ICOS-expressing Regulatory T Cells Influence the Composition of Antitumor CTL Populations
Bibliographic record
Abstract
The role of ICOS in antitumor T cell responses and overall tumor progression has been controversial. In this study, we compared tumor progression in mice lacking ICOS selectively in regulatory T (Treg) cells or in all T cells. Using an experimental melanoma lung metastasis model, we found that Treg cell-specific ICOS knockout reduces the overall tumor burden compared with Cre control mice, with increased CD4+-to-Treg cell and CD8+-to-Treg cell ratios in the tumor. In contrast, there was no difference in the tumor burden in mice lacking ICOS in all of the T cell compartments. This suggests a dual role of ICOS costimulation in promoting protumor and antitumor T cell responses. Consistent with reduced tumor burden, we found that Treg cell-specific deletion of ICOS leads to an increase of CD8+ CTLs that express high levels of granzyme B and perforin. Moreover, single-cell transcriptome analysis revealed an increase of Ly108+Eomeshi CD8+ T cells at the cost of the Ly108+T-bethi subset in Treg cell-specific knockout mice. These results suggest that ICOS-expressing Treg cells suppress the CTL maturation process at the level of Eomes upregulation, a critical step known to drive perforin expression and cytotoxicity. Collectively, our data imply that cancer immunotherapies using ICOS agonist Abs may work better in Treg cell-low tumors or when they are combined with regimens that deplete tumor-infiltrating Treg cells.
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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".