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. 2010 Aug;59(8):1046-55.
doi: 10.1136/gut.2009.202986.

Molecular imaging of VEGF in gastrointestinal cancer in vivo using confocal laser endomicroscopy

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Molecular imaging of VEGF in gastrointestinal cancer in vivo using confocal laser endomicroscopy

Sebastian Foersch et al. Gut. 2010 Aug.

Abstract

Background: Vascular endothelial growth factor (VEGF) is a therapeutic target in gastrointestinal cancer (GiC). However, its in vivo visualisation could not be achieved to date with endoscopic techniques. Confocal laser endomicroscopy (CLE) is a novel imaging technique for gastrointestinal endoscopy providing in vivo microscopy at subcellular resolution. The aim of the study was to evaluate CLE for in vivo molecular imaging of VEGF in GiC.

Methods: Molecular imaging of tumours in APCmin mice, in xenograft models and in surgical specimens of patients with colorectal cancer (CRC) was achieved after application of labelled antibodies. The tumour sites were scanned with the probe for the strongest specific fluorescent signal. From all tumour sites examined with CLE in vivo, targeted specimens were obtained for histology, immunohistochemistry (IHC) and fluorescence microscopy.

Results: A VEGF-specific signal was visualised in vivo in 13/15 APCmin mice and in 9/10 xenograft tumours. CLE enabled the cytoplasmatic distribution of VEGF to be displayed due to its subcellular resolution. In human tissue, a VEGF-specific signal was observed in 12/13 malignant specimens and in 10/11 samples from healthy mucosa from the patients (p<0.03). CLE findings correlated well with ex vivo microscopy.

Conclusion: In vivo molecular imaging with specific targeting of VEGF is possible in murine tumours, human xenografts and tissue specimens using CLE. CLE with similar probes can be performed in human colonoscopy. Therefore-from a technical point of view-in vivo molecular imaging is transferable to stratification of patients with CRC during endoscopy even today. CLE could contribute to the identification of lesions at risk and potentially predict response to targeted treatment.

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