UrC) are rare but aggressive. Despite being of profound therapeutic relevance, UrC cannot be differentiated by histomorphology alone from other adenocarcinomas of differential diagnostic importance. As no reliable tissue-based diagnostic biomarkers are available, we aimed to detect such by integrating mass-spectrometry imaging-based metabolomics and digital pathology, thus allowing for a multimodal approach on the basis of spatial information. To achieve this, a cohort of UrC (n = 19) and colorectal adenocarcinomas (CRC, n = 27) as the differential diagnosis of highest therapeutic relevance was created, tissue micro-arrays (TMAs) were constructed, and pathological data was recorded. Hematoxylin and eosin (H&E) stained tissue sections were scanned and annotated, enabling an automized discrimination of tumor and non-tumor areas after training of an adequate algorithm. Spectral information within tumor regions, obtained via matrix-assisted laser desorption/ionization (MALDI)-Orbitrap-mass spectrometry imaging (MSI), were subsequently extracted in an automated workflow. On this basis, metabolic differences between UrC and CRC were revealed using machine learning algorithms. As a result, the study demonstrated the feasibility of MALDI-MSI for the evaluation of FFPE tissue in UrC and CRC with the potential to combine spatial metabolomics data with annotated histopathological data from digitalized H&E slides. The detected Area under the curve (AUC) of 0.94 in general and 0.77 for the analyte taurine alone (diagnostic accuracy for taurine: 74%) makes the technology a promising tool in this differential diagnostic dilemma situation. Although the data has to be considered as a proof-of-concept study, it presents a new adoption of this technology that has not been used in this scenario in which reliable diagnostic biomarkers (such as immunohistochemical markers) are currently not available.

Rethinking the initial changes in subarachnoid haemorrhage: Focusing on real-time metabolism during early brain injury

“… 52 Recently, Chen Y et al employed matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to visualize the spatial-chemical changes in metabolite concentrations in an APP/PS1 transgenic mouse model of Alzheimer's disease and found 93 metabolites within different brain regions for the etiopathogenesis of Alzheimer's disease. 53 This newly adopted technology might have applications in scenarios lacking reliable immunohistochemical and diagnostic biomarkers 54 for real-time metabolic changes in the brain parenchyma. In addition, extended algorithms, such as false discovery rate (FDR)-controlled metabolite annotation 55 or the spatial single nuclear metabolomics (SEAM) platform, 56 could explore the spatial metabolic profile and tissue histology at the single-cell level, leading to a deeper understanding of tissue metabolic organization.…”

Editorial: Personalized Medicine for Urological Cancers: Targeting Cancer Metabolism

“…At present, the most prominent issues for such rare tumors are the difficulty of obtaining drugs and the lack of late-stage clinical trials to guide therapeutic decisions. Matrix-assisted laser desorption/ionization (MALDI)-Orbitrap-mass spectrometry imaging (MSI) was recently used in a proof of principle study to showcase that such new methods can be used to identify biomarkers in a scenario where reliable diagnostic standards are not available ( 3 ).…”

New Advances in Tissue Metabolomics: A Review

“…Indeed, better standardization of biomarker validation studies, specimen collection procedures, and robust analytical protocols are required to render metabolomic discoveries more translatable in a clinical setting [ 126 ]. We will discuss recent tissue metabolomic studies with a focus on unique tissue specimens analyzed by innovative methods, as well as their application to improve clinical diagnostics for tissue-specific cancers lacking effective biomarkers for their early detection, such as urachal adenocarcinomas [ 127 ].…”

“…Tissue metabolomic studies via imaging MS is thus ideally suited for analysis of the distribution of metabolites, lipids and/or drugs especially when combined with new advances in machine learning, deep learning, and artificial intelligence [ 144 ]. In fact, MS imaging heralds a revolutionary approach for digital pathology based on data-rich molecular information that may enable accurate discrimination of tumour from non-tumor regions of tissue after adequate model training [ 127 ]. However, further advances in tissue preparation, method reproducibility, faster acquisition times, and broader metabolome coverage is still needed.…”