LG-68THE GENETIC AND CLINICAL LANDSCAPE IN PEDIATRIC LOW GRADE GLIOMA; PRELIMINARY RESULTS FROM PLGG TASKFORCE
Bibliographic record
Abstract
BACKGROUND: Alterations of the RAS/MAPK pathway have been identified as the major driver of pediatric low-grade gliomas (PLGG) however the prevalence of specific lesions and prognostic implications are unknown. METHODS: We undertook a population based study of all PLGG diagnosed from 2000-2015. Analysis included QX200™Droplet Digital™PCR, NanoString and copy number profiling. Findings were correlated with pathology, demographics and outcome. RESULTS: Sufficient tissue for BRAF-V600E, BRAF-fusions, FGFR1-TACC1 and MYBL1 was available on 310 out of 450 biopsied patients. In decreasing frequency were BRAF-KIAA1549 fusion (38%), BRAF-V600E (14%), FGFR1-TACC1 (3%) and MYB1 alteration (1%). 44% PLGG had none of these alterations. The percent represented by each alteration varied depending on pathology and tumor location. In addition to the high prevalence of BRAF-KIAA1549 in pilocytic astrocytoma, it was found also in ganglioglioma (13%) and diffuse astrocytoma (10%). In low-grade astrocytoma, NOS BRAF-V600E and BRAF-KIAA1549 were evenly distributed, each found in 20% of cases, while in pleomorphic xanthoastrocytoma and ganglioglioma BRAF-V600E was most frequent. BRAF-KIAA1549 was found in only 10% of non-midline gliomas, MYBL1 is most prevalent in diffuse astrocytomas and exclusive to the hemispheres, while BRAF-V600E prevalence was similar in hemispheres, diencephalon and brainstem (20-30%). BRAF-V600E PLGG has significantly worse PFS when compared to NF-1 or BRAF-fused. All patients with FGFR1-TACC1 and MYBL1 are still alive. CONCLUSIONS: Our study constitutes the largest PLGG cohort assembled to our knowledge and provides a robust characterization of the clinical and genomic landscape of PLGG. This has tremendous implications in the planning and accessibility of targeted therapies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".