Oncolytic Adenovirus Type 3 Coding for CD40L Facilitates Dendritic Cell Therapy of Prostate Cancer in Humanized Mice and Patient Samples
Bibliographic record
Abstract
Dendritic cell (DC)-based vaccines have shown some degree of success for the treatment of prostate cancer (PC). However, the highly immunosuppressive tumor microenvironment leads to DC dysfunction, which has limited the effectiveness of these vaccines. We hypothesized that use of a fully serotype 3 oncolytic adenovirus (Ad3-hTERT-CMV-hCD40L; TILT-234) could stimulate DCs in the prostate tumor microenvironment by expressing CD40L. Activated DCs would then activate cytotoxic T cells against the tumor, resulting in therapeutic immune responses. Oncolytic cell killing due to cancer cell-specific virus replication adds to antitumor effects but also enhances the immunological effect by releasing tumor epitopes for sampling by DC, in the presence of danger signals. In this study, we evaluated the companion effect of Ad3-hTERT-CMV-hCD40L and DC-therapy in a humanized mouse model and PC histocultures. Treatment with Ad3-hTERT-CMV-hCD40L and DC resulted in enhanced antitumor responses in vivo . Treatment of established histocultures with Ad3-hTERT-CMV-hCD40L induced DC maturation and notable increase in proinflammatory cytokines. In conclusion, Ad3-hTERT-CMV-hCD40L is able to modulate an immunosuppressive prostate tumor microenvironment and improve the effectiveness of DC vaccination in PC models and patient histocultures, setting the stage for clinical translation.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".