[18F]tetrafluoroborate-PET/CT enables sensitive tumor and metastasis in vivo imaging in a sodium iodide symporter-expressing tumor model

Abstract Cancer cell metastasis is responsible for most cancer deaths. Non-invasive in vivo cancer cell tracking in spontaneously metastasizing tumor models still poses a challenge requiring highest sensitivity and excellent contrast. The goal of this study was to evaluate if the recently introduced...

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Autores principales: S. Diocou, A. Volpe, M. Jauregui-Osoro, M. Boudjemeline, K. Chuamsaamarkkee, F. Man, P. J. Blower, T. Ng, G. E. D. Mullen, G. O. Fruhwirth
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2017
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Acceso en línea:https://doaj.org/article/fa7619417ee24b909ddb30aebc30019a
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Sumario:Abstract Cancer cell metastasis is responsible for most cancer deaths. Non-invasive in vivo cancer cell tracking in spontaneously metastasizing tumor models still poses a challenge requiring highest sensitivity and excellent contrast. The goal of this study was to evaluate if the recently introduced PET radiotracer [18F]tetrafluoroborate ([18F]BF4 −) is useful for sensitive and specific metastasis detection in an orthotopic xenograft breast cancer model expressing the human sodium iodide symporter (NIS) as a reporter. In vivo imaging was complemented by ex vivo fluorescence microscopy and γ-counting of harvested tissues. Radionuclide imaging with [18F]BF4 − (PET/CT) was compared to the conventional tracer [123I]iodide (sequential SPECT/CT). We found that [18F]BF4 − was superior due to better pharmacokinetics, i.e. faster tumor uptake and faster and more complete clearance from circulation. [18F]BF4 −-PET was also highly specific as in all detected tissues cancer cell presence was confirmed microscopically. Undetected comparable tissues were similarly found to be free of metastasis. Metastasis detection by routine metabolic imaging with [18F]FDG-PET failed due to low standard uptake values and low contrast caused by adjacent metabolically active organs in this model. [18F]BF4 −-PET combined with NIS expressing disease models is particularly useful whenever preclinical in vivo cell tracking is of interest.