{"doi":"10.1172/jci184964","title":"Tumor-specific surface marker–independent targeting of tumors through nanotechnology and bioorthogonal glycochemistry","abstract":"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 (NP-based), tumor-specific surface marker-independent (TRACER) targeting approach. Utilizing the unique biodistribution properties of NPs, we encapsulated Ac4ManNAz (Maz) to selectively label tumors with azide-reactive groups. Surprisingly, while NP-delivered Maz 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 Abs with dibenzocyclooctyne to target azide-labeled tumor cells and activate the immune response. In syngeneic B16F10 melanoma and orthotopic 4T1 breast cancer models, TRACER enhanced the therapeutic efficacy of anti-4-1BB, increasing the median survival time. Immunofluorescence analyses revealed increased tumor infiltration of CD8+ T and NK cells with TRACER. Importantly, TRACER reduced the hepatotoxicity associated with anti-4-1BB, resulting in normal serum ALT and AST levels and decreased CD8+ T cell infiltration into the liver. Quantitative analysis confirmed a 4.5-fold higher tumor-to-liver ratio of anti-4-1BB accumulation with TRACER compared with conventional anti-4-1BB Abs. 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 the liver toxicity associated with anti-4-1BB.","journal":"Journal of Clinical Investigation","year":2025,"id":550296,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9507,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":731753,"name":"Bo Sun","orcid":"0000-0002-5186-0727","position":1,"is_corresponding":false},{"id":273778,"name":"Mostafa Yazdimamaghani","orcid":"0000-0001-5090-9528","position":2,"is_corresponding":false},{"id":1366145,"name":"Albert R. Wielgus","orcid":null,"position":3,"is_corresponding":false},{"id":691961,"name":"Yue Wang","orcid":"0000-0003-4671-8547","position":4,"is_corresponding":false},{"id":105115,"name":"Stephanie A. Montgomery","orcid":"0000-0001-8012-5302","position":5,"is_corresponding":false},{"id":1445571,"name":"Tian Zhang","orcid":"0009-0002-2287-0392","position":6,"is_corresponding":false},{"id":602082,"name":"Jianjun Cheng","orcid":"0000-0003-2561-9291","position":7,"is_corresponding":false},{"id":262408,"name":"Jonathan S. Serody","orcid":"0000-0003-4568-1092","position":8,"is_corresponding":false},{"id":277905,"name":"Andrew Z. Wang","orcid":"0000-0002-9781-4494","position":9,"is_corresponding":false},{"id":292992,"name":"Hyesun Hyun","orcid":"0000-0001-8525-1471","position":0,"is_corresponding":true}],"reference_count":68,"raw_metadata":null,"created_at":"2026-07-19T02:54:16.596730Z","pmid":"40067370","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}