A new classification method for MALDI imaging mass spectrometry data acquired on formalin-fixed paraffin-embedded tissue samples

• Verfasst von: Boskamp, Tobias [VerfasserIn]
• Verfasst von: Warth, Arne [VerfasserIn]
• Verfasst von: Dienemann, Hendrik [VerfasserIn]
• Verfasst von: Kriegsmann, Mark [VerfasserIn]
• Titel: A new classification method for MALDI imaging mass spectrometry data acquired on formalin-fixed paraffin-embedded tissue samples
• Verf.angabe: Tobias Boskamp, Delf Lachmund, Janina Oetjen, Yovany Cordero Hernandez, Dennis Trede, Peter Maass, Rita Casadonte, Jörg Kriegsmann, Arne Warth, Hendrik Dienemann, Wilko Weichert, Mark Kriegsmann
• Jahr: 2017
• Umfang: 11 S.
• Fussnoten: Gesehen am 03.05.2018
• Titel Quelle: Enthalten in: Biochimica et biophysica acta / Proteins and proteomics
• Ort Quelle: Amsterdam [u.a.], 2002
• Jahr Quelle: 2017
• Band/Heft Quelle: 1865(2017), 7, Seite 916-926
• ISSN Quelle: 1878-1454
• DOI: doi:10.1016/j.bbapap.2016.11.003
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Characteristic spectral patterns
• Sach-SW: Classification
• Sach-SW: Feature extraction
• Sach-SW: Formalin-fixed paraffin-embedded
• Sach-SW: MALDI imaging MS
• Sach-SW: Tumor typing
• K10plus-PPN: 1572575263

Abstract

Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) shows a high potential for applications in histopathological diagnosis, and in particular for supporting tumor typing and subtyping. The development of such applications requires the extraction of spectral fingerprints that are relevant for the given tissue and the identification of biomarkers associated with these spectral patterns. We propose a novel data analysis method based on the extraction of characteristic spectral patterns (CSPs) that allow automated generation of classification models for spectral data. Formalin-fixed paraffin embedded (FFPE) tissue samples from N=445 patients assembled on 12 tissue microarrays were analyzed. The method was applied to discriminate primary lung and pancreatic cancer, as well as adenocarcinoma and squamous cell carcinoma of the lung. A classification accuracy of 100% and 82.8%, resp., could be achieved on core level, assessed by cross-validation. The method outperformed the more conventional classification method based on the extraction of individual m/z values in the first application, while achieving a comparable accuracy in the second. LC-MS/MS peptide identification demonstrated that the spectral features present in selected CSPs correspond to peptides relevant for the respective classification. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.