CD4 cells can be more efficient at tumor rejection than CD8 cells
Bibliographic record
Abstract
Researchers designing antitumor treatments have long focused on eliciting tumor-specific CD8 cytotoxic T lymphocytes (CTL) because of their potent killing activity and their ability to reject transplanted organs. The resulting treatments, however, have generally been surprisingly poor at inducing complete tumor rejection, both in experimental models and in the clinic. Although a few scattered studies suggested that CD4 T "helper" cells might also serve as antitumor effectors, they have generally been studied mostly for their ability to enhance the activity of CTL. In this mouse study, we compared monoclonal populations of tumor-specific CD4 and CD8 T cells as effectors against several different tumors, and found that CD4 T cells eliminated tumors that were resistant to CD8-mediated rejection, even in cases where the tumors expressed major histocompatibility complex (MHC) class I molecules but not MHC class II. MHC class II expression on host tissues was critical, suggesting that the CD4 T cells act indirectly. Indeed, the CD4 T cells partnered with NK cells to obtain the maximal antitumor effect. These findings suggest that CD4 T cells can be powerful antitumor effector cells that can, in some cases, outperform CD8 T cells, which are the current "gold standard" effector cell in tumor immunotherapy.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".