Detection and Characterization of Sentinel Lymph Node by Ultrasound Molecular Imaging with B7-H3-Targeted Microbubbles in Orthotopic Breast Cancer Model in Mice.

Abstract

Accurate identification and assessment of sentinel lymph node (SLN) using noninvasive imaging methods can play a vital role in tumor staging, surgical planning, and prognostic evaluation. In this study, we assessed the efficacy of B7-H3-targeted molecular-ultrasound imaging for the early SLN detection, and characterization in a mouse model of orthotopic breast cancer. We established a mouse breast cancer model with lymph node metastasis by injecting MAD-MB 231 cells which were engineered to express firefly luciferase reporter gene into the fat pad of the right 4th mammary gland in female BALB/c nude mice. The sole lymph node (LN) close to the tumor was regarded as the SLN for imaging investigation, which included metastatic and non-metastatic SLNs. The LN in the right 4th mammary gland from normal mice was used as normal control (normal mice LN). The commercially available preclinical streptavidin-coated, perfluorocarbon-containing lipid-shelled microbubbles (VisualSonics, Toronto, Canada) were used to generate B7-H3-targeted microbubbles (MBB7-H3) and control microbubbles (MBControl). Then, ultrasound molecular imaging (USMI) was performed using a high-resolution transducer (MS250; center frequency, 21MHz; Vevo 2100; VisualSonics, Toronto, Canada) after intravenous injection of microbubbles. The SLN was clearly detected and located under conventional (B-mode) and contrast-enhanced ultrasonography with microbubble injection. The metastatic SLNs showed a markedly higher signal from B7-H3-targeted microbubbles (MBB7-H3) compared to the non-metastatic SLNs and normal LNs. The metastatic SLN was further confirmed by ex vivo bioluminescence imaging and eventually verified by histological analysis. Our findings suggest the potential value of USMI using B7-H3 targeted microbubbles in breast cancer and establish an effective imaging method for the non-invasive detection and characterization of SLN.