Characterization of inflammatory breast cancer: a vibrational microspectroscopy and imaging approach at the cellular and tissue level.
Fiche publication
Date publication
novembre 2018
Journal
The Analyst
Auteurs
Membres identifiés du Cancéropôle Est :
Dr BREZILLON Stéphane, Pr SOCKALINGUM Ganesh
Tous les auteurs :
Mohamed HT, Untereiner V, Proult I, Ibrahim SA, Götte M, El-Shinawi M, Mohamed MM, Sockalingum GD, Brézillon S
Lien Pubmed
Résumé
Inflammatory breast cancer (IBC) has a poor prognosis because of the lack of specific biomarkers and its late diagnosis. An accurate and rapid diagnosis implemented early enough can significantly improve the disease outcome. Vibrational spectroscopy has proven to be useful for cell and tissue characterization based on the intrinsic molecular information. Here, we have applied infrared and Raman microspectroscopy and imaging to differentiate between non-IBC and IBC at both cell and tissue levels. Two human breast cancer cell lines (MDA-MB-231 and SUM-149), 20 breast cancer patients (10 non-IBC and 10 IBC), and 4 healthy volunteer biopsies were investigated. Fixed cells and tissues were analyzed by FTIR microspectroscopy and imaging, while live cells were studied by Raman microspectroscopy. Spectra were analyzed by hierarchical cluster analysis (HCA) and images by common k-means clustering algorithms. For both cell suspensions and single cells, FTIR spectroscopy showed sufficient high inter-group variability to delineate MDA-MB-231 and SUM-149 cell lines. Most significant differences were observed in the spectral regions of 1096-1108 and 1672-1692 cm-1. Analysis of live cells by Raman microspectroscopy gave also a good discrimination of these cell types. The most discriminant regions were 688-992, 1019-1114, 1217-1375 and 1516-1625 cm-1. Finally, k-means cluster analysis of FTIR images allowed delineating non-IBC from IBC tissues. This study demonstrates the potential of vibrational spectroscopy and imaging to discriminate between non-IBC and IBC at both cell and tissue levels.
Mots clés
Adult, Aged, Algorithms, Cell Line, Tumor, Cluster Analysis, Female, Humans, Inflammatory Breast Neoplasms, chemistry, Middle Aged, Single-Cell Analysis, methods, Spectroscopy, Fourier Transform Infrared, methods, Spectrum Analysis, Raman, methods, Vibration
Référence
Analyst. 2018 Nov 23;: