Immunotherapeutic Targeting and PET Imaging of DLL3 in Small-Cell Neuroendocrine Prostate Cancer
Bibliographic record
Abstract
Surface protein targeting therapies, such as antibody-drug conjugates (ADCs), bispecific T cell engagers (BiTEs) and chimeric antigen receptor (CAR) T cells are an exciting emerging class of cancer therapies. In this talk, we will discuss how to harness these surface proteins to build effective therapeutic and imaging agents, and how to modulate their expression in cancer cells to improve targeting efficacy. Using DLL3 as an example target in small-cell/neuroendocrine prostate cancer (SCNC), we will demonstrate how AMG 757 (tarlatamab), a half-life extended BiTE immunotherapy, redirects CD3-positive T cells to kill DLL3-expressing patient-derived xenografts (PDX) models of SCNC and provide long-term, durable tumor control in mouse models. We will explore how heterogeneity of DLL3 expression impacts response to AMG 757 and provide data on how PET imaging agents may help us with patient selection, as these therapies enter into clinical trials. Finally, we will explore methods to prime tumors and enhance surface protein target expression, which can lead to improved tumor control.
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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) | 0.002 | 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".