Abstract 1121: Targeting the DNA damage response in high-risk medulloblastoma
Bibliographic record
Abstract
Abstract Medulloblastoma (MB) is the most common malignant pediatric brain tumour and is currently treated with surgery, external beam radiation and chemotherapy however, current treatments are woefully inadequate, with survival of only 50% but morbidity approaching 100%. A very-high risk group of SHH-activated MB harboring TP53 mutations (TP53mut-SHH) account for a substantial proportion of treatment failures after radiotherapy with a uniformly fatal outcome. Through optimizing radiation as a selection pressure and employing a functional genetic approach through whole-genome CrisprCas-9 dropout screening, the present study aims to identify drivers of radiation resistance and uncover unique vulnerabilities to sensitize these tumours to therapy. The dropout screen was conducted across a panel of established in vitro models of sporadic murine TP53mut-SHH, which resemble the human disease with profound radio-resistance. Initial results reveal that targeting the DNA damage response confer radiation sensitization. However, consistent hits across all models demonstrate non-homologous end-joining (NHEJ) as being synthetically lethal with radiation specifically in TP53mut-SHH. Genetic knockout experiments confirm genes encoding main proteins involved in NHEJ are synthetically lethal in combination with radiation. A small molecule drug screen targeting NHEJ revealed such vulnerabilities may be therapeutically exploited in TP53mut-SHH and are able sensitize these tumours to radiation. Using our unique approach of leveraging newly established models of TP53mut-SHH with a combination of functional genetic screening and small-molecule drug screening, this study highlights the genetic mechanisms of treatment resistance and reveals novel approaches for radiation sensitization. Here, we develop a novel approach to identify targets that are synthetically lethal with radiotherapy and identify multiple synthetically lethal targets at the genetic level which can be therapeutically targeted, allowing the development of new treatments to overcome current barriers. Citation Format: Alexandria DeCarlo, Carolina Fernandes da Silva, Lily Shen, Vijay Ramaswamy, Uri Tabori. Targeting the DNA damage response in high-risk medulloblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1121.
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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 | 0.000 | 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) | 0.002 | 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".