USP22 stabilizes FoxP3 to maintain Regulatory T cell function through a dual mechanism resulting in tumor immune evasion
Bibliographic record
Abstract
Abstract Aggressive cancers are less sensitive to standard cancer treatments available, reducing overall survival rates and increasing relapse percentage in cancer patients. As a result, there is an increasing need for alternative therapeutics that will prolong the survival of patients. Within the last decade, methods to attenuate tumor immune evasion have become a centerpiece of tumor therapies. However, a major hurdle in tumor immunotherapy is the immunosuppression mediated by Regulatory T (Treg) cells, which can promote tumor progression through the suppression of tumor immune evasion. Therefore, the ability to modulate Treg function in the context of cancer could lead to more effective therapies. Here, we identify the ubiquitin-specific peptidase 22 (USP22), a member of the deubiquitination module of the SAGA chromatin modifying complex, as a specific regulator of Foxp3, the lineage-specifying transcription factor of Tregs. Treg-specific ablation of Usp22 in mice reduced Foxp3 at both the transcriptional and post-translational level, creating defects in their suppressive function and leading to spontaneous autoimmunity. Importantly, USP22 ablation protected against tumor growth in multiple cancer models. Collectively, our findings reveal a previously underappreciated physiological function of USP22 in maintaining Treg stability and identify USP22 as a potential target for cancer immunotherapy.
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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".