Tumor-associated MUC1 glycopeptide epitopes are not subject to self-tolerance and improve responses to MUC1 peptide epitopes in MUC1 transgenic mice
Bibliographic record
Abstract
Human adenocarcinomas overexpress a hypoglycosylated, tumor-associated form of the mucin-like glycoprotein MUC1 containing abnormal mono- and disaccharide antigens, such as Tn, sialyl-Tn, and TF, as well as stretches of unglycosylated protein backbone in the variable number of tandem repeats (VNTR) region. Both peptide and glycopeptide epitopes generated from the VNTR are candidates for cancer vaccines and we performed experiments to evaluate their relative potential to elicit tumor-MUC1-specific immunity. We show here that immunization with the 100 amino acid-long VNTR peptide (MUC1p) elicits weaker responses in MUC1 transgenic mice compared to wild type mice suggesting self-tolerance. In contrast, when glycosylated with tumor-associated Tn antigen (GalNAc-O-S/T), TnMUC1 induces glycopeptide-specific T cell and antibody responses in both strains of mice and helps enhance responses to MUC1p in MUC1 transgenic mice. Using newly derived MUC1-specific mouse T cell hybridomas we show that the only antigen-presenting cells able to cross-present TnMUC1 glycopeptide are dendritic cells (DCs). This is likely due to their exclusive expression of receptors capable of binding TnMUC1. We conclude that MUC1 glycopeptides induce stronger immunity in MUC1-Tg mice because they are recognized as 'foreign' rather than ;self' and because they are cross-presented preferentially by DCs.
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) | 0.001 | 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 | 0.001 | 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".