Application of central immunologic concepts to cancer: Helping T cells and B cells become intolerant of tumors
Bibliographic record
Abstract
CD4-mediated T-cell help in the activation of CD8(+) T cells and B cells, through linked-recognition of antigenic determinants, is a long-standing concept foundational to our understanding of immunity (presence of help) versus tolerance (lack of help). Surprisingly, this function of CD4(+) T cells has not been extensively examined as a means to overcome immune tolerance of the self-antigens made by tumor cells. Hesitation to employ this powerful mechanism may be due to the potential to cause unwanted autoimmune pathology. In this issue of the European Journal of Immunology, Snook et al. [Eur. J. Immunol. 2014. 44: 1956-1966] identify a state of split tolerance, showing that CD4(+) T cells specific for a number of tumor-associated self-antigens are robustly tolerant, while their CD8(+) T-cell and B-cell counterparts are far less tolerant. Furthermore, the authors demonstrate that provision of linked foreign helper epitopes, such as influenza hemagglutinin, substantially enhances both CD8(+) T-cell and B-cell responses to tumor self-antigens without causing any overt autoimmune pathology. These findings provide a strong rationale to employ foreign helper epitopes in cancer vaccines and highlight the need to fully explore therapeutic strategies that are based on well-established immunologic concepts.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".