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Preclinical PET imaging study of lung cancer with 64CuCl2

Abstract

Objective

Human copper transporter 1 (CTR1) has been proven to be overexpressed in many types of cancer cells, and copper (II)-64 chloride (64CuCl2) has been used as an effective tracer for positron emission tomography (PET) imaging in tumor-bearing animal models. Thus, this study aimed to investigate the potential application of 64CuCl2 in PET imaging of lung cancer through targeting CTR1.

Methods

The expression of CTR1 in a series of lung cancer cell lines was identified by quantitative real-time polymerase chain reaction (Q-PCR), western blot, enzyme-linked immunosorbnent assay (ELISA), and immunofluorescent staining. Then in vitro cell uptake assay of 64CuCl2 was investigated in human lung cancer cell lines with different levels of CTR1 expression. Small animal PET imaging and quantitative analysis were performed in human lung cancer tumor-bearing mice after intravenous injection of 64CuCl2, respectively.

Results

The CTR1 expression in multiple human lung cancer cells was identified and confirmed, and H1299 cell lines with high CTR1 expression, H460 with moderate CTR1, and H1703 with low CTR1 were selected for further experiments. In vitro cellular uptake assay displayed that the 64CuCl2 uptake by these three kinds of cells was positively correlated with their CTR1 expressed levels. The blocking experiments testified the specificity of 64CuCl2 to target CTR1. Moreover, small animal PET imaging and quantitative results showed that 64CuCl2 accumulation in H1299, H460, and H1703 tumor-bearing mice were consistent with CTR1 levels and cell uptake experiments.

Conclusions

The expression of CTR1 in human lung cancer xenograft model could be successfully visualized by 64CuCl2 PET examination. With the expected growth of PET/CT examination to be an essential strategy in clinical lung cancer management, 64CuCl2 has the potential to be a promising PET imaging agent of lung cancer.

Cited products
Source:Annals of Nuclear Medicine     by Q Wang, DL Song, XW Ma, et al.
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