Tumor-associated antigen MUC1 contains self epitopes subject to self-tolerance and tumor-associated “foreign” epitopes that elicit effective immunity: important distinction for cancer vaccines (41.60)
Bibliographic record
Abstract
Abstract Human adenocarcinomas express tumor-associated antigen (TAA) MUC1 that presents to the immune system peptide epitopes as well as glycopeptide epitopes carrying tumor specific carbohydrates. In MUC1-Tg mice responses to MUC1-peptides are suppressed while vaccination with tumor-associated glycopeptides results in effective anti-MUC1 immunity. Transgene negative mice respond equally to both antigens. Using our newly generated TCR transgenic mice with peptide and glycopeptide specific TCRs, we show for the first time that peptide specific CD4 T cells transferred to MUC1-Tg mice are suppressed through mechanisms of peripheral tolerance that are not induced against MUC1-glycopeptide specific CD4 T cells. This tolerance is due to MUC1-peptide epitope presentation in the periphery of healthy MUC1-Tg mice, previously thought to occur primarily in tumor bearing mice. MUC1 glycopeptide epitopes are tumor specific and thus treated as foreign in MUC1-Tg mice, resulting in effective activation of CD4 T cells. Furthermore, glycopeptide-specific T cells provide help to enhance peptide-specific T cell responses. We conclude that TAA contain epitopes that are subject to self tolerance and also tumor specific (foreign) epitopes not affected by self tolerance. It is important to maintain this distinction in attempts to develop effective and safe cancer vaccines. (Supported by NIH T32CA82084, RO1CA56103, CBCRA/CCS)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".