LG-50MULTIPLEX DETECTION OF PEDIATRIC LOW GRADE GLIOMA SIGNATURE FUSION AND DUPLICATION TRANSCRIPTS USING THE NANOSTRING NCOUNTER SYSTEM
Bibliographic record
Abstract
DNA microarray and next generation sequencing studies have identified recurrent fusion and duplication events in pediatric low grade glioma (pLGG), including KIAA1549-BRAF fusion events. In addition to its role in diagnosis, the presence of BRAF fusions have been linked to better patient survival. Clinically, the fusion events are typically identified using fluorescent in situ hybridization (FISH) or RT-PCR based techniques which are costly, labour-intensive and insensitive on formalin-fixed/paraffin-embedded (FFPE) material. Here, we devised a probe set targeting 33 of the most commonly reported gene fusion and duplication transcripts in pLGG in conjunction with nanoString. The assay was validated on 78 FFPE FFPE samples using FISH as the gold standard. Discrepant cases were tested with SNP arrays. Compared with FISH, nanoString had a sensitivity of 95% (38/40) and a specificity of 92% (35/38) for detecting the presence of a fusion event. There were 5 discordant cases. Additional molecular analysis confirmed the nanoString result in 3 cases and the FISH result in 2 case. Once validated, we utilized the assay on a cohort of 377 cases. 134 (36%) of the pLGGs tested positive for a fusion or duplication event contained within our panel. These events, in order of prevalence, were BRAF:KIAA1549 16:09 (46%), BRAF:KIAA1549 15:09 (29%), FGFR1:TACC1 17:07 (10%) BRAF:KIAA1549 16:11 (9%), MYBL1 duplication (3%), BRAF:KIAA1549 15:11 (2%), and FGFR1 duplication (1%). This work provides an optimized and validated technique by which fusion and duplication events can be effectively identified in pLGG patients within a clinical setting, aiding in efficient patient stratification.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".