Clinical and molecular characterization of a multi-institutional cohort of pediatric spinal cord low-grade gliomas
Bibliographic record
Abstract
Abstract Background The mitogen-activated protein kinases/extracelluar signal-regulated kinases pathway is involved in cell growth and proliferation, and mutations in BRAF have made it an oncogene of interest in pediatric cancer. Previous studies found that BRAF mutations as well as KIAA1549–BRAF fusions are common in intracranial low-grade gliomas (LGGs). Fewer studies have tested for the presence of these genetic changes in spinal LGGs. The aim of this study was to better understand the prevalence of BRAF and other genetic aberrations in spinal LGG. Methods We retrospectively analyzed 46 spinal gliomas from patients aged 1–25 years from Children’s Hospital Colorado (CHCO) and The Hospital for Sick Children (SickKids). CHCO utilized a 67-gene panel that assessed BRAF and additionally screened for other possible genetic abnormalities of interest. At SickKids, BRAFV600E was assessed by droplet digital polymerase chain reaction and immunohistochemistry. BRAF fusions were detected by fluorescence in situ hybridization, reverse transcription polymerase chain reaction, or NanoString platform. Data were correlated with clinical information. Results Of 31 samples with complete fusion analysis, 13 (42%) harbored KIAA1549–BRAF. All 13 (100%) patients with confirmed KIAA1549–BRAF survived the entirety of the study period (median [interquartile range] follow-up time: 47 months [27–85 months]) and 15 (83.3%) fusion-negative patients survived (follow-up time: 37.5 months [19.8–69.5 months]). Other mutations of interest were also identified in this patient cohort including BRAFV600E, PTPN11, H3F3A, TP53, FGFR1, and CDKN2A deletion. Conclusion KIAA1549–BRAF was seen in higher frequency than BRAFV600E or other genetic aberrations in pediatric spinal LGGs and experienced lower death rates compared to KIAA1549–BRAF negative patients, although this was not statistically significant.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".