Pancreatic carcinoma, pancreatitis, and healthy controls: metabolite models in a three-class diagnostic dilemma |
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Authors: | Alexander Benedikt Leichtle Uta Ceglarek Peter Weinert Christos T. Nakas Jean-Marc Nuoffer Julia Kase Tim Conrad Helmut Witzigmann Joachim Thiery Georg Martin Fiedler |
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Affiliation: | 1. Center of Laboratory Medicine, University Institute of Clinical Chemistry, Inselspital—Bern University Hospital, Inselspital INO F 502/UKC, 3010, Bern, Switzerland 2. Institute of Laboratory Medicine, Clinical Chemistry, and Molecular Diagnostics, University Hospital Leipzig, 04103, Leipzig, Germany 3. Leibniz Supercomputing Centre, Bavarian Academy of Sciences and Humanities, Boltzmannstr. 1, 85748, Garching, Germany 4. Laboratory of Biometry, University of Thessaly, Fytokou Str., N. Ionia, 38446, Magnesia, Greece 8. Center of Laboratory Medicine, University Institute of Clinical Chemistry, Inselspital—Bern University Hospital, Inselspital INO F 610/UKC, 3010, Bern, Switzerland 5. Department of Hematology, Oncology and Tumor Immunology, Campus Virchow Clinic, and Molekulares Krebsforschungszentrum, Charité—Universit?tsmedizin, Augustenburger Platz 1, 13353, Berlin, Germany 6. Department of Mathematics, Free University of Berlin, Arnimallee 6, 14195, Berlin, Germany 7. Clinic of Visceral Surgery, University Hospital Leipzig, Liebigstr. 20, 04103, Leipzig, Germany 9. Center of Laboratory Medicine, University Institute of Clinical Chemistry, Inselspital—Bern University Hospital, Inselspital INO F 603/UKC, 3010, Bern, Switzerland
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Abstract: | 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 mass-spectrometric 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. 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. 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 $ [ {text{volume under ROC surface}};left( {text{VUS}} right) = 0. 8 9 1 { }left( { 9 5,% {text{ CI }}0. 7 9 4- 0. 9 6 8} right)]. $ We combined highly standardized samples, a three-class study design, a high-throughput 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. |
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