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. 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".