Lipidomic Profiling of Colorectal Lesions for Real-Time Tissue Recognition and Risk-Stratification Using Rapid Evaporative Ionization Mass Spectrometry

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 1000m/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 monounsaturated fatty acid-containing phospholipids. Conclusions: The colorectal lipidome can be sampled by REIMS and leveraged for accurate real-time tissue recognition, in addition to risk-stratification of colorectal adenomas. Unique lipidomic features associated with carcinogenesis are described.

Automated summary

This study used REIMS to analyze the lipid composition of colorectal tissues and evaluate its accuracy for real-time tissue recognition and risk stratification. The results showed that REIMS can differentiate samples of carcinoma, adenoma, and normal mucosa with 93.1% accuracy and 96.1% negative predictive value for carcinoma. It can also predict the presence of neoplasia (carcinoma or adenoma) with 96.0% accuracy and 91.8% negative predictive value. In addition, the study identified unique lipidomic features associated with colorectal carcinogenesis, such as the progressive increase in relative abundance of phosphatidylglycerols, sphingomyelins, and monounsaturated fatty acid-containing phospholipids.