Molecular Imaging of Prostate Cancer Targeting CD46 Using ImmunoPET
Bibliographic record
Abstract
Abstract Purpose: We recently identified CD46 as a novel therapeutic target in prostate cancer. In this study, we developed a CD46-targeted PET radiopharmaceutical, [89Zr]DFO-YS5, and evaluated its performance for immunoPET imaging in murine prostate cancer models. Experimental Design: [89Zr]DFO-YS5 was prepared and its in vitro binding affinity for CD46 was measured. ImmunoPET imaging was conducted in male athymic nu/nu mice bearing DU145 [AR−, CD46+, prostate-specific membrane antigen–negative (PSMA−)] or 22Rv1 (AR+, CD46+, PSMA+) tumors, and in NOD/SCID gamma mice bearing patient-derived adenocarcinoma xenograft, LTL-331, and neuroendocrine prostate cancers, LTL-331R and LTL-545. Results: [89Zr]DFO-YS5 binds specifically to the CD46-positive human prostate cancer DU145 and 22Rv1 xenografts. In biodistribution studies, the tumor uptake of [89Zr]DFO-YS5 was 13.3 ± 3.9 and 11.2 ± 2.5 %ID/g, respectively, in DU145 and 22Rv1 xenografts, 4 days postinjection. Notably, [89Zr]DFO-YS5 demonstrated specific uptake in the PSMA- and AR-negative DU145 model. [89Zr]DFO-YS5 also showed uptake in the patient-derived LTL-331 and -331R models, with particularly high uptake in the LTL-545 neuroendocrine prostate cancer tumors (18.8 ± 5.3, 12.5 ± 1.8, and 32 ± 5.3 %ID/g in LTL-331, LTL-331R, and LTL-545, respectively, at 4 days postinjection). Conclusions: [89Zr]DFO-YS5 is an excellent PET imaging agent across a panel of prostate cancer models, including in both adenocarcinoma and neuroendocrine prostate cancer, both cell line- and patient-derived xenografts, and both PSMA-positive and -negative tumors. It demonstrates potential for clinical translation as an imaging agent, theranostic platform, and companion biomarker in prostate cancer.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".