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In Vivo Molecular Imaging of Colorectal Cancer With Confocal Endomicroscopy by Targeting Epidermal Growth Factor Receptor

2009; Elsevier BV; Volume: 138; Issue: 2 Linguagem: Inglês

10.1053/j.gastro.2009.10.032

1528-0012

Martin Goetz, Alex Ziebart, Sebastian Foersch, Michael Vieth, Maximilian J. Waldner, Peter Delaney, Peter R. Galle, Markus F. Neurath, Ralf Kießlich,

Cancer Research and Treatments

Background & Aims Epidermal growth factor receptor (EGFR) is a therapeutic target for colorectal cancer (CRC). However, technical challenges have limited in vivo imaging of EGFR in CRCs. Confocal laser endomicroscopy (CLE) enables accurate microscopic visualization of CRC in patients during endoscopy. We evaluated the ability to use CLE in vivo for instantaneous molecular imaging of EGFR in CRC models. Methods Tumors were grown in mice (n = 68) from human CRC cell lines that expressed high (SW480 cells) or low (SW620 cells) levels of EGFR. Tumors were visualized in vivo with a handheld CLE probe after injection of fluorescently labeled EGFR antibodies. EGFR-specific fluorescence was graded from 0 to 3+. Neoplastic and non-neoplastic specimens from human colorectal mucosa were examined. In vivo findings were correlated with histopathology, immunohistochemistry, and fluorescence microscopy analyses. Results CLE analysis of cell cultures confirmed the different expression levels of EGFR between cell lines. In living animals, CLE differentiated EGFR expression levels between tumor cell limes (mean fluorescence, 1.92 ± 0.22 [SW480] and 0.59 ± 0.21 [SW620], P = .0004). CLE analysis of EGFR expression in human specimens allowed distinction of neoplastic from non-neoplastic tissues (mean fluorescence, 2.0 ± 0.37 vs 0.25 ± 0.16, respectively, P = .0035). Conclusions CLE can be used for in vivo, molecular analysis of CRC and to differentiate EGFR expression patterns in xenograft tumors and human tissue samples. Because CLE can be performed during endoscopy, in vivo molecular imaging might be used in diagnosis of CRC and to predict response to targeted therapies.