Immunotherapeutic Targeting and PET Imaging of DLL3 in Small-Cell Neuroendocrine Prostate Cancer
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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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".