HGG-10. YAP1-MAML2 FUSION IN YOUNG CHILDREN WITH PEDIATRIC HIGH-GRADE GLIOMA: A CASE REPORT
Bibliographic record
Abstract
Abstract BACKGROUND High-grade gliomas (HGG) in children are a heterogeneous group of central nervous system (CNS) tumors that are very aggressive and highly malignant. Few patients achieve long-term survival despite decades of clinical trials. International collaborations have sought to better describe and understand the genetic underpinnings of pediatric HGG in hopes to uncover more effective treatment options. We report two HGG cases of young children with a YAP1-MAML2 fusion. METHODS Clinical data was collected retrospectively, and pathology was reviewed to confirm the diagnosis. RNA sequencing was performed using the Illumina TruSight RNA Pan-cancer Next Generation Sequencing Panel. Methylation profiling was completed using the HumanMethylation450 BeadChip platform (Illumina, San Diego, CA). RESULTS Two children ages less than 5 years with HGG were identified to have the YAP1-MAML2 fusion. One child was diagnosed at age 4 years and had an additional somatic BRAF V600D mutation. The child was treated initially with surgical debulking and cisplatin-based chemotherapy but had rapid clinical and radiologic progression. Due to the BRAF V600D mutation, the child started Dabrafenib and Trametinib but continued to have rapid craniospinal dissemination and died within 8 weeks of presentation. The second child was diagnosed at age 2 years and had an additional oncogenic PTENpF341V mutation. DNA methylation profiling on this tumor did not show evidence of clustering with any known CNS tumor entity. This child was treated with surgical debulking, chemotherapy (Baby POG), focal radiation 5400cGy, and maintenance chemotherapy (lomustine and temozolomide as per COG ACNS 0423). This child continues to have stable residual disease 29 months from presentation. CONCLUSION We present two cases of HGG in young children with a unique YAP1-MAML2 fusion. Future studies are needed to further characterize the significance of how this new molecular alteration impacts treatment and long-term outcomes in pediatric HGG.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".