Tumor Antigen Epitopes Interpreted by the Immune System as Self or Abnormal-Self Differentially Affect Cancer Vaccine Responses
Bibliographic record
Abstract
Epitope selection is an important consideration in the design of cancer vaccines, but factors affecting selection are not fully understood. We compared the immune responses to peptides and glycopeptides from the common human tumor antigen MUC1, a mucin that is coated with O-linked carbohydrates in its variable number of tandem repeats (VNTR) region. MUC1 expressed on tumor cells is characteristically underglycosylated, creating peptide and glycopeptide neoepitopes that are recognized by the immune system. The response to VNTR peptides is weaker in MUC1-transgenic mice (MUC1-Tg mice) than in wild-type (WT) mice, whereas the response to VNTR glycopeptides is equally strong in the two strains. Thus, glycopeptides seem to be recognized as foreign, whereas peptides, although immunogenic, are perceived as self. To explore this further, we generated MUC1 peptide- and glycopeptide-specific T-cell receptor transgenic mice and studied the function of their CD4 T cells when adoptively transferred into MUC1-Tg or WT mice. Peptide-specific T-cell precursors were not centrally deleted in MUC1-Tg mice and did not acquire a T regulatory phenotype. However, their response to the cognate peptide was reduced in MUC1-Tg mice compared with WT mice. In contrast, glycopeptide-specific CD4 T cells responded equally well in the two hosts and, when simultaneously activated, also enhanced the peptide-specific T-cell responses. Our data show that the immune system differentially recognizes various epitopes of tumor-associated antigens either as self or as foreign, and this controls the strength of antitumor immunity. This represents an important consideration for designing safe and effective cancer vaccines.
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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".