IFN-β Signaling Positively Regulates Tumorigenesis in Aggressive Fibromatosis, Potentially by Modulating Mesenchymal Progenitors
Bibliographic record
Abstract
Aggressive fibromatosis (also called desmoid tumor) is a benign, locally invasive, soft tissue tumor composed of cells with mesenchymal characteristics. These tumors are characterized by increased levels of beta-catenin-mediated T-cell factor (TCF)-dependent transcriptional activation. We found that type 1 IFN signaling is activated in human and murine aggressive fibromatosis tumors and that the expression of associated response genes is regulated by beta-catenin. When mice deficient for the type 1 IFN receptor (Ifnar1-/-) were crossed with mice predisposed to developing aggressive fibromatosis tumors (Apc/Apc1638N), a significant decrease in aggressive fibromatosis tumor formation was observed compared with littermate controls, showing a novel role for type 1 IFN signaling in promoting tumor formation. Type 1 IFN activation inhibits cell proliferation but does not alter cell apoptosis or the level of beta-catenin-mediated TCF-dependent transcriptional activation in aggressive fibromatosis cell cultures. Thus, these changes cannot explain our in vivo results. Intriguingly, Ifnar1-/- mice have smaller numbers of mesenchymal progenitor cells compared with littermate controls, and treatment of aggressive fibromatosis cell cultures with IFN increases the proportion of cells that exclude Hoechst dye and sort to the side population, raising the possibility that type 1 IFN signaling regulates the number of precursor cells present that drive aggressive fibromatosis tumor formation and maintenance. This study identified a novel role for IFN type 1 signaling as a positive regulator of neoplasia and suggests that IFN treatment is a less than optimal therapy for this tumor type.
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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 | 0.000 |
| 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".