LGG-34. CLINICAL AND MOLECULAR CHARACTERIZATION OF A MULTI-INSTITUTIONAL COHORT OF PEDIATRIC SPINAL CORD LOW-GRADE GLIOMAS
Bibliographic record
Abstract
Abstract BACKGROUND The MAPK/ERK pathway is involved in cell growth and proliferation, and mutations in the BRAF paralog of this pathway have made it an oncogene of interest in pediatric cancer. Previous studies have identified that BRAF mutations as well as BRAF-KIAA1549 fusions are common in intracranial low-grade gliomas (LGGs). Fewer studies have tested for the presence of these genetic aberrations 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 analyzed 46 spinal LGGs from children age 1–25 years from two institutions, Children’s Hospital Colorado (CHCO) and The Hospital for Sick Children (Sick Kids) for the presence of BRAF fusions or mutations. Data was correlated with clinical information. A 67 gene panel additionally screened for other possible genetic abnormalities of interest in the patient cohort from CHCO. In the Sick Kids cohort, BRAFV600E was tested for by ddPCR and IHC while BRAF fusions where detected by FISH, RT-PCR or Nanostring platform. RESULTS Of the 31 patient samples who underwent fusion analysis, 13 (42%) harbored the BRAF-KIAA1549 fusion. Overall survival (OS) for patients confirmed positive for BRAF-KIAA1549 was 100% compared to 76% for fusion negative patients. Other mutations of interest were also identified in this patient cohort including BRAFV600E, STK11, PTPN11, H3F3A, APC, TP53, PIK3CA (polymorphism), FGFR1, and CDKN2A deletion. CONCLUSION BRAF-KIAA1549 was seen in higher frequency than BRAFV600E or other genetic aberrations in pediatric spinal LGGs and trends towards longer OS although 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".