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Targeted Feature Extraction in MALDI Mass Spectrometry Imaging to Discriminate Proteomic Profiles of Breast and Ovarian Cancer
Author(s) -
Cordero Hernandez Yovany,
Boskamp Tobias,
Casadonte Rita,
HaubergLotte Lena,
Oetjen Janina,
Lachmund Delf,
Peter Annette,
Trede Dennis,
Kriegsmann Katharina,
Kriegsmann Mark,
Kriegsmann Jörg,
Maass Peter
Publication year - 2019
Publication title -
proteomics – clinical applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.948
H-Index - 54
eISSN - 1862-8354
pISSN - 1862-8346
DOI - 10.1002/prca.201700168
Subject(s) - linear discriminant analysis , discriminative model , artificial intelligence , pattern recognition (psychology) , computer science , ovarian cancer , feature extraction , mass spectrometry imaging , biomarker discovery , mass spectrometry , proteomics , computational biology , cancer , biology , medicine , chemistry , chromatography , biochemistry , gene
Purpose To develop a mass spectrometry imaging (MSI) based workflow for extracting m/z values related to putative protein biomarkers and using these for reliable tumor classification. Experimental design Given a list of putative breast and ovarian cancer biomarker proteins, a set of related m/z values are extracted from heterogeneous MSI datasets derived from formalin‐fixed paraffin‐embedded tissue material. Based on these features, a linear discriminant analysis classification model is trained to discriminate the two tumor types. Results It is shown that the discriminative power of classification models based on the extracted features is increased compared to the automatic training approach, especially when classifiers are applied to spectral data acquired under different conditions (instrument, preparation, laboratory). Conclusions and clinical relevance Robust classification models not confounded by technical variation between MSI measurements are obtained. This supports the assumption that the classification of the respective tumor types is based on biological rather than technical differences, and that the selected features are related to the proteomic profiles of the tumor types under consideration.