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Casadonte, Rita; Kriegsmann, Mark; Perren, Aurel; Baretton, Gustavo; Deininger, Sören‐Oliver; Kriegsmann, Katharina; Welsch, Thilo; Pilarsky, Christian; Kriegsmann, Jörg

Development of a Class Prediction Model to Discriminate Pancreatic Ductal Adenocarcinoma from Pancreatic Neuroendocrine Tumor by MALDI Mass Spectrometry Imaging

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  • Medientyp: E-Artikel
  • Titel: Development of a Class Prediction Model to Discriminate Pancreatic Ductal Adenocarcinoma from Pancreatic Neuroendocrine Tumor by MALDI Mass Spectrometry Imaging
  • Beteiligte: Casadonte, Rita; Kriegsmann, Mark; Perren, Aurel; Baretton, Gustavo; Deininger, Sören‐Oliver; Kriegsmann, Katharina; Welsch, Thilo; Pilarsky, Christian; Kriegsmann, Jörg
  • Erschienen: Wiley, 2019
  • Erschienen in: PROTEOMICS – Clinical Applications, 13 (2019) 1
  • Sprache: Englisch
  • DOI: 10.1002/prca.201800046
  • ISSN: 1862-8346; 1862-8354
  • Schlagwörter: Clinical Biochemistry
  • Entstehung:
  • Hochschulschrift:
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  • Beschreibung: <jats:sec><jats:title>Purpose</jats:title><jats:p>To define proteomic differences between pancreatic ductal adenocarcinoma (pDAC) and pancreatic neuroendocrine tumor (pNET) by matrix‐assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI).</jats:p></jats:sec><jats:sec><jats:title>Experimental design</jats:title><jats:p>Ninety‐three pDAC and 126 pNET individual tissues are assembled in tissue microarrays and analyzed by MALDI MSI. The cohort is separated in a training (52 pDAC and 83 pNET) and validation set (41 pDAC and 43 pNET). Subsequently, a linear discriminant analysis (LDA) model based on 46 peptide ions is performed on the training set and evaluated on the validation cohort. Additionally, two liver metastases and a whole slide of pDAC are analyzed by the same LDA algorithm.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Classification of pDAC and pNET by the LDA model is correct in 95% (39/41) and 100% (43/43) of patients in the validation cohort, respectively. The two liver metastases and the whole slide of pDAC are also correctly classified in agreement with the histopathological diagnosis.</jats:p></jats:sec><jats:sec><jats:title>Conclusion and clinical relevance</jats:title><jats:p>In the present study, a large dataset of pDAC and pNET by MALDI MSI is investigated, a class prediction model that allowed separation of both entities with high accuracy is developed, and differential peptide peaks with potential diagnostic, prognostic, and predictive values are highlighted.</jats:p></jats:sec>