A35 DNA HYPOMETHYLATION INHIBITS TUFT CELL-DERIVED COLITIS-ASSOCIATED CANCER
Bibliographic record
Abstract
Abstract Background Colorectal cancer is the second leading cause of cancer death in Canada. A major risk factor for the development of colorectal cancer is chronic inflammation leading to colitis-associated cancer (CAC). We previously described a CAC mouse mode in which tumors arise from DCLK1+ tuft cells following loss of the tumor suppressor adenomatous polyposis coli (APC) and induction of colitis. Interestingly, both colitis and CAC display epigenetic changes that modulate gene expression. Specifically, DNA methylation is altered in colitis, but its role in colonic tumorigenesis is not known. We hypothesize that inhibition of DNA methylation in DCLK1+ tuft cells reduces colonic tumorigenesis. Aims In this study, we aim to investigate the role of DNA methylation in CAC by inhibiting DNA methylation by genetic and pharmacologic means. Methods We crossed our Dclk1-CreERT2/Apcf/f mice to DNMT1f/f mice to delete the DNA methyltransferase DNMT1 in DCLK1+ tuft cells. We induced CAC in Dclk1/Apcf/f and Dclk1/Apcf/f/DNMT1f/f mice by administering three doses of tamoxifen followed by 2.5% dextran sodium sulfate (DSS) for five days. Fourteen weeks later we assessed colonic tumor number and size. Lineage tracing of Dclk1+ cells was also examined in colonic tissues from all mice. In a separate cohort of Dclk1/Apcf/f mice, we induced colitis and treated the mice with six doses of the DNA de-methylating drug 5-AZA-2’-deoxycytidine (5-AZA) or vehicle. Ki67 immunostaining was additionally performed to assess cellular proliferation in the colon. Results Deletion of DNMT1 in DCLK1+ cells significantly inhibited the number and size of colonic tumors. Treatment of mice with 5-AZA similarly reduced the overall number of mice with tumors, as well as, the number and size of tumors per mouse. Interestingly, 5-AZA treatment was associated with reduced colonic proliferation as assessed by fewer Ki67+ cells, and quiescent DCLK1+ cells that did not lineage trace. Furthermore, deletion of DNMT1 or treatment with 5-AZA reduced the number of lineage tracing events detected upon exposure to low DSS. Conclusions Our findings demonstrate that loss of Dnmt1 or 5-AZA treatment reduces CAC formation. Furthermore, 5-AZA appears to exert its anti-tumor effects by reducing proliferation and preventing tuft cell stemness. Our data demonstrates that altering DNA methylation plays an important role in CAC. Funding Agencies CIHR
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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 | 0.000 |
| 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) | 0.003 | 0.001 |
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".