Esophageal squamous cell carcinoma (ESCC) frequently exhibits skip metastasis to lymph nodes; however, non-invasive imaging techniques capable of directly visualizing metastatic lymph nodes (MLN) are still lacking. Although biopsy is the clinical standard method, it is invasive and poses risks to patient health. This study aims to detect MLN in an intralymphatic tumor metastasis model of ESCC using the CXCR4-targeted tracer [64Cu]Cu-NOTA-CP01. The CXCR4 expression in ESCC cell lines was assessed using Western blot and immunofluorescence. An intralymphatic tumor metastasis model was established and monitored using bioluminescence imaging (BLI). Small animal PET studies and biodistribution studies were performed to evaluate the specificity of [64Cu]Cu-NOTA-CP01 for MLN. Histopathology evaluation was employed to check for the presence of metastatic tumor cells and to assess CXCR4 expression levels in the metastatic lymph nodes. The intralymphatic tumor metastasis model was successfully established using the EC109/Luc cell line, which exhibited high CXCR4 expression, as verified by BLI. PET/CT imaging showed that the MLN uptakes in the baseline group were significantly inhibited in the blocking group. The ratios of MLN/muscle and MLN/blood werealso significantly higher in the baseline group than in the blocking group. Ex vivo PET/CT imaging of MLN corroborated the in vivo data. Biodistribution studies further supported the PET imaging studies, showing rapid clearance of the tracer from the blood and major organs, with significantly higher MLN/muscle and MLN/blood ratios in the baseline group compared to the blocking group. Histopathological staining verified positive CXCR4 expression in these lymph nodes containing metastatic tumor cells. Targeting CXCR4 with [64Cu]Cu-NOTA-CP01 for PET imaging of lymph nodes metastasis represents a promising approach that warrants further investigation. These findings have the potential to enhance diagnostic and therapeutic strategies for individuals with lymph nodes metastasis of ESCC.
Read full abstract