Immunotherapeutic Targeting and PET Imaging of DLL3 in Small-Cell Neuroendocrine Prostate Cancer
Why this work is in the frame
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Bibliographic record
Abstract
Effective treatments for de novo and treatment-emergent small-cell/neuroendocrine (t-SCNC) prostate cancer represent an unmet need for this disease. Using metastatic biopsies from patients with advanced cancer, we demonstrate that delta-like ligand 3 (DLL3) is expressed in de novo and t-SCNC and is associated with reduced survival. We develop a PET agent, [89Zr]-DFO-DLL3-scFv, that detects DLL3 levels in mouse SCNC models. In multiple patient-derived xenograft models, AMG 757 (tarlatamab), a half-life-extended bispecific T-cell engager (BiTE) immunotherapy that redirects CD3-positive T cells to kill DLL3-expressing cells, exhibited potent and durable antitumor activity. Late relapsing tumors after AMG 757 treatment exhibited lower DLL3 levels, suggesting antigen loss as a resistance mechanism, particularly in tumors with heterogeneous DLL3 expression. These findings have been translated into an ongoing clinical trial of AMG 757 in de novo and t-SCNC, with a confirmed objective partial response in a patient with histologically confirmed SCNC. Overall, these results identify DLL3 as a therapeutic target in SCNC and demonstrate that DLL3-targeted BiTE immunotherapy has significant antitumor activity in this aggressive prostate cancer subtype. SIGNIFICANCE: The preclinical and clinical evaluation of DLL3-directed immunotherapy, AMG 757, and development of a PET radiotracer for noninvasive DLL3 detection demonstrate the potential of targeting DLL3 in SCNC prostate cancer.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it