Val-BoroPro Accelerates T Cell Priming via Modulation of Dendritic Cell Trafficking Resulting in Complete Regression of Established Murine Tumors
Bibliographic record
Abstract
Although tumors naturally prime adaptive immune responses, tolerance may limit the capacity to control progression and can compromise effectiveness of immune-based therapies for cancer. Post-proline cleaving enzymes (PPCE) modulate protein function through N-terminal dipeptide cleavage and inhibition of these enzymes has been shown to have anti-tumor activity. We investigated the mechanism by which Val-boroPro, a boronic dipeptide that inhibits post-proline cleaving enzymes, mediates tumor regression and tested whether this agent could serve as a novel immune adjuvant to dendritic cell vaccines in two different murine syngeneic murine tumors. In mice challenged with MB49, which expresses the HY antigen complex, T cell responses primed by the tumor with and without Val-boroPro were measured using interferon gamma ELISPOT. Antibody depletion and gene-deficient mice were used to establish the immune cell subsets required for tumor regression. We demonstrate that Val-boroPro mediates tumor eradication by accelerating the expansion of tumor-specific T cells. Interestingly, T cells primed by tumor during Val-boroPro treatment demonstrate increased capacity to reject tumors following adoptive transfer without further treatment of the recipient. Val-boroPro -mediated tumor regression requires dendritic cells and is associated with enhanced trafficking of dendritic cells to tumor draining lymph nodes. Finally, dendritic cell vaccination combined with Val-boroPro treatment results in complete regression of established tumors. Our findings demonstrate that Val-boroPro has antitumor activity and a novel mechanism of action that involves more robust DC trafficking with earlier priming of T cells. Finally, we show that Val-boroPro has potent adjuvant properties resulting in an effective therapeutic vaccine.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".