Induction of HLA-DP4–Restricted Anti-Survivin Th1 and Th2 Responses Using an Artificial Antigen-Presenting Cell
Bibliographic record
Abstract
PURPOSE: In previous cancer vaccine clinical trials targeting survivin, induction of specific CD8(+) T-cell responses did not consistently lead to clinical responses. Considering the critical role of CD4(+) T-cell help in generating antitumor immunity, integration of anti-survivin CD4(+) T-cell responses may enhance the efficacy of anti-survivin cancer immunotherapy. Human leukocyte antigen (HLA)-DP4 is emerging as an attractive MHC target allele of CD4(+) T cell-mediated immunotherapy, because it is one of the most frequent HLA alleles in many ethnic groups. In this article, we aimed to elucidate DP4-restricted CD4(+) T-cell responses against survivin in cancer patients. EXPERIMENTAL DESIGN: We generated a human cell-based artificial antigen-presenting cell (aAPC) expressing HLA-DP4, CD80, and CD83 and induced DP4-restricted antigen-specific CD4(+) T cells. The number, phenotype, effector function, and in vitro longevity of generated CD4(+) T cells were determined. RESULTS: We first determined previously unknown DP4-restricted CD4(+) T-cell epitopes derived from cytomegalovirus pp65, to which sustained Th1-biased recall responses were induced in vitro by using DP4-aAPC. In contrast, DP4-aAPC induced in vitro both Th1 and Th2 long-lived anti-survivin CD4(+) T cells from cancer patients. Both survivin-specific Th1 and Th2 cells were able to recognize survivin-expressing tumors in a DP4-restricted manner. Neither survivin-specific interleukin 10 secreting Tr1 cells nor Th17 cells were induced by DP4-aAPC. CONCLUSIONS: DP4-restricted anti-survivin Th1 and Th2 immunity with sufficient functional avidity can be induced from cancer patients. The development of strategies to concurrently induce both CD4(+) and CD8(+) T-cell responses against survivin is warranted for optimal anti-survivin cancer 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) | 0.000 | 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".