Tumor-Specific Zr-89 Immuno-PET Imaging in a Human Bladder Cancer Model
Bibliographic record
Abstract
Tumor-specific molecular imaging is an important tool for assessing disease burden and treatment response. CA19.9 is an important tumor-specific marker in several malignancies, including urothelial carcinoma. [ 89 Zr]DFO-HuMab-5B1 (MVT-2163) is a CA19.9-specific antibody-based construct that has been validated in preclinical animal models of lung, colorectal, and pancreatic malignancies for positron emission tomography (PET) imaging and is currently in a phase I trial for pancreatic cancer (NCT02687230). Here, we examine whether [ 89 Zr]DFO-HuMab-5B1 may be useful in defining urothelial malignancies. Surface expression of CA19.9 was confirmed in the human bladder cancer line HT 1197. The radioimmunoconjugate [ 89 Zr]DFO-HuMab-5B1 was injected into mice bearing HT 1197 xenografts, and followed by PET imaging, ex vivo experiments including biodistribution, histology and autoradiography, and analysis of blood samples for shed antigen levels were performed. [ 89 Zr]DFO-HuMab-5B1 specifically accumulates in HT 1197 engrafted tumors when imaged with PET. Ex vivo biodistribution of organs and autoradiography of engrafted tumors confirm our construct’s specific tumor binding. The target antigen CA19.9 was not found to be shed in vitro or in vivo . [ 89 Zr]DFO-HuMab-5B1 can be used to delineate urothelial carcinomas by PET imaging and may provide tumor-specific information prior to, during, and after systemic therapies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".