Mast cell-T cell axis alters development of colitis-dependent and colitis-independent colorectal tumours: potential for therapeutically targeting via mast cell inhibition
Bibliographic record
Abstract
BACKGROUND: Colorectal cancer (CRC) has a high mortality rate and can develop in either colitis-dependent (colitis-associated (CA)-CRC) or colitis-independent (sporadic (s)CRC) manner. There has been a significant debate about whether mast cells (MCs) promote or inhibit the development of CRC. Herein we investigated MC activity throughout the multistepped development of CRC in both human patients and animal models. METHODS: We analyzed human patient matched samples of healthy colon vs CRC tissue alongside conducting a The Cancer Genome Atlas-based immunogenomic analysis and multiple experiments employing genetically engineered mouse (GEM) models. RESULTS: Analyzing human CRC samples revealed that MCs can be active or inactive in this disease. An activated MC population decreased the number of tumor-residing CD8 T cells. In mice, MC deficiency decreased the development of CA-CRC lesions, while it increased the density of tumor-based CD8 infiltration. Furthermore, co-culture experiments revealed that tumor-primed MCs promote apoptosis in CRC cells. In MC-deficient mice, we found that MCs inhibited the development of sCRC lesions. Further exploration of this with several GEM models confirmed that different immune responses alter and are altered by MC activity, which directly alters colon tumorigenesis. Since rescuing MC activity with bone marrow transplantation in MC-deficient mice or pharmacologically inhibiting MC effects impacts the development of sCRC lesions, we explored its therapeutic potential against CRC. MC activity promoted CRC cell engraftment by inhibiting CD8+ cell infiltration in tumors, pharmacologically blocking it inhibits the ability of allograft tumors to develop. This therapeutic strategy potentiated the cytotoxic activity of fluorouracil chemotherapy. CONCLUSION: Therefore, we suggest that MCs have a dual role throughout CRC development and are potential druggable targets against this disease.
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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".