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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".