Foxp3+ Regulatory T Cell Depletion after Nonablative Oligofractionated Irradiation Boosts the Abscopal Effects in Murine Malignant Mesothelioma
Bibliographic record
Abstract
Abstract Increasing evidence indicates that local hypofractionated radiotherapy (LRT) can elicit both immunogenic and immunosuppressive local and systemic immune responses. We thus hypothesized that blockade of LRT-induced immunosuppressive responses could augment the antitumor effects and induce an abscopal response. In this study, we found that the upregulation of Foxp3+ regulatory T cells (Tregs) in the mesothelioma tumor microenvironment after nonablative oligofractionated irradiation significantly limited the success of irradiation. Using DEREG mice, which allow conditional and efficient depletion of Foxp3+ Tregs by diphtheria toxin injection, we observed that transient Foxp3+ Treg depletion immediately after nonablative oligofractionated irradiation provided synergistic local control and biased the T cell repertoire toward central and effector memory T cells, resulting in long-term cure. Furthermore, this combination therapy showed significant abscopal effect on the nonirradiated tumors in a concomitant model of mesothelioma through systemic activation of cytotoxic T cells and enhanced production of IFN-γ and granzyme B. Although local control was preserved with one fraction of nonablative irradiation, three fractions were required to generate the abscopal effect. PD-1 and CTLA-4 were upregulated on tumor-infiltrating CD4+ and CD8+ T cells in irradiated and nonirradiated tumors, suggesting that immune checkpoint inhibitors could be beneficial after LRT and Foxp3+ Treg depletion. Our findings are applicable to the strategy of immuno-radiotherapy for generating optimal antitumor immune responses in the clinical setting. Targeting Tregs immediately after a short course of irradiation could have a major impact on the local response to irradiation and its abscopal effect.
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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) | 0.001 | 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".