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  • Title: Molecular profiles of thyroid cancer subtypes: Classification based on features of tissue revealed by mass spectrometry imaging.
    Author: Pietrowska M, Diehl HC, Mrukwa G, Kalinowska-Herok M, Gawin M, Chekan M, Elm J, Drazek G, Krawczyk A, Lange D, Meyer HE, Polanska J, Henkel C, Widlak P.
    Journal: Biochim Biophys Acta Proteins Proteom; 2017 Jul; 1865(7):837-845. PubMed ID: 27760391.
    Abstract:
    Determination of the specific type of thyroid cancer is crucial for the prognosis and selection of treatment of this malignancy. However, in some cases appropriate classification is not possible based on histopathological features only, and it might be supported by molecular biomarkers. Here we aimed to characterize molecular profiles of different thyroid malignancies using mass spectrometry imaging (MSI) which enables the direct annotation of molecular features with morphological pictures of an analyzed tissue. Fifteen formalin-fixed paraffin-embedded tissue specimens corresponding to five major types of thyroid cancer were analyzed by MALDI-MSI after in-situ trypsin digestion, and the possibility of classification based on the results of unsupervised segmentation of MALDI images was tested. Novel method of semi-supervised detection of the cancer region of interest (ROI) was implemented. We found strong separation of medullary cancer from malignancies derived from thyroid epithelium, and separation of anaplastic cancer from differentiated cancers. Reliable classification of medullary and anaplastic cancers using an approach based on automated detection of cancer ROI was validated with independent samples. Moreover, extraction of spectra from tumor areas allowed the detection of molecular components that differentiated follicular cancer and two variants of papillary cancer (classical and follicular). We concluded that MALDI-MSI approach is a promising strategy in the search for biomarkers supporting classification of thyroid malignant tumors. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.
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