Preclinical drug screen reveals topotecan, actinomycin D, and volasertib as potential new therapeutic candidates for ETMR brain tumor patients
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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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".