The Role of Cbl-b in CD4+ T Cell Resistance to Regulatory T Cell Suppression
Bibliographic record
Abstract
Abstract Cancer immunotherapy has demonstrated enormous potential as a novel therapeutic modality in the management of multiple malignancies, including melanoma and leukemia. While cancer immunotherapy is designed to enhance effector T cell function and promote tumor destruction, one of its limitations is the negative influence of tumour-resident CD4+ FoxP3+ regulatory T (Treg) cells on T cell anti-tumour activity. Previous attempts to overcome this challenge have largely focused on the depletion of Treg cells, but this has proven to be a challenging and largely ineffectual process. An alternative approach to overcoming this problem involves generating effector T cell resistance against Treg cell-mediated suppression. Work in this area to date has shown that deficiency in the E3 ubiquitin ligase, Cbl-b, in mice results in the development of hyper-proliferative and pro-inflammatory effector T cells, even in the presence of Treg cells. This study investigates the cellular mechanisms behind this Treg resistance using Cbl-b deficient CD4+ FoxP3− and CD8+ T cells. We demonstrate that Cbl-b −/− CD4+ FoxP3− T cells exhibit both hyper-secretion of and hyper-sensitivity to IL-2, and that this serves as an important mechanism to escape suppression by Treg cells. Accordingly, we show that the blockade of IL-2Rα is sufficient to reverse the Treg cell-mediated suppression and excess IL-2 alone is sufficient to override suppressive signals by Treg cells. Overall, this study provides further insight into the potential development of Treg resistant effector T cells able to generate a more robust anti-tumor immune response.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".