Targeting STAT3 Signaling in COL1+ Fibroblasts Controls Colitis-Associated Cancer in Mice
Bibliographic record
Abstract
Colorectal cancer (CRC) is a common disease and has limited treatment options. The importance of cancer-associated fibroblasts (CAFs) within the tumor microenvironment (TME) in CRC has been increasingly recognized. However, the role of CAF subsets in CRC is hardly understood and opposing functions of type I (COL1+) vs. type VI (COL6+) collagen-expressing subsets were reported before with respect to NFκB-related signaling. Here, we have focused on COL1+ fibroblasts, which represent a frequent CAF population in CRC and studied their role upon STAT3 activation in vivo. Using a dual strategy with a conditional gain-of-function and a conditional loss-of-function approach in an in vivo model of colitis-associated cancer, tumor development was evaluated by different readouts, including advanced imaging methodologies, e.g., light sheet microscopy and CT-scan. Our data demonstrate that the inhibition of STAT3 activation in COL1+ fibroblasts reduces tumor burden, whereas the constitutive activation of STAT3 promotes the development of inflammation-driven CRC. In addition, our work characterizes the co-expression and distribution of type I and type VI collagen by CAFs in inflammation-associated colorectal cancer using reporter mice. This work indicates a critical contribution of STAT3 signaling in COL1+ CAFs, suggesting that the blockade of STAT3 activation in type I collagen-expressing fibroblasts could serve as promising therapeutic targets in colitis-associated CRC. In combination with previous work by others and us, our current findings highlight the context-dependent roles of COL1+ CAFs and COL6+ CAFs that might be variable according to the specific pathway activated.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".