Precise Spatiotemporal Interruption of Regulatory T-cell–Mediated CD8+ T-cell Suppression Leads to Tumor Immunity
Bibliographic record
Abstract
Abstract Tumors can develop despite the presence of competent host immunity via a complex system of immune evasion. One of the most studied factors originating from the host is immune suppression by regulatory T cells (Treg). Ample laboratory and clinical evidence suggests that Treg ablation leads to robust antitumor immune activation. However, how Tregs specifically achieve their suppression in the context of tumor progression is not entirely clear, particularly with regard to the timing and location where Treg inhibition takes place. In this work, we report that Tregs migrate to tumor-draining lymph nodes (TDLN) and block expression of sphingosine-1-phosphate receptor 1 (S1P1) on CD8+ T cells. This event trapped the CD8+ T cells in the TDLN and served as a facilitating factor for tumor growth. Intriguingly, minimalistic depletion of Tregs in TDLN in a short window following tumor inoculation was sufficient to restore CD8+ T-cell activities, which resulted in significant tumor reduction. Similar treatments outside this time frame had no such effect. Our work therefore reveals a subtle feature in tumor biology whereby Tregs appear to be driven by newly established tumors for a programmed encounter with newly activated CD8+ T cells in TDLN. Our results suggest the possibility that clinical interception of this step can be tested as a new strategy of cancer therapy, with expected high efficacy and low systemic side effects. Significance: These findings reveal a strong tumor suppressive effect invoked by minimal blockade of tumor draining lymph node regulatory T cells during early versus late tumorigenesis.
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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".