Vaccine against MUC1 Antigen Expressed in Inflammatory Bowel Disease and Cancer Lessens Colonic Inflammation and Prevents Progression to Colitis-Associated Colon Cancer
Bibliographic record
Abstract
Association of chronic inflammation with an increased risk of cancer is well established, but the contributions of innate versus adaptive immunity are not fully delineated. There has furthermore been little consideration of the role played by chronic inflammation-associated antigens, including cancer antigens, and the possibility of using them as vaccines to lower the cancer risk. We studied the human tumor antigen MUC1 which is abnormally expressed in colon cancers and also in inflammatory bowel disease (IBD) that gives rise to colitis-associated colon cancer (CACC). Using our new mouse model of MUC1(+) IBD that progresses to CACC, interleukin-10 knockout mice crossed with MUC1 transgenic mice, we show that vaccination against MUC1 delays IBD and prevents progression to CACC. One mechanism is the induction of MUC1-specific adaptive immunity (anti-MUC1 IgG and anti-MUC1 CTL), which seems to eliminate abnormal MUC1(+) cells in IBD colons. The other mechanism is the change in the local and the systemic microenvironments. Compared with IBD in vaccinated mice, IBD in control mice is dominated by larger numbers of neutrophils in the colon and myeloid-derived suppressor cells in the spleen, which can compromise adaptive immunity and facilitate tumor growth. This suggests that the tumor-promoting microenvironment of chronic inflammation can be converted to a tumor-inhibiting environment by increasing adaptive immunity against a disease-associated antigen.
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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".