{"doi":"10.52601/bpr.2025.250034","title":"Nanobody PET tracer enables GPA33 expression profiling for precision diagnosis of colorectal cancer","abstract":"Colorectal cancer (CRC) remains a leading cause of cancer mortality, highlighting the need for precise molecular imaging tools targeting biomarkers like glycoprotein A33 (GPA33), which is highly expressed in over 95% of CRC but currently lacks an ideal non-invasive probe for rapid clinical translation. This study developed a GPA33-targeted nanobody tracer, <sup>68</sup>GaGa-NOTA-WWH347, for PET imaging of CRC by engineering and radiolabeling a specific nanobody WWH347 with Ga-68. Western blot confirmed higher GPA33 expression in LS174T cells versus HT29 controls. <i>In vitro</i> cell uptake assays demonstrated specific tracer accumulation in LS174T cells (10.27 ± 0.45% at 2 h) versus negative HT29 cells (1.13 ± 0.14%) or LS174T blocking groups (0.96 ± 0.55%). The novel tracer <sup>68</sup>GaGa-NOTAWWH347 enabled superior PET visualization of CRC tumors in subcutaneous, liver metastasis, and systemic metastasis models, clearly outperforming both <sup>18</sup>FF-FDG and the non-specific probe <sup>68</sup>GaGa-NOTA-5D5. Subsequent biodistribution studies revealed significantly higher tracer uptake in subcutaneous LS174T tumors (10.45 ± 0.78%ID/g) compared to HT29 (3.29 ± 0.61%ID/g). In addition, animal models with liver metastases showed tracer uptake at 11.69 ± 2.69%ID/g, while 9.71 ± 1.01%ID/g in systemic metastatic lesions, enabling non-invasive visualization of GPA33 expression profiles on the whole-body level. These findings establish <sup>68</sup>GaGa-NOTA-WWH347 as a precision diagnostic tool that could optimize GPA33-targeted therapies by mapping expression patterns in CRC primary tumors and metastatic lesions through rapid, specific PET imaging.","journal":"Biophysics Reports","year":2025,"id":536878,"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":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9516,"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":1422701,"name":"Zhidie Huang","orcid":null,"position":1,"is_corresponding":false},{"id":1422702,"name":"Shaowen Yang","orcid":null,"position":2,"is_corresponding":false},{"id":1422703,"name":"Sixuan Cheng","orcid":null,"position":3,"is_corresponding":false},{"id":1422197,"name":"Wenbo Li","orcid":"0000-0002-4988-9766","position":4,"is_corresponding":false},{"id":1422198,"name":"Hao Yang","orcid":"0000-0002-6997-6963","position":5,"is_corresponding":false},{"id":244584,"name":"Dawei Jiang","orcid":"0000-0002-4072-0075","position":6,"is_corresponding":false},{"id":483181,"name":"Weijun Wei","orcid":"0000-0003-3190-2480","position":7,"is_corresponding":false},{"id":244592,"name":"Weibo Cai","orcid":"0000-0003-4641-0833","position":8,"is_corresponding":false},{"id":1422196,"name":"Chengwen Zheng","orcid":"0000-0002-2633-643X","position":0,"is_corresponding":true}],"reference_count":39,"raw_metadata":null,"created_at":"2026-07-19T02:52:09.056872Z","pmid":null,"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":[]}