Lipidomic Profiling of Colorectal Lesions for Real-Time Tissue Recognition and Risk-Stratification Using Rapid Evaporative Ionization Mass Spectrometry. Issue 3 (13th March 2023)

• Record Type: Journal Article
• Authors: Mason, Sam E.; Manoli, Eftychios; Alexander, James L.; Poynter, Liam; Ford, Lauren; Paizs, Petra; Adebesin, Afeez; McKenzie, James S.; Rosini, Francesca; Goldin, Rob; Darzi, Ara; Takats, Zoltan; Kinross, James M.
• Is Part Of: Annals of surgery. Volume 277:Issue 3(2023)
• Page Start: e569
• Page End: e577
• Publication Date: 2023-03-13
• Subjects: colorectal cancer -- mass spectrometry -- metabolic profiling -- metabolomics -- tissue recognition; Surgery -- Periodicals; 617.005
• DOI: 10.1097/SLA.0000000000005164
• Languages: English
• ISSNs: 0003-4932

Abstract

Abstract : Objective: Rapid evaporative ionization mass spectrometry (REIMS) is a metabolomic technique analyzing tissue metabolites, which can be applied intraoperatively in real-time. The objective of this study was to profile the lipid composition of colorectal tissues using REIMS, assessing its accuracy for real-time tissue recognition and risk-stratification. Summary Background Data: Metabolic dysregulation is a hallmark feature of carcinogenesis; however, it remains unknown if this can be leveraged for real-time clinical applications in colorectal disease. Methods: Patients undergoing colorectal resection were included, with carcinoma, adenoma and paired-normal mucosa sampled. Ex vivo analysis with REIMS was conducted using monopolar diathermy, with the aerosol aspirated into a Xevo G2S QToF mass spectrometer. Negatively charged ions over 600 to 1000 m/z were used for univariate and multivariate functions including linear discriminant analysis. Results: A total of 161 patients were included, generating 1013 spectra. Unique lipidomic profiles exist for each tissue type, with REIMS differentiating samples of carcinoma, adenoma, and normal mucosa with 93.1% accuracy and 96.1% negative predictive value for carcinoma. Neoplasia (carcinoma or adenoma) could be predicted with 96.0% accuracy and 91.8% negative predictive value. Adenomas can be risk-stratified by grade of dysplasia with 93.5% accuracy, but not histological subtype. The structure of 61 lipid metabolites was identified, revealing that during colorectal carcinogenesis there is progressive increase in relative abundance of phosphatidylglycerols, sphingomyelins, and mono-unsaturated fatty acid-containing phospholipids. Conclusions: The colorectal lipidome can be sampled by REIMS and leveraged for accurate real-time tissue recognition, in addition to riskstratification of colorectal adenomas. Unique lipidomic features associated with carcinogenesis are described.