Targeting Stem Cell Behavior in Desmoid Tumors (Aggressive Fibromatosis) by Inhibiting Hedgehog Signaling
Bibliographic record
Abstract
Desmoid tumor (also called aggressive fibromatosis) is a lesion of mesenchymal origin that can occur as a sporadic tumor or a manifestation of the preneoplastic syndrome, familial adenomatous polyposis caused by a mutation in adenomatous polyposis coli (APC). This tumor type is characterized by the stabilization of β-catenin and activation of Tcf-mediated transcription. Cell transplantation data suggest that desmoid tumors are derived from mesenchymal progenitor cells (MSCs). As such, modulating cell signaling pathways that regulate MSC differentiation or proliferation, such as hedgehog (Hh) signaling, could alter the tumor phenotype. Here, we found that Hh signaling is activated in human and murine desmoid tumors. Inhibiting Hh signaling in human cell cultures decreased cell proliferation and β-catenin protein levels. Apc(+)/Apc(1638N) mice, which develop desmoid tumors, develop smaller and fewer tumors when Hh signaling was inhibited either genetically (by crossing Apc(+)/Apc(1638N) mice with mice lacking one copy of a Hh-activated transcription factor, Gli2 (+/-) mice) or using a pharmacologic inhibitor. Both in mice and in human tumor cell cultures, β-catenin and Hh-mediated signaling positively regulate each other's activity. These data show that targeting a pathway that regulates MSC differentiation influences desmoid tumor behavior, providing functional evidence supporting the notion that these tumors are derived from mesenchymal progenitors. It also suggests Hh blockade as a therapeutic approach 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".