Development of a Class Prediction Model to Discriminate Pancreatic Ductal Adenocarcinoma from Pancreatic Neuroendocrine Tumor by MALDI Mass Spectrometry Imaging. Issue 1 (19th December 2018)

• Record Type: Journal Article
• Authors: Casadonte, Rita; Kriegsmann, Mark; Perren, Aurel; Baretton, Gustavo; Deininger, Sören‐Oliver; Kriegsmann, Katharina; Welsch, Thilo; Pilarsky, Christian; Kriegsmann, Jörg
• Other Names: Longuespée Rémi guestEditor.; Casadonte Rita guestEditor.; Schwamborn Kristina guestEditor.; Kriegsmann Mark guestEditor.
• Is Part Of: Proteomics. Volume 13:Issue 1(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-12-19
• Subjects: formalin‐fixed paraffin embedded (FFPE) -- matrix‐assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) -- neuroendocrine‐tumor -- pancreas -- tumor typing; Proteomics -- Periodicals; 572.605
• DOI: 10.1002/prca.201800046
• Languages: English
• ISSNs: 1862-8346

Abstract

Abstract : Purpose: 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). Experimental design: 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. Results: 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. Conclusion and clinical relevance: 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.