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SCIENCE CHINA Materials, Volume 61, Issue 8: 1101-1111(2018) https://doi.org/10.1007/s40843-017-9223-6

Real-time monitoring of tumor vascular disruption induced by radiofrequency assisted gadofullerene

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  • ReceivedDec 23, 2017
  • AcceptedJan 29, 2018
  • PublishedFeb 11, 2018

Abstract

The anti-vascular therapy has been extensively studied for high performance tumor therapy by suppressing the tumor angiogenesis or cutting off the existing tumor vasculature. We have previously reported a novel anti-tumor treatment technique using radiofrequency (RF)-assisted gadofullerene nanocrystals (GFNCs) to selectively disrupt the tumor vasculature. In this work, we further revealed the changes on morphology and functionality of the tumor vasculature during the high-performance RF-assisted GFNCs treatment in vivo. Here, a clearly evident mechanism of this technique in tumor vascular disruption was elucidated. Based on the H22 tumor bearing mice with dorsal skin flap chamber (DSFC) model and the dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) technique, it was revealed that the GFNCs would selectively inset in the gaps of tumor vasculature due to the innately incomplete structures and unique microenvironment of tumor vasculature, and they damaged the surrounding endothelia cells excited by the RF to induce a phase transition accompanying with size expansion. Soon afterwards, the blood flow of the tumor blood vessels was permanently shut off, causing the entire tumor vascular network to collapse within 24 h after the treatment. The RF-assistant GFNCs technique was proved to aim at the tumor vasculature precisely, and was harmless to the normal vasculature. The current studies provide a rational explanation on the high efficiency anticancer activity of the RF-assisted GFNCs treatment, suggesting a novel technique with potent clinical application.


Funded by

the National Natural Science Foundation of China(51472248,51502301)

National Major Scientific Instruments and Equipments Development Project(ZDYZ2015-2)

and the Key Research Program of the Chinese Academy of Sciences(QYZDJ-SSW-SLH025)


Acknowledgment

This work was supported by the National Natural Science Foundation of China (51472248 and 51502301), National Major Scientific Instruments and Equipments Development Project (ZDYZ2015-2), and the Key Research Program of the Chinese Academy of Sciences (QYZDJ-SSW-SLH025). We thank Zhentao Zuo for the design of the radiofrequency generator and the help by the State Key Lab of Brain & Cognitive Sciencesin Institute of Biophysics, Chinese Academy of Sciences.


Interest statement

The authors declare that they have no conflict of interest.


Contributions statement

Deng R, Zhen M and Wang C designed the devices and experiments; Li J, Yu T and Zhou Y synthesized the GFNCs; Deng R, Li X and Shu C analyzed the nature of GFNCs; Deng R and Zou T performed the DSFC experiments; Deng R, Wang Y and Xu H performed the DCE-MRI experiments; Deng R, Wang Y and Wang C analyzed the data; Deng R wrote the paper with support from Wang C and Zhen M. All authors contributed to the general discussion.


Author information

Ruijun Deng was born in 1991. She received her bachelor degree from Hebei University. She is currently pursuing her PhD degree in physical chemistry from the Institute of Chemistry, Chinese Academy of Sciences. Her main research includes the biomedical application of the fullerenes and gadofullerenes.


Yuqing Wang was born in 1980. He received his PhD degree in biomedical engineering from the University of Electronic Science and Technology in 2011. Currently, he is an engineer at the National Center for Nanoscience and Technology. His research interests refer to medical imaging processing in MR.


Mingming Zhen was born in 1987. She received her PhD degree in physical chemistry from the Institute of Chemistry, Chinese Academy of Sciences in 2014. Currently, she is an assistant professor at the Institute of Chemistry, Chinese Academy of Sciences. Her research interests include biomedical applications of fullerenes and gadofullerenes.


Chunru Wang was born in 1965. He received his PhD degree in physical chemistry from Dalian Institute of Chemistry Physics, Chinese Academy of Sciences in 1992. Currently, he is a professor at the Institute of Chemistry, Chinese Academy of Sciences. His research interests include fullerenes and endohedral fullerenes, mainly focusing on their industrialization and applications. He discovered the metal carbide fullerenes for the first time, researched on high efficiency MRI contrast agents and developed a novel tumor vascular-targeting therapy technique using gadofullerenes.


Supplement

Supplementary information

Supporting data are available in the online version of the paper.


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  • Figure 1

    The physiochemical characterizations of GFNCs. (a) Chromatography of the purified GFNCs on a Cosmosil column (4.6×250 mm, flow rate 1.0 mg mL−1, 90% acetonitrile in water as the eluent). The retention time of GFNCs was 4.3 min achieving a good symmetrical peak shape. (b) The FTIR spectrum of GFNCs. (c) AFM image of GFNCs aggregates. The average height and diameter were approximately 30 and 80 nm, respectively. (d) The measured hydrodynamic size distribution of GFNCs in ultrapure water by DLS. The size was 157.2 nm.

  • Figure 2

    Short-time observation of the tumor vasculature during RF-assisted GFNCs treatment in DSFC model in vivo. (a) The first day and the 7th day after H22 tumor inoculation in DSFC. The white dot line and white arrows indicated the approximate border of tumor and tumor angiogenesis, respectively. The scale bar is 300 μm. (b) The RF coil is combined with the DSFC model in the back to irradiate the corresponding RF to trigger the GFNCs treatment. The black arrows indicated the solid tumor tissues in back of DSFC. (c) The fluorescence images of vasculature in RF-control, treatment in tumor and normal tissue (n=3). In the treatment group, the most majority of tumor vasculature was out of circulations at 15 min of treatment compared to the respective controls. The scale bar is 50 μm.

  • Figure 3

    Morphological and functional observation of the tumor vascular disruption by RF-assisted GFNCs treatment. (a) Real-time observations of the typical tumor vascular destruction by RF-assistant GFNCs treatment at separate time points obtained at 40× magnification in the DSFC model. The yellow dotted lines and the black ones indicate the approximate border of tumor and ROI of tumor vasculature, respectively. The scale bar is 300 μm. (b) The amplification of three representative ROIs of tumor vasculatures in DSFC. The white dotted circles indicate the tumor vasculature impacted by RF-assistant GFNCs treatment. The scale bar is 100 μm. (c) Fluorescence imaging of rhodamine B isothiocyanate-dextran (70 kDa, 150 μL, 5 mg mL−1) by intravenous injection shows decrease of blood vessels in tumor (n=3) within 48 h after treatment. Yellow arrows indicate the tumor vasculature perfused by rhodamine B dextran. Scale bar is 50 μm.

  • Figure 4

    The signal and concentration of Gd-DTPA in duration of DCE-MRI in periphery of H22 tumor. (a, b) The procedure of DCE-MRI experiment of RF-Control (a) and Treatment (b) (n=3). (c, d) DCE-MRI in the duration of Gd-DTPA injection at different treatment time points in RF-control (c) and Treatment (d). The enhancement of MRI signal in tumor before and after contrast agent injection in treatment was obviously smaller than that in RF-control. The yellow dotted circles indicate the location of tumor. (e, f) The concentration of Gd-DTPA in periphery of H22 tumor in duration of DCE-MRI in RF-control (e) and Treatment (f) at different treatment times. The DCE-MRI data were converted into the concentration of the contrast agent using a different flip angle method.

  • Figure 5

    Hemodynamic parameter Ktrans of tumor in DCE-MRI at the pre-treatment, 4th, 24th and 48th h after treatment. (a) ΔKtrans (relative change in Ktrans between before and after treatment) of the periphery of H22 tumors (n=3) in RF-control and Treatment at the 4th, 24th and 48th h of post-treatment (ΔKtrans = (Ktrans(t)—Ktrans(pre))/Ktrans(pre) × 100%). These data were expressed as mean±SD. (b) Ktrans map of H22 tumor for RF-control and Treatment at the pre-treatment, 4th, 24th and 48th h after treatment. Colored parts and gray parts are the manually-defined tumor regions and the normal muscle tissue beside tumor, respectively.

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