Suppression of Antitumor Immunity by IL-10 and TGF-β-Producing T Cells Infiltrating the Growing Tumor: Influence of Tumor Environment on the Induction of CD4+ and CD8+ Regulatory T Cells
Bibliographic record
Abstract
We examined the hypothesis that a failure of the immune system to eradicate tumors is due to the immunosuppressive environment created by the growing tumor, which is influenced by the site of tumor growth. We demonstrated that T cell responses to a bystander Ag in mice were suppressed by a growing CT26 tumor. T cells purified from the growing tumor expressed mRNA for IL-10, TGF-beta, and Foxp3. Intracellular cytokine staining revealed a high frequency of IL-10-secreting macrophages, dendritic cells, and CD4+ and CD8+ T cells infiltrating the tumor. In contrast, T cell IFN-gamma production was weak and CD8+ CTL responses were undetectable in mice with CT26 lung metastases and weak and transient following s.c. injection of CT26 cells, but were enhanced in the presence of anti-IL-10 and anti-TGF-beta. Consistent with this, removal of CD8+ T cells abrogated CTL responses and promoted progression of the s.c. tumor. However, in the lung model, depletion of CD8+ T cells significantly reduced the tumor burden. Furthermore, depletion of CD4+ or CD25+ T cells in vivo reduced tumor burden in s.c. and lung models, and this was associated with significantly enhanced IFN-gamma production by CD8+ T cells. These findings suggest that tumor growth facilitates the induction or recruitment of CD4+ regulatory T cells that secrete IL-10 and TGF-beta and suppress effector CD8+ T cell responses. However, CD8+ T regulatory cells expressing IL-10 and TGF-beta are also recruited or activated by the immunosuppressive environment of the lung, where they may suppress the induction of antitumor immunity.
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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) | 0.001 | 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".