Clinical Utility of GlioSeq Next-Generation Sequencing Test in Pediatric and Young Adult Patients With Brain Tumors
Bibliographic record
Abstract
Brain tumors are the leading cause of death in children. Establishing an accurate diagnosis and therapy is critical for patient management. This study evaluated the clinical utility of GlioSeq, a next-generation sequencing (NGS) assay, for the diagnosis and management of pediatric and young adult patients with brain tumors. Between May 2015 and March 2017, 142 consecutive brain tumors were tested using GlioSeq v1 and subset using GlioSeq v2. Out of 142 samples, 63% were resection specimens and 37% were small stereotactic biopsies. GlioSeq sequencing was successful in 100% and 98.6% of the cases for the detection of mutations and copy number changes, and gene fusions, respectively. Average turnaround time was 8.7 days. Clinically significant genetic alterations were detected in 95%, 66.6%, and 66.1% of high-grade gliomas, medulloblastomas, and low-grade gliomas, respectively. GlioSeq enabled molecular-based stratification in 92 (65%) cases by specific molecular subtype assignment (70, 76.1%), substantiating a neuropathologic diagnosis (18, 19.6%), and diagnostic recategorization (4, 4.3%). Fifty-seven percent of the cases harbored therapeutically actionable findings. GlioSeq NGS analysis offers rapid detection of a wide range of genetic alterations across a spectrum of pediatric brain tumors using formalin-fixed, paraffin-embedded specimens and facilitates integrated molecular-morphologic classification and personalized management of pediatric brain tumors.
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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 | 0.002 |
| 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 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".