Multiplex Detection of Pediatric Low-Grade Glioma Signature Fusion Transcripts and Duplications Using the NanoString nCounter System
Bibliographic record
Abstract
Previous studies identified recurrent fusion and duplication events in pediatric low-grade glioma (pLGG). In addition to their role in diagnosis, the presence of these events aid in dictating therapy and predicting patient survival. Clinically, BRAF alterations are most commonly identified using fluorescent in situ hybridization (FISH). However, this method is costly, labor-intensive and does not identify nonBRAF events. Here, we evaluated the NanoString nCounter gene expression system for detecting 32 of the most commonly reported fusion/duplication events in pLGG. The assay was validated on 90 pLGG samples using FISH as the gold standard and showed sensitivity and specificity of 97% and 98%, respectively. We next profiled formalin-fixed paraffin-embedded preserved biopsy specimens from 429 pLGG cases. 171 (40%) of the cases within our cohort tested positive for a fusion or duplication event contained within our panel. These events, in order of prevalence, were KIAA1549-BRAF 16;9 (89/171, 52.0%), KIAA1549-BRAF 15;9 (42/171, 24.6%), KIAA1549-BRAF 16;11 (14/171, 8.2%), FGFR1-TACC1 17;7 (13/171, 7.6%), MYBL1 duplication (5/171, 2.9%), KIAA1549-BRAF 18;10 (4/171, 2.3%), KIAA1549-BRAF 15;11 (2/171, 1.2%), FAM131B-BRAF 2;9 (1/171, 0.6%), and RNF130-BRAF 3;9 (1/171, 0.6%). This work introduces NanoString as a viable clinical replacement for the detection of fusion and duplication events in pLGG.
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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".