89Zr-Labeled AR20.5: A MUC1-Targeting ImmunoPET Probe
Bibliographic record
Abstract
High expression levels of the tumor-associated antigen MUC1 have been correlated with tumor aggressiveness, poor response to therapy, and poor survival in several tumor types, including breast, pancreatic, and epithelial ovarian cancer. Herein, we report the synthesis, characterization, and in vivo evaluation of a novel radioimmunoconjugate for the immuno-positron emission tomography (immunoPET) imaging of MUC1 expression based on the AR20.5 antibody. To this end, we modified AR20.5 with the chelator desferrioxamine (DFO) and labeled it with the positron-emitting radiometal zirconium-89 (t1/2 ~3.3 d) to produce [89Zr]Zr-DFO-AR20.5. In subsequent in vivo experiments in athymic nude mice bearing subcutaneous MUC1-expressing ovarian cancer xenografts, [89Zr]Zr-DFO-AR20.5 clearly delineated tumor tissue, producing a tumoral activity concentration of 19.1 ± 6.4 percent injected dose per gram (%ID/g) at 120 h post-injection and a tumor-to-muscle activity concentration ratio of 42.4 ± 10.6 at the same time point. Additional PET imaging experiments in mice bearing orthotopic MUC1-expressing ovarian cancer xenografts likewise demonstrated that [89Zr]Zr-DFO-AR20.5 enables the visualization of tumor tissue—including metastatic lesions—with promising tumor-to-background contrast.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".