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Data-Driven Design of Triple-Targeted Protein Nanoprobes for Multiplexed Imaging of Cancer Lymphatic Metastasis
Guodong Shen1, Xiaohua Jia2,3, Tianyi Qi4
1Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
Abstract:
Targeted imaging of cancer lymphatic metastasis remains challenging due to its highly heterogeneous molecular and phenotypic diversity. Herein, triple-targeted protein nanoprobes capable of specifically binding to three targets for imaging cancer lymphatic metastasis, through a data-driven design approach combined with a synthetic biology-based assembly strategy, are introduced. Specifically, to address the diversity of metastatic lymph nodes (LNs), a combination of three targets, including C-X-C motif chemokine receptor 4 (CXCR4), transferrin receptor protein 1 (TfR1), and vascular endothelial growth factor receptor 3 (VEGFR3) is identified, leveraging machine leaning-based bioinformatics analysis and examination of LN tissues from patients with gastric cancer. Using this identified target combination, ferritin nanocage-based nanoprobes capable of specifically binding to all three targets are designed through the self-assembly of genetically engineered ferritin subunits using a synthetic biology approach. Using these nanoprobes, multiplexed imaging of heterogeneous metastatic LNs is successfully achieved in a polyclonal lymphatic metastasis animal model. In 19 freshly resected human gastric specimens, the signal from the triple-targeted nanoprobes significantly differentiates metastatic LNs from benign LNs. This study not only provides an effective nanoprobe for imaging highly heterogeneous lymphatic metastasis but also proposes a potential strategy for guiding the design of targeted nanomedicines for cancer lymphatic metastasis.
Insights
Researchers developed triple-targeted protein nanoprobes for imaging diverse cancer lymphatic metastasis. These nanoprobes successfully differentiate metastatic lymph nodes (LNs) in human gastric cancer specimens, offering a new tool for targeted cancer therapy.
Area of Science:
- Biomedical Engineering
- Nanotechnology
- Oncology
Background:
- Cancer lymphatic metastasis is challenging to image due to molecular heterogeneity.
- Existing imaging methods struggle with the diverse nature of metastatic lymph nodes (LNs).
Purpose of the Study:
- To develop novel triple-targeted protein nanoprobes for effective imaging of heterogeneous cancer lymphatic metastasis.
- To identify a combination of molecular targets for enhanced nanoprobe specificity and efficacy.
Main Methods:
- Data-driven design using machine learning-based bioinformatics analysis and patient LN tissue examination to identify targets.
- Synthetic biology-based assembly of ferritin nanocage nanoprobes engineered to bind three targets: CXCR4, TfR1, and VEGFR3.
- In vivo imaging in a polyclonal lymphatic metastasis animal model and ex vivo analysis of human gastric cancer specimens.
Main Results:
- Successfully designed and synthesized triple-targeted ferritin nanocage nanoprobes.
- Achieved multiplexed imaging of heterogeneous metastatic LNs in an animal model.
- Demonstrated significant differentiation between metastatic and benign LNs in human gastric cancer specimens.
Conclusions:
- The developed triple-targeted nanoprobes are effective for imaging highly heterogeneous lymphatic metastasis.
- This approach offers a potential strategy for designing targeted nanomedicines for cancer lymphatic metastasis.
- The nanoprobes show promise in distinguishing metastatic LNs in clinical samples.
