Repository: Freie Universität Berlin, Math Department

Pancreatic carcinoma, pancreatitis, and healthy controls - metabolite models in a three-class diagnostic dilemma

Leichtle, A. and Ceglarek, U. and Weinert, P. and Nakas, C. T. and Nuoffer, J.-M. and Kase, J. and Conrad, T. O. F. and Witzigmann, H. and Thiery, J. and Fiedler, G. M. (2013) Pancreatic carcinoma, pancreatitis, and healthy controls - metabolite models in a three-class diagnostic dilemma. Metabolomics, 9 (3). pp. 677-687. ISSN 1573-3890

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Background: Metabolomics as one of the most rapidly growing technologies in the “-omics”field denotes the comprehensive analysis of low molecular-weight compounds and their pathways. Cancer-specific alterations of the metabolome can be detected by high-throughput massspectrometric metabolite profiling and serve as a considerable source of new markers for the early differentiation of malignant diseases as well as their distinction from benign states. However, a comprehensive framework for the statistical evaluation of marker panels in a multi-class setting has not yet been established. Methods: We collected serum samples of 40 pancreatic carcinoma patients, 40 controls, and 23 pancreatitis patients according to standard protocols and generated amino acid profiles by routine mass-spectrometry. In an intrinsic three-class bioinformatic approach we compared these profiles, evaluated their selectivity and computed multi-marker panels combined with the conventional tumor marker CA 19-9. Additionally, we tested for non-inferiority and superiority to determine the diagnostic surplus value of our multi-metabolite marker panels. Results: Compared to CA 19-9 alone, the combined amino acid-based metabolite panel had a superior selectivity for the discrimination of healthy controls, pancreatitis, and pancreatic carcinoma patients [Volume under ROC surface (VUS) = 0.891 (95% CI 0.794 - 0.968)]. Conclusions: We combined highly standardized samples, a three-class study design, a highthroughput mass-spectrometric technique, and a comprehensive bioinformatic framework to identify metabolite panels selective for all three groups in a single approach. Our results suggest that metabolomic profiling necessitates appropriate evaluation strategies and –despite all its current limitations– can deliver marker panels with high selectivity even in multi-class settings.

Item Type:Article
Subjects:Mathematical and Computer Sciences > Statistics > Mathematical Statistics
Medicine and Dentistry > Clinical Medicine
Mathematical and Computer Sciences > Artificial Intelligence > Machine Learning
Divisions:Department of Mathematics and Computer Science > Institute of Mathematics > Comp. Proteomics Group
Department of Mathematics and Computer Science > Institute of Mathematics
ID Code:1165
Deposited By: Admin Administrator
Deposited On:15 Oct 2012 08:34
Last Modified:13 May 2013 11:37

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