@article{ConradLeichtleCeglareketal., author = {Conrad, Tim and Leichtle, Alexander Benedikt and Ceglarek, Uta and Weinert, P. and Nakas, C.T. and Nuoffer, Jean-Marc and Kase, Julia and Witzigmann, Helmut and Thiery, Joachim and Fiedler, Georg Martin}, title = {Pancreatic carcinoma, pancreatitis, and healthy controls - metabolite models in a three-class diagnostic dilemma}, series = {Metabolomics}, volume = {9}, journal = {Metabolomics}, number = {3}, doi = {10.1007/s11306-012-0476-7}, pages = {677 -- 687}, abstract = {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.}, language = {en} } @article{FiedlerLeichtleKaseetal.2009, author = {Fiedler, Georg Martin and Leichtle, Alexander Benedikt and Kase, Julia and Baumann, Sven and Ceglarek, Uta and Felix, Klaus and Conrad, Tim and Witzigmann, Helmut and Weimann, Arved and Sch{\"u}tte, Christof and Hauss, Johann and B{\"u}chler, Markus and Thiery, Joachim}, title = {Serum Peptidome Profiling Revealed Platelet Factor 4 as a Potential Discriminating Peptide Associated With Pancreatic Cancer}, series = {Clinical Cancer Research}, volume = {15}, journal = {Clinical Cancer Research}, number = {11}, publisher = {American Association for Cancer Research,}, doi = {10.1158/1078-0432.CCR-08-2701}, pages = {3812 -- 3819}, year = {2009}, language = {en} }