EAPH-06. HYPERMUTANT PEDIATRIC HIGH GRADE GLIOMAS ARE DRIVEN BY RAS/MAPK MUTATIONS AND RESPOND TO MEK INHIBITION
Bibliographic record
Abstract
Hypermutant pediatric high grade gliomas (pHGG) constitute 5–10% of PHGG and are almost invariantly caused by replication repair deficiency (RRD). Constitutive activation of the RAS/MAPK pathway via oncogenic mutations can be targeted with MEK inhibitors (MEKi). We hypothesized that hypermutant RRD PHGG would harbor mutations in NF1 and other MAPK pathway regulators, making their tumors susceptible to MEKi. Tumor sequencing data from >1200 pediatric tumors was extracted from our foundation-medicine cohort. Exome and RNA-seq was performed on tumors from our international RRD consortium. Xenografts from patient derived pHGG were established in NOD-SCID mice and treated with Trametinib and Selumetinib. Functional experiments were performed to demonstrate deregulation of the MAPK pathway in the tumors. Screening of 1215 cancers revealed that RAS/MAPK mutations are the second most common mutation in pediatric tumors. Exome data from patients revealed 95% of PHGG were found to harbor mutations in key RAS/MAPK pathway regulators. Subclonal analysis reveals mutations in the pathway in each subclone and enrichment of these mutations over time. In-vitro and in-vivo treatment of patient derived RRD hypermutant PHGG with MEKi revealed significant tumor suppression with a 50% decrease in tumor volume and improved survival (p <0.001). Finally, two patients with progressive/recurrent PHGG treated with Selumetinib and trametinib exhibited >90% objective tumor response and prolonged survival. These preclinical and clinical observations suggest a novel therapeutic option for children with hypermutant RRD PHGG based on possible oncogenic addiction to RAS/MAPK alterations. These observations are the basis of the upcoming SU2C international clinical trial.
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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) | 0.003 | 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".