MRI detection of nonproliferative tumor cells in lymph node metastases using iron oxide particles in a mouse model of breast cancer.

Abstract

Cell tracking with magnetic resonance imaging (MRI) and iron nanoparticles is commonly used to monitor the fate of implanted cells in preclinical disease models. Few studies have employed these methods to study cancer cells because proliferative iron-labeled cancer cells will lose the label as they divide. In this study, we evaluate the potential for retention of the iron nanoparticle label, and resulting MRI signal, to serve as a marker for slowly dividing cancer cells. Green fluorescent protein-transfected MDA-MB-231 breast cancer cells were labeled with red fluorescent micron-sized superparamagnetic iron oxide (MPIO) nanoparticles. Cells were examined in vitro at multiple time points after labeling by staining for iron-labeled cells and by flow cytometric detection of the fluorescent MPIO. Severe combined immune deficiency (SCID) mice were implanted with 5 x 10(5) MPIO-labeled or unlabeled cells in the mammary fat pad and MRI was performed weekly until 28 days after injection. Microscopy was performed to validate MRI. In vitro assays revealed a very small percentage of cells that retained MPIO at 14 days after labeling. Regions of signal loss were observed in MRI of primary tumors that developed from iron-labeled cancer cells. Small focal regions of signal loss were detected in images of the axillary and brachial nodes in six of eight mice, at day 14 or later, with microscopy confirming the presence of iron-labeled cancer cells. Our data suggest an interesting role for cell tracking with iron particles since label retention leads to persistent signal void, allowing proliferative status to be determined.

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@article{Economopoulos2013MRIDO, title={MRI detection of nonproliferative tumor cells in lymph node metastases using iron oxide particles in a mouse model of breast cancer.}, author={Vasiliki Economopoulos and Yuhua Chen and Catherine D. McFadden and Paula j. Gareau Foster}, journal={Translational oncology}, year={2013}, volume={6 3}, pages={347-54} }