<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>2696</id>
    <completedYear>2009</completedYear>
    <publishedYear>2009</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>3812</pageFirst>
    <pageLast>3819</pageLast>
    <pageNumber/>
    <edition/>
    <issue>11</issue>
    <volume>15</volume>
    <type>article</type>
    <publisherName>American Association for Cancer Research,</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Serum Peptidome Profiling Revealed Platelet Factor 4 as a Potential Discriminating Peptide Associated With Pancreatic Cancer</title>
    <parentTitle language="eng">Clinical Cancer Research</parentTitle>
    <identifier type="url">http://publications.imp.fu-berlin.de/155/</identifier>
    <identifier type="doi">10.1158/1078-0432.CCR-08-2701</identifier>
    <author>Georg Martin Fiedler</author>
    <author>Alexander Benedikt Leichtle</author>
    <author>Julia Kase</author>
    <author>Sven Baumann</author>
    <author>Uta Ceglarek</author>
    <author>Klaus Felix</author>
    <author>Tim Conrad</author>
    <author>Helmut Witzigmann</author>
    <author>Arved Weimann</author>
    <author>Christof Schütte</author>
    <author>Johann Hauss</author>
    <author>Markus Büchler</author>
    <author>Joachim Thiery</author>
    <collection role="persons" number="schuette">Schütte, Christof</collection>
  </doc>
  <doc>
    <id>6350</id>
    <completedYear/>
    <publishedYear>2013</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>677</pageFirst>
    <pageLast>687</pageLast>
    <pageNumber/>
    <edition/>
    <issue>3</issue>
    <volume>9</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Pancreatic carcinoma, pancreatitis, and healthy controls - metabolite models in a three-class diagnostic dilemma</title>
    <abstract language="eng">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.&#13;
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. &#13;
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)].&#13;
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&#13;
metabolomic profiling necessitates appropriate evaluation strategies and ?despite all its current limitations? can deliver marker panels with high selectivity even in multi-class settings.</abstract>
    <parentTitle language="eng">Metabolomics</parentTitle>
    <identifier type="doi">10.1007/s11306-012-0476-7</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <author>Tim Conrad</author>
    <submitter>Paulina Bressel</submitter>
    <author>Alexander Benedikt Leichtle</author>
    <author>Uta Ceglarek</author>
    <author>P. Weinert</author>
    <author>C.T. Nakas</author>
    <author>Jean-Marc Nuoffer</author>
    <author>Julia Kase</author>
    <author>Helmut Witzigmann</author>
    <author>Joachim Thiery</author>
    <author>Georg Martin Fiedler</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="persons" number="conrad">Conrad, Tim</collection>
    <collection role="projects" number="MODAL-MedLab">MODAL-MedLab</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>6355</id>
    <completedYear/>
    <publishedYear>2012</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Serum amino acid profiles and their alterations in colorectal cancer</title>
    <abstract language="eng">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.</abstract>
    <parentTitle language="eng">Metabolomics</parentTitle>
    <identifier type="doi">10.1007/s11306-011-0357-5</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <author>Tim Conrad</author>
    <submitter>Paulina Bressel</submitter>
    <author>Alexander Benedikt Leichtle</author>
    <author>Jean-Marc Nuoffer</author>
    <author>Uta Ceglarek</author>
    <author>Julia Kase</author>
    <author>Helmut Witzigmann</author>
    <author>Joachim Thiery</author>
    <author>Georg Martin Fiedler</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="persons" number="conrad">Conrad, Tim</collection>
    <collection role="projects" number="MODAL-MedLab">MODAL-MedLab</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
</export-example>
