@article{ConradLeichtleNuofferetal., author = {Conrad, Tim and Leichtle, Alexander Benedikt and Nuoffer, Jean-Marc and Ceglarek, Uta and Kase, Julia and Witzigmann, Helmut and Thiery, Joachim and Fiedler, Georg Martin}, title = {Serum amino acid profiles and their alterations in colorectal cancer}, series = {Metabolomics}, journal = {Metabolomics}, doi = {10.1007/s11306-011-0357-5}, abstract = {Mass spectrometry-based serum metabolic profiling is a promising tool to analyse complex cancer associated metabolic alterations, which may broaden our pathophysiological understanding of the disease and may function as a source of new cancer-associated biomarkers. Highly standardized serum samples of patients suffering from colon cancer (n = 59) and controls (n = 58) were collected at the University Hospital Leipzig. We based our investigations on amino acid screening profiles using electrospray tandem-mass spectrometry. Metabolic profiles were evaluated using the Analyst 1.4.2 software. General, comparative and equivalence statistics were performed by R 2.12.2. 11 out of 26 serum amino acid concentrations were significantly different between colorectal cancer patients and healthy controls. We found a model including CEA, glycine, and tyrosine as best discriminating and superior to CEA alone with an AUROC of 0.878 (95\\% CI 0.815?0.941). Our serum metabolic profiling in colon cancer revealed multiple significant disease-associated alterations in the amino acid profile with promising diagnostic power. Further large-scale studies are necessary to elucidate the potential of our model also to discriminate between cancer and potential differential diagnoses. In conclusion, serum glycine and tyrosine in combination with CEA are superior to CEA for the discrimination between colorectal cancer patients and controls.}, language = {en} } @article{ConradGenzelCvetkovicetal., author = {Conrad, Tim and Genzel, Martin and Cvetkovic, Nada and Wulkow, Niklas and Leichtle, Alexander Benedikt and Vybiral, Jan and Kytyniok, Gitta and Sch{\"u}tte, Christof}, title = {Sparse Proteomics Analysis - a compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data}, series = {BMC Bioinfomatics}, volume = {18}, journal = {BMC Bioinfomatics}, number = {160}, doi = {10.1186/s12859-017-1565-4}, abstract = {Background: High-throughput proteomics techniques, such as mass spectrometry (MS)-based approaches, produce very high-dimensional data-sets. In a clinical setting one is often interested in how mass spectra differ between patients of different classes, for example spectra from healthy patients vs. spectra from patients having a particular disease. Machine learning algorithms are needed to (a) identify these discriminating features and (b) classify unknown spectra based on this feature set. Since the acquired data is usually noisy, the algorithms should be robust against noise and outliers, while the identified feature set should be as small as possible. Results: We present a new algorithm, Sparse Proteomics Analysis (SPA),based on thet heory of compressed sensing that allows us to identify a minimal discriminating set of features from mass spectrometry data-sets. We show (1) how our method performs on artificial and real-world data-sets, (2) that its performance is competitive with standard (and widely used) algorithms for analyzing proteomics data, and (3) that it is robust against random and systematic noise. We further demonstrate the applicability of our algorithm to two previously published clinical data-sets.}, language = {en} } @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{ConradLeichtleHagehuelsmannetal.2006, author = {Conrad, Tim and Leichtle, Alexander Benedikt and Hageh{\"u}lsmann, Andre and Diederichs, Elmar and Baumann, Sven and Thiery, Joachim and Sch{\"u}tte, Christof}, title = {Beating the Noise}, series = {Lecture Notes in Computer Science}, volume = {4216}, journal = {Lecture Notes in Computer Science}, publisher = {Springer}, pages = {119 -- 128}, year = {2006}, 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} }