CD8+ T Cell Immunity Against a Tumor/Self-Antigen Is Augmented by CD4+ T Helper Cells and Hindered by Naturally Occurring T Regulatory Cells
Bibliographic record
Abstract
CD4(+) T cells control the effector function, memory, and maintenance of CD8(+) T cells. Paradoxically, we found that absence of CD4(+) T cells enhanced adoptive immunotherapy of cancer when using CD8(+) T cells directed against a persisting tumor/self-Ag. However, adoptive transfer of CD4(+)CD25(-) Th cells (Th cells) with tumor/self-reactive CD8(+) T cells and vaccination into CD4(+) T cell-deficient hosts induced autoimmunity and regression of established melanoma. Transfer of CD4(+) T cells that contained a mixture of Th and CD4(+)CD25(+) T regulatory cells (T(reg) cells) or T(reg) cells alone prevented effective adoptive immunotherapy. Maintenance of CD8(+) T cell numbers and function was dependent on Th cells that were capable of IL-2 production because therapy failed when Th cells were derived from IL-2(-/-) mice. These findings reveal that Th cells can help break tolerance to a persisting self-Ag and treat established tumors through an IL-2-dependent mechanism, but requires simultaneous absence of naturally occurring T(reg) cells to be effective.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".