LG-58THERAPEUTIC IMPLICATIONS OF MOLECULAR BIOMARKERS IN INFANTS WITH PEDIATRIC LOW-GRADE GLIOMA
Bibliographic record
Abstract
BACKGROUND: Pediatric low-grade gliomas (PLGG) are the most common childhood brain tumors, largely driven by alterations of the Ras/BRAF/MAPK pathway. Though considered benign, infants (<3 years of age) have worse survival and significant long-term endocrine, visual and neurological sequelae. The factors that impact outcome of these patients remain poorly understood. METHODS: Retrospective study of all patients diagnosed with PLGG under the age of 3 years at the hospital for Sick Children in Toronto from 1985–2015 (n = 169). Clinical data were ascertained by chart review. PLGG-associated molecular biomarkers (BRAF fusion, FGFR fusion, BRAFV600E mutation) were defined using Nanostring and droplet digital PCR assays. RESULTS: At a median follow-up of 13.12 years (range, 0.14–27.4), fifteen-year overall and progression-free survival for the entire cohort was 88% and 47%, respectively. The main clinical adverse risk factors included age <12 months, midline location and incomplete resection (p < 0.001). Molecular analysis of 74 optic pathway PLGG revealed superior long-term survival for children with NF1 mutations (n = 37) with a 15-year OS of 96% and 66% for NF1 mutated vs WT tumors, respectively (p = 0.01). Strikingly, further analysis of RAS alterations in this group revealed significantly worse outcome for infants with BRAF-V600E mutated PLGG as compared to BRAF-fused or other alterations (p < 0.01). The distinct group of BRAFV600E mutated optic pathway PLGG accounted for one third of non-NF1 patients and was characterized by poor response to chemotherapy. CONCLUSIONS: RAS pathway alterations are frequent and targetable events in infants with optic pathway PLGG.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".