Tackling Prostate Cancer with Theranostic E5B9-Bombesin Target Modules (TMs): From Imaging to Treatment with UniCAR T-Cells
Bibliographic record
Abstract
Target modules (TMs), intermediate molecules required for UniCAR T-cell therapy, are promising molecules for immunotheranostic approaches. In the current work, we developed TMs containing a monomeric or dimeric form of the antagonist bombesin peptide (BBN2) and assessed their potential for diagnostic imaging using positron emission tomography (PET) as well as immunotherapy in combination with UniCAR T-cells to target and image GRPR expression in prostate cancer. Synthesized monomeric and dimeric BBN2 TMs retained binding to GRPR in vitro. Both BBN2 TMs specifically activated and redirected UniCAR T-cells to eradicate PC3 and LNCaP cancer cells with high efficiency and in a comparable manner. UniCAR T-cells retained a non-exhausted memory phenotype favorable to their persistence and fitness. The 68Ga-labeled BBN2 TMs showed proof-of-target towards GRPR in PC3 and LNCaP xenografts with similar uptake profiles for both BBN2 TMs in dynamic PET experiments. Clearance occurred exclusively through renal elimination. A tremendously increased in vivo metabolic stability of the BBN2 TMs was observed compared to their counterparts without E5B9. Both monomeric and dimeric BBN2 TMs represent novel and promising immunotheranostic tools for application in prostate cancer with exceptionally high in vivo metabolic stability.
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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".