Preclinical drug screen reveals topotecan, actinomycin D, and volasertib as potential new therapeutic candidates for ETMR brain tumor patients
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Bibliographic record
Abstract
BACKGROUND: Embryonal tumor with multilayered rosettes (ETMR) is a rare and aggressive embryonal brain tumor that solely occurs in infants and young children and has only recently been recognized as a separate brain tumor entity in the World Health Organization classification for CNS tumors. Patients have a very dismal prognosis with a median survival of 12 months upon diagnosis despite aggressive treatment. The aim of this study was to develop novel treatment regimens in a preclinical drug screen in order to inform potentially more active clinical trial protocols. METHODS: We have carried out an in vitro and in vivo drug screen using the ETMR cell line BT183 and its xenograft model. Furthermore, we have generated the first patient-derived xenograft (PDX) model for ETMR and evaluated our top drug candidates in an in vitro drug screen using this model. RESULTS: BT183 cells are very sensitive to the topoisomerase inhibitors topotecan and doxorubicin, to the epigenetic agents decitabine and panobinostat, to actinomycin D, and to targeted drugs such as the polo-like kinase 1 (PLK1) inhibitor volasertib, the aurora kinase A inhibitor alisertib, and the mammalian target of rapamycin (mTOR) inhibitor MLN0128. In xenograft mice, monotherapy with topotecan, volasertib, and actinomycin D led to a temporary response in tumor growth and a significant increase in survival. Finally, using multi-agent treatment regimens of topotecan or doxorubicin combined with methotrexate and vincristine, the response in tumor growth and survival was further increased compared with mice receiving single treatments. CONCLUSIONS: We have identified several promising candidates for combination therapies in future clinical trials for ETMR patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it