Multimodal tomography of orthotopic prostate cancer with far-red fluorescent gene reporter using an optical/PET/CT scanner

Objectives The bioluminescence imaging provides longitudinal monitoring of expressed luciferase gene reporter. Although gene-encoded fluorescent reporters, such as Green Fluorescent Protein (GFP) and Red Fluorescent Protein (DsRed), offer opportunities for fluorescence imaging, strong tissue autofluorescence and high tissue absorption at visible light wavelengths limit the sensitivity of fluorescence molecular imaging. The recent availability of a far red excitable gene reporter, IFP1.4 (1), promises more sensitive fluorescence imaging. In this study, we perform near-infrared fluorescence, far red fluorescence/PET/CT imaging to image orthotopic prostate cancer.

Methods We developed a tri-modality (Optical/PET/CT) imaging system by integrating fluorescence imaging components into a Siemens Inveon scanner. Nude mice were orthotopically implanted with 10⁶ PC3 cells stably expressing IFP 1.4. After 6 weeks, fluorescence/PET/CT imaging was performed using a dual-labeled ((⁶⁴Cu-NODAGA)_n-anti-EpCAM-(IRDye 800)_m) antibody-based target agent. CT and PET scanning were conducted using standard protocols. Fluorescent distribution data at 830 and 710nm for IRDye 800 and IFP 1.4 respectively from different projections were acquired and mapped from the camera system onto the CT-derived surface using a pinhole camera model. A linear reconstruction algorithm with high-order simplified spherical harmonics approximations was developed to realize fluorescence tomography (2).

Results Although there are some localization errors (~2.0mm) compared to PET images and some reconstructed artifacts on the mouse surface, this study represents the first time that orthotopic prostate cancer imaging is realized by using far-red fluorescent gene reporter to the best of the authors’ knowledge.

Conclusions The results demonstrate the potential of far-red fluorescent gene reporter in imaging orthotopic prostate cancer and relevant metastasis.

Research Support This work is supported by NIH R01CA135673 and U54CA136404, and a training fellowship from the Keck Center Computational Cancer Biology Training Program of the Gulf Coast Consortia (CPRIT Grant No. RP101489)