Biological targeting is crucial for effective cancer treatment with reduced toxicity but is limited by the availability of tumor surface markers. To overcome this, we developed a nanoparticle-based, Tumor-specific suRfACE maRker-independent (TRACER) targeting approach. Utilizing the unique biodistribution properties of nanoparticles, we encapsulated Ac4ManNAz to selectively label tumors with azide reactive groups. Surprisingly, while NP-delivered Ac4ManNAz was cleared by the liver, it did not label macrophages, potentially reducing off-target effects. To exploit this tumor-specific labeling, we functionalized anti-4-1BB antibodies with dibenzocyclooctyne (DBCO) to target azide-labeled tumor cells and activate the immune response. In syngeneic B16F10 melanoma and orthotopic 4T1 breast cancer models, TRACER enhanced anti-4-1BB’s therapeutic efficacy, increasing median survival time. Immunofluorescence analyses revealed increased tumor infiltration of CD8+ T and NK cells with TRACER. Importantly, TRACER reduced hepatotoxicity associated with anti-4-1BB, resulting in normal serum ALT and AST levels and decreased CD8+ T cell infiltration in the liver. Quantitative analysis confirmed a 4.5-fold higher tumor-to-liver ratio of anti-4-1BB accumulation with TRACER compared to conventional anti-4-1BB antibodies. Our work provides a promising approach for developing targeted cancer therapies that circumvent limitations imposed by the paucity of tumor-specific markers, potentially improving efficacy and reducing off-target effects to overcome liver toxicity associated with anti-4-1BB.
Hyesun Hyun, Bo Sun, Mostafa Yazdimamaghani, Albert Wielgus, Yue Wang, Stephanie Ann Montgomery, Tian Zhang, Jianjun Cheng, Jonathan S. Serody, Andrew Z. Wang
Usage data is cumulative from March 2025 through March 2025.
Usage | JCI | PMC |
---|---|---|
Text version | 191 | 0 |
68 | 0 | |
Supplemental data | 13 | 0 |
Totals | 272 | 0 |
Total Views | 272 |
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.