Abstract B52: Targeted LC-MS/MS metabolic profiling for colon cancer progression monitoring (2016)

Abstract Introduction: Colorectal cancer (CRC) is one of the most prevalent cancers worldwide, and a major cause of human morbidity and mortality. A number of current efforts are focused on earlier detection of colon cancer using a variety of technologies including genomics, proteomics and metabolomics. Research focused on CRC disease status surveillance using metabolomics or other approaches has not been reported; Close monitoring of disease progression (DP) in CRC can be critical for patients' prognosis management and treatment decisions. In this study we investigate a targeted LC-MS/MS approach for serum metabolic profiling to monitor and predict patient disease progression, using a panel of significantly altered metabolites as potential biomarkers. Methods: 59 serum samples from 21 CRC patients were analyzed, including 23 samples from DP patients and 36 from other CRC disease status (e.g., stable disease and complete remission). Chromatographic separations were performed via an Agilent HPLC system installed with two hydrophilic interaction chromatography (HILIC) columns, and then targeted data acquisition was performed in multiple-reaction-monitoring (MRM) mode using an AB Sciex QTrap 5500 mass spectrometer. We monitored 106 and 58 MRM transitions in negative and positive mode, respectively. Univariate and multivariate statistical analyses (such as the Mann- Whitney U-test and PLS-DA) were applied for metabolite biomarker discovery and model development on a selected set of promising biomarker candidates. Monte Carlo cross validation (MCCV) was performed to evaluate model robustness. Results and conclusion: LC-MS/MS targeted analysis provided a robust system for metabolic profiling of CRC patient disease status monitoring using serum samples. Targeted screening of 164 metabolites, representing more than 20 different classes (such as amino acids, carboxylic acids, pyridines, and etc.) and from 25 important metabolic pathways (e.g., TCA cycle, amino acid metabolism, purine and pyrimidine metabolism, and glycolysis, and etc.) was performed using both positive and negative ionization modes. 131 metabolites could be reproducibly detected in the serum samples, with an average CV of 7.1% measured in pooled serum quality control samples. After univariate analysis, 36 metabolites from different classes, such as monosaccharides, amino acids, carboxylic acids and nucleosides, showed a significant statistical difference (p&lt;0.05) between CRC DP compared to other disease status (e.g., stable disease and complete remission), and twelve of these were previously reported in other CRC serum metabolites studies. Highly significant changes (defined as p&lt;0.001) were found in the average levels of seven metabolites, namely fructose, aspartic acid, oxalic acid, lactate, pyruvate, oxaloacetate and orotate. These metabolites are involved in multiple important metabolic pathways, such as carbohydrate metabolism, tricarboxylic acid cycle, glycolosis, amino acid metabolism, and pyrimidine metabolism. A PLS-DA model was built based on the combination of these seven metabolite biomarkers, and excellent performance was obtained with sensitivity of 96%, specificity of 75% and area under the receiver operator curve (AUROC) of 0.92. Superior performance of this metabolite profile was observed as compared to the traditional carcinoembryonic antigen (CEA) marker currently used for CRC monitoring, which had an AUROC of 0.76, sensitivity of 87% and specificity of 55% for detecting DP over other disease status. Further, Monte Carlo cross validation (MCCV) was performed to compare the true sample classifications with randomly permuted sample classes, and to measure model robustness. MCCV also demonstrated the improved sensitivity and robust diagnostic power of this metabolic profiling approach. In conclusion, our targeted LC-MS/MS metabolic profiling of serum provides an improved approach for colon cancer disease progression monitoring. Citation Format: Jiangjiang Zhu, Danijel Djukovic, Lingli Deng, Lingli Deng, Haiwei Gu, Farhan Himmati, Mohammad Abu Zaid, E. Gabriela Chiorean, E. Gabriela Chiorean, Daniel Raftery, Daniel Raftery. Targeted LC-MS/MS metabolic profiling for colon cancer progression monitoring. [abstract]. In: Proceedings of the AACR Special Conference: Metabolism and Cancer; Jun 7-10, 2015; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Res 2016;14(1_Suppl):Abstract nr B52.