Abstract A25: Evaluation of Drug Disposition in Supratentorial Ependymoma
Bibliographic record
Abstract
Abstract Introduction: The majority of pediatric supratentorial (ST) ependymomas (EPN) is driven by distinct gene fusions between C11orf95 and RELA. The resultant molecular group of ST-EPN-RELA tumors is characterized by constitutive activation of NF-κB signaling and deregulation of the p53 pathway. In contrast to surgery and radiotherapy, chemotherapy has failed to demonstrate significant benefit in the management of affected children. Alternative strategies including enhanced drug delivery, combination treatments, or application of new selective compounds are needed to tackle this disease. Material and Methods: RNAi and drug screening methods were applied to identify potential therapeutic approaches using ST-EPN-RELA cell lines. In order to identify optimal dosing strategies of selected drugs and to assess effects of combinatorial treatment approaches on blood-brain barrier (BBB) penetration, cerebral microdialysis combined with ultraperformance liquid chromatography and tandem mass spectrometry (UPLC-MS/MS) was applied. This approach allowed for exact, continuous, and time-dependent drug quantification in tumors or healthy tissue in freely moving experimental mice. Patient-derived xenograft models of ST-EPN-RELA were treated to investigate toxicity and outcome parameters. Results: Regulation of p53 signaling and nuclear protein shuttling were identified as promising therapeutic approaches. While low-dose dactinomycin could successfully reestablish p53 function in ST-EPN-RELA cells in vitro, penetration of the drug across the BBB was found to be very poor and did not result in a survival benefit of tumor-bearing mice. Preliminary results of alternative strategies such as combination with efflux pump inhibitors, liposomal packaging, and inhibition of XPO1 being the sole nuclear exporter of p53 hold promise to overcome these constraints. Conclusion: Oncogenic dependencies of ST-EPN-RELA are currently difficult to target. Preclinical evaluation of effective drug disposition combined with long-term treatment studies may help to better select promising compounds and thereby increase success rates of early clinical trials in patients with ST-EPN-RELA in the future. Citation Format: Julia Benzel, Max Sauter, Norman Mack, Abigail Davis, Johanna Weiss, Philipp Uhl, Jürgen Burhenne, Kendra K. Maass, Jens-Martin Hübner, Hendrik Witt, Anang Shelat, Amar Gajjar, Santhosh A. Upadhyaya, Aylin Camgoz, Frank Buchholz, Sina Oppermann, Marcel Kool, Daisuke Kawauchi, Olaf Witt, Walter E. Haefeli, Stefan M. Pfister, Clinton Stewart, Kristian W. Pajtler. Evaluation of Drug Disposition in Supratentorial Ependymoma [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr A25.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".