Differentiation of rhabdomyosarcoma cell lines using retinoic acid
Bibliographic record
Abstract
BACKGROUND: Rhabdomyosarcoma (RMS) is the most frequent sporadic soft tissue sarcoma of childhood and adolescence. The overall 5-year survival rate for patients with RMS is 70% with the use of surgery, radiation, and chemotherapy. Novel therapeutic approaches are necessary to improve on these outcomes particularly among the more aggressive alveolar RMS (ARMS) and late stages of disease, where 5-year survival is less than 20%. Retinoids have been successfully used in the treatment of acute promyelocytic leukemia (APML) and neuroblastoma. PURPOSE: However, analysis of retinoids as a differentiating agent for RMS has been incomplete. This work examined the ability of retinoic acid (RA) to promote differentiation of RMS cell lines by examining the expression of myogenic proteins in five RMS cell lines in response to All-trans Retinoic Acid (ATRA) or 9-cis retinoic acid (CRA). RESULTS: Analysis of growth curves indicates that both retinoids suppress cell growth of Rh4 and Rh28. RD cells only responded to-CRA whereas Rh30 and Rh18 did not respond. Following treatment with ATRA FACS analysis showed an altered cell cycle with the same pattern as the growth curves. ATRA altered cellular morphology of two cell lines, Rh4 and Rh28, and induced Troponin T expression in these cells suggesting a differentiating effect. CONCLUSIONS: These studies suggest that retinoids are effective inducers of growth arrest and differentiation in some RMS cell lines, and offer a basis for further in vivo testing in mice of ATRA as a potential approach to ARMS treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".