Aggressive Fibromatosis (Desmoid Tumor) Is Derived from Mesenchymal Progenitor Cells
Bibliographic record
Abstract
The cellular origins from which most tumors arise are poorly defined, especially in mesenchymal neoplasms. Aggressive fibromatosis, also known as desmoid tumor, is a locally invasive soft tissue tumor that has mesenchymal characteristics. We found that aggressive fibromatosis tumors express genes and cell surface markers characteristic of mesenchymal stem cells (MSC). In mice that are genetically predisposed to develop aggressive fibromatosis tumors (Apc(wt/1638N)), we found that the number of tumors formed was proportional to the number of MSCs present. Sca-1(-/-) mice, which develop fewer MSCs, were crossed with Apc(wt/1638N) mice. Doubly mutant mice deficient in Sca-1 developed substantially fewer aggressive fibromatosis tumors than wild-type (WT) littermates, but Sca-1 deficiency had no effect on the formation of epithelial-derived intestinal polyps. MSCs isolated from Apc(wt/1638N) mice (or mice expressing a stabilized form of β-catenin) induced aberrant cellular growth reminiscent of aggressive fibromatosis tumors after engraftment to immunocompromised mice, but WT cells and mature fibroblasts from the same animals did not. Taken together, our findings indicate that aggressive fibromatosis is derived from MSCs, and that β-catenin supports tumorigenesis by maintaining mesenchymal progenitor cells in a less differentiated state. Protecting this progenitor cell population might prevent tumor formation in patients harboring a germline APC mutation, where fibromatosis is currently the leading cause of mortality.
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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 | 0.000 |
| 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".