OS06.4 Identification of novel NTRK fusion in glioneuronal tumors and radiographic response following therapy with an NTRK inhibitor
Bibliographic record
Abstract
Glioneuronal tumors constitute a histologically diverse group of primary CNS neoplasms, with no effective systemic therapy after surgery has failed. To further characterize this group of tumors, we collected a cohort of 26 glioneuronal tumors and performed in-depth genomic analysis. We identified mutations in BRAF (34%) and oncogenic fusions (30%). In addition, we discovered novel oncogenic fusions involving members of the NTRK gene family in a subset of our cohort. One patient with BCAN exon 13 fused to NTRK1 exon 11 initially underwent a subtotal resection for a 4th ventricular glioneuronal tumor but ultimately required additional therapy due to progressive, symptomatic disease. Given the patient’s targetable fusion, the patient was enrolled on a clinical trial with entrectinib, a pan-Trk, ROS1 and ALK inhibitor. The patient was treated for 11 months and during this time volumetric analysis of the lesion demonstrated a maximum reduction of 60% in the contrast-enhancing tumor compared to his pre-treatment MRI. The radiologic response was associated with resolution of his clinical symptoms and was maintained for eleven months on treatment. This represents the first report of BCAN-NTRK1 fusion in glioneuronal tumors and highlights its clinical importance as a novel, targetable alteration.
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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 | 0.000 |
| 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".