Abstract B50: Detecting colorectal cancer and polyps using nuclear magnetic resonance spectroscopy and mass spectrometry based metabolomics
Authors: Deng Lingli , Gu Haiwei , Zhu Jiangjiang , Gowda Nagana , Djukovic Danijel , Raftery Daniel 
Published: May 2016

Background: Colorectal cancer (CRC) represents the third most prevalent cancer in the US, despite being one of the most preventable. Given the strong connection between CRC and metabolism, a number of these efforts have been made using metabolomics. Currently, the majority of metabolomics studies use either nuclear magnetic resonance (NMR) or mass spectrometry (MS) separately. However, thus far the potential of combining NMR and MS for biomarker discovery and statistical modeling is still poorly recognized and not well developed (such as the challenges of variable selection in the combined data sets). In this study we examine the potential of combining NMR and MS metabolomics for the detection of patients with either CRC or polyps.

Methods: A total of 127 serum samples from three groups of age, gender, and BMI-matched subjects (CRC (N=28), polyp patients (N=44) and healthy controls (N=55)) were analyzed, and potential biomarkers were selected from backward variable elimination incorporated multi-block partial least squares-discriminant analysis (BVE-PLSDA). In each iteration, one variable was dropped out, and the remaining variables were used for PLS-DA. The variables with the highest prediction accuracy for the test samples in Monte Carlo cross validation (MCCV) were kept for the next iteration.

Results: For all the three pairwise comparisons among CRC, polyps, and healthy controls, the highest classification accuracy of the NMR+MS data was obviously better than that from NMR or MS alone. For example, in the case of CRC Vs healthy controls, the NMR+MS data provided the highest classification accuracy of 0.95±0.05 after BVE, compared to 0.84±0.07 from NMR and 0.93±0.05 from MS. As expected, an excessive number of variables deteriorated statistical models, and there was an optimal number/range of variables which could produce the best statistical performance.

Our approach was able to select an optimal set of metabolites for each pairwise comparison, and we achieved a comprehensive profile of altered metabolites from the combined (NMR+MS) data that were related to CRC and polyps, including those in glycolysis, the TCA cycle, amino acid metabolism, etc. Both NMR and MS detected metabolites contributed to the mixed panel of biomarker candidates, and thus both analytical platforms are very valuable methods to identify metabolic changes occurring in patients with CRC and polyps. The combined set of metabolite biomarkers should be helpful to comprehensively understand metabolite alterations of CRC and the mechanisms during disease progression. The majority of important NMR and MS metabolites were different from each other, evidencing that both NMR and MS can provide unique contributions to statistical modeling in metabolomics. Interestingly, CRC had the lowest adenosine levels while the controls had the highest levels. However, orotate (and some other metabolites) did not continuously increase or decrease from controls to polyps, and then to CRC, which indicates that disease progression could be a very complex process metabolically.

Conclusions: The metabolic profiles of blood serum from CRC patients, polyps, and healthy controls were measured by NMR and LC-MS/MS and showed significant changes in metabolism with the onset of CRC. BVE-PLSDA identified optimal sets of metabolites that could be further validated for the diagnosis of CRC and polyps. The combination of NMR and MS showed significantly better statistical performance than NMR or MS alone. Therefore, we recommend the combined approach of NMR and MS through appropriate variable selection methods in metabolomics, especially for the purpose of discovering biomarker candidates. Notably, our approach is relatively universal and can be expanded to combine other analytical technologies.

Citation Format: Lingli Deng, Haiwei Gu, Jiangjiang Zhu, Nagana Gowda, Danijel Djukovic, Daniel Raftery. Detecting colorectal cancer and polyps using nuclear magnetic resonance spectroscopy and mass spectrometry based metabolomics. [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 B50.