Abstract SY43-02: Mismatch repair, gut microbiota, and the etiology of colon cancer
Bibliographic record
Abstract
Abstract The etiology of colorectal cancer (CRC) has been linked to deficiencies in mismatch repair and adenomatous polyposis coli (APC) proteins, diet, inflammatory processes, and gut microbiota. However, the mechanism through which the microbiota synergizes with these etiologic factors to promote CRC is not clear. We report that altering the microbiota composition reduces CRC in APC(Min/+)Msh2-/- and APC(Min/+)Mlh1-/- mice, and that a diet reduced in carbohydrates phenocopies this effect. Gut microbes did not induce CRC in these mice through an inflammatory response or the production of DNA mutagens but rather by providing carbohydrate-derived metabolites such as butyrate that fuels hyperproliferation of Msh2-/- and Mlh1-/- colon epithelial cells. Further, we provide evidence that the mismatch repair pathway has a role in regulating β-catenin activity and potentially modulating the differentiation of transit-amplifying cells in the colon. These data show that mismatch repair deficiency sensitizes colon epithelial cells to transformation by gut microbiota, and thereby provide an explanation for the interaction between microbiota, diet, and mismatch repair deficiency in CRC induction. Citation Format: Alberto Martin. Mismatch repair, gut microbiota, and the etiology of colon cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr SY43-02. doi:10.1158/1538-7445.AM2015-SY43-02
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".