NTRK2 Fusion Driven Pediatric Glioblastoma: Identification of key molecular drivers by personalized oncology
Bibliographic record
Abstract
We describe the case of an 11-month-old girl with a rare cerebellar glioblastoma driven by a NACC2-NTRK2 (Nucleus Accumbens Associated Protein 2-Neurotrophic Receptor Tyrosine Kinase 2) fusion. Initial workup of our case demonstrated homozygous CDKN2A deletion, but immunohistochemistry for other driver mutations, including IDH1 R132H, BRAF V600E, and H3F3A K27M were negative, and ATRX was retained. Tissue was subsequently submitted for personalized oncogenomic analysis, including whole genome and whole transcriptome sequencing, which demonstrated an activating NTRK2 fusion, as well as high PD-L1 expression, which was subsequently confirmed by immunohistochemistry. Furthermore, H3 and IDH demonstrated wildtype status. These findings suggested the possibility of treatment with either NTRK- or immune checkpoint- inhibitors through active clinical trials. Ultimately, the family pursued standard treatment that involved Head Start III chemotherapy and proton radiotherapy. Notably, at most recent follow upapproximately two years from initial diagnosis, the patient is in disease remission and thriving, suggesting favorable biology despite histologic malignancy. This case illustrates the value of personalized oncogenomics, as the molecular profiling revealed two actionable changes that would not have been apparent through routine diagnostics. NTRK fusions are known oncogenic drivers in a range of cancer types, but this is the first report of a NACC2-NTRK2 fusion in a glioblastoma. LEARNING OBJECTIVES This presentation will enable the learner to: 1. Explore the current molecular landscape of pediatric high grade gliomas 2. Recognize the value of personalized oncogenomic analysis, particularly in rare and/or aggressive tumors 3. Discuss the current status of NTRK inhibitor clinical trials
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".