Cancer-associated fibroblasts promote tumor progression in fusion-positive rhabdomyosarcoma
Bibliographic record
Abstract
In children, rhabdomyosarcoma is the most common tumor originating in soft connective tissue. The PAX3/PAX7-FOXO1 fusion-positive alveolar subtype has poor clinical outcomes, with frequent recurrence, metastasis, and low survival. Current therapies may be limited by the tumor microenvironment containing cancer, immune, and fibroblast cells. In cancer, cells that transform into cancer-associated fibroblasts support growth and other malignant properties of the tumor. To study the role of cancer-associated fibroblasts, we isolated cells from the lung metastases of a patient with rhabdomyosarcoma that are positive for fibroblast biomarkers. Compared to normal lung fibroblasts, these cancer-associated fibroblasts secreted distinct factors that specifically supported the growth and migration of fusion-positive rhabdomyosarcoma cells. The cancer-associated fibroblasts also promoted expression of immune checkpoints and conferred resistance to cyclophosphamide, a chemotherapy commonly used to treat this disease. We further characterized the secretory phenotype of cytokines and growth factors produced by these cancer-associated fibroblasts and targeted CXCR4 expression inhibition, which induced cytotoxicity at increased sensitivity. This study establishes a model of cancer-associated fibroblasts from metastatic fusion-positive rhabdomyosarcoma. Altogether, our results describe tumor-promoting mechanisms of growth, migration, and treatment resistance supported by the tumor microenvironment, and offer a novel therapeutic strategy for the treatment of rhabdomyosarcoma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".