Transient Treg depletion enhances therapeutic anti‐cancer vaccination
Bibliographic record
Abstract
INTRODUCTION: Regulatory T cells (Treg) play an important role in suppressing anti- immunity and their depletion has been linked to improved outcomes. To better understand the role of Treg in limiting the efficacy of anti-cancer immunity, we used a Diphtheria toxin (DTX) transgenic mouse model to specifically target and deplete Treg. METHODS: Tumor bearing BALB/c FoxP3.dtr transgenic mice were subjected to different treatment protocols, with or without Treg depletion and tumor growth and survival monitored. RESULTS: DTX specifically depleted Treg in a transient, dose-dependent manner. Treg depletion correlated with delayed tumor growth, increased effector T cell (Teff) activation, and enhanced survival in a range of solid tumors. Tumor regression was dependent on Teffs as depletion of both CD4 and CD8 T cells completely abrogated any survival benefit. Severe morbidity following Treg depletion was only observed, when consecutive doses of DTX were given during peak CD8 T cell activation, demonstrating that Treg can be depleted on multiple occasions, but only when CD8 T cell activation has returned to base line levels. Finally, we show that even minimal Treg depletion is sufficient to significantly improve the efficacy of tumor-peptide vaccination. CONCLUSIONS: BALB/c.FoxP3.dtr mice are an ideal model to investigate the full therapeutic potential of Treg depletion to boost anti-tumor immunity. DTX-mediated Treg depletion is transient, dose-dependent, and leads to strong anti-tumor immunity and complete tumor regression at high doses, while enhancing the efficacy of tumor-specific vaccination at low doses. Together this data highlight the importance of Treg manipulation as a useful strategy for enhancing current and future cancer immunotherapies.
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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".