Acquisition of suppressive function by conventional T cells limits antitumor immunity upon T <sub>reg</sub> depletion
Bibliographic record
Abstract
Regulatory T (T reg ) cells contribute to immune homeostasis but suppress immune responses to cancer. Strategies to disrupt T reg cell–mediated cancer immunosuppression have been met with limited clinical success, but the underlying mechanisms for treatment failure are poorly understood. By modeling T reg cell–targeted immunotherapy in mice, we find that CD4 + Foxp3 − conventional T (T conv ) cells acquire suppressive function upon depletion of Foxp3 + T reg cells, limiting therapeutic efficacy. Foxp3 − T conv cells within tumors adopt a T reg cell–like transcriptional profile upon ablation of T reg cells and acquire the ability to suppress T cell activation and proliferation ex vivo. Suppressive activity is enriched among CD4 + T conv cells marked by expression of C-C motif receptor 8 (CCR8), which are found in mouse and human tumors. Upon T reg cell depletion, CCR8 + T conv cells undergo systemic and intratumoral activation and expansion, and mediate IL-10–dependent suppression of antitumor immunity. Consequently, conditional deletion of Il10 within T cells augments antitumor immunity upon T reg cell depletion in mice, and antibody blockade of IL-10 signaling synergizes with T reg cell depletion to overcome treatment resistance. These findings reveal a secondary layer of immunosuppression by T conv cells released upon therapeutic T reg cell depletion and suggest that broader consideration of suppressive function within the T cell lineage is required for development of effective T reg cell–targeted therapies.
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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 | 0.001 |
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".