Tumor-associated MUC1 glycopeptide epitopes are not subject to self-tolerance and improve responses to MUC1 peptide epitopes in MUC1 transgenic mice
Why this work is in the frame
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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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it