PCM-20PRE-CLINICAL DRUG SCREEN IN A PDX ETMR MOUSE MODEL
Bibliographic record
Abstract
Embryonal tumors with multilayered rosettes (ETMR) is a subgroup of CNS-PNETs with three histological variants: ependymoblastoma (EBL), medulloepithelioma (MEPL) and embryonal tumor with abundant neuropil and true rosettes (ETANTR). They are clinically and genetically highly similar, occur mostly in infants and young children with a very aggressive clinical course and poor outcome. To develop novel treatment strategies we performed a drug screen and identified drugs that effectively inhibit survival of the ETMR cell line BT183 in vitro and in vivo. Our drug library comprised 30 drugs having one of the following criteria: approval for treatment in children, classic chemotherapeutics, drugs from the ETMR treatment protocol and novel compounds for targeted therapy. Overall, ten drugs inhibited BT183 cell growth in vitro with an IC50 <120 nM. Among them were three topoisomerase inhibitors and two demethylating reagents. Our drug combination screen showed that the demethylating compound decitabine and the topoisomerase I inhibitor topotecan act synergistically on BT183 cells. Seven compounds were tested in our ETANTR xenograft mouse model by single treatment regimen. Preliminary data suggest that volasertib and topotecan slow down tumor progression and extend survival of tumor-bearing animals. Decitabine, alisertib and MLN0128 also show some effect, but have extensive toxicity in the animals. Currently, we are testing combination treatment of decitabine and topotecan in vivo and are establishing a second PDX ETMR model for our screen. Our data suggest that epigenetic drugs alone or in combination with classic chemotherapeutic regimens should be considered as a novel treatment strategy for ETMR tumors.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".