Inhibition of fibroblast to myofibroblast transition by halofuginone contributes to the chemotherapy-mediated antitumoral effect
Bibliographic record
Abstract
Stromal myofibroblasts play an important role in tumor progression. The transition of fibroblasts to myofibroblasts is characterized by expression of smooth muscle genes and profuse synthesis of extracellular matrix proteins. We evaluated the efficacy of targeting fibroblast-to-myofibroblast transition with halofuginone on tumor progression in prostate cancer and Wilms' tumor xenografts. In both xenografts, low doses of halofuginone treatment, independent of the route of administration, resulted in a trend toward inhibition in tumor development. Moreover, halofuginone synergizes with low dose of docetaxel in prostate cancer and vincristine and dactinomycin in Wilms' tumor xenografts, resulting in significant reduction in tumor volume and weight comparable to the effect observed by high doses of the respective chemotherapies. In prostate cancer and Wilms' tumor xenografts, halofuginone, but not the respective chemotherapies, inhibited the synthesis of collagen type I, alpha-smooth muscle actin, transgelin, and cytoglobin, all of which are characteristics of activated myofibroblasts. Halofuginone, as the respective chemotherapies, increased the synthesis of Wilms' tumor suppressor gene product (WT-1) and prostate apoptosis response gene-4 (Par-4), resulting in apoptosis/necrosis. These results suggest that targeting the fibroblast-to-myofibroblast transition with halofuginone may synergize with low doses of chemotherapy in achieving a significant antitumoral effect, avoiding the need of high-dose chemotherapy and its toxicity without impairing treatment efficacy.
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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".