Processing of Tumor Antigen Differentially Impacts the Development of Helper and Effector CD4+ T-cell Responses
Bibliographic record
Abstract
CD4(+) T cells contribute to the antitumor T-cell response as both effectors that promote tumor rejection and helpers that facilitate the activation of other antitumor effector cells, such as CD8(+) T cells. Maximal engagement of both effector and helper CD4(+) T-cell responses is a desirable attribute of cancer vaccines. We have employed the B16F10 murine melanoma model and a series of recombinant adenovirus (Ad) vaccines expressing mutant forms of the tumor antigen, dopachrome tautomerase, to investigate the relationship between antigen processing and the antitumor CD4(+) T-cell response. Our results have revealed an unexpected dichotomy in the generation of helper and effector CD4(+) T-cell responses where CD4(+) T effector responses are dependent upon protein processing and trafficking, whereas CD4(+) T helper responses are not. The results have important implications for strategies aimed at augmenting antigen immunogenicity by altering intracellular processing and localization.
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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 | 0.001 | 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".