LG-46INFERIOR OUTCOME AND POOR RESPONSE TO CONVENTIONAL THERAPIES IN PEDIATRIC LOW-GRADE GLIOMAS HARBORING THE BRAF V600E MUTATION
Bibliographic record
Abstract
BACKGROUND: Pediatric low-grade gliomas (PLGG) are histologically and clinically diverse and molecular stratification is desperately needed. The BRAF-V600E mutation is highly targetable for therapy but its biological relevance and impact on PLGG outcome is controversial. METHODS: We determined RAS pathway and other secondary mutations in all patients diagnosed with PLGG since 1985 (n = 854 patients biopsied), 506 had sufficient tissue. Tumors were tested for these alterations using the QX200™Droplet Digital™PCR, Nanostring and SNP array technologies. Long-term clinical data and imaging response were collected on all patients. These data were validated on a large international cohort of V600E mutant PLGG. RESULTS: BRAF-V600E mutation was observed in 19 % of PLGG. Mutations were observed across a broad spectrum of histologies including less frequently reported such as pilocytic, pilomyxoid, and diffuse astrocytomas. Importantly, BRAF-V600E positive PLGG were common in midline locations, which are often not biopsied. BRAF-V600E PLGG had inferior long-term outcome when compared with wild-type (WT) cases. Fifteen-year PFS was 7 ± 6% and 64 ± 12% for BRAF-V600E and WT-PLGG respectively (p < 0.05). This translated to OS of 57 ± 16% and 94 ± 6%, respectively (p < 0.05). Secondary molecular alterations stratified BRAF-V600E tumors into low and high-risk tumors (p < 0.05). Specific quantitative imaging analysis revealed response to non-targeted chemotherapy in only 10% BRAF-V600E PLGG, while BRAF-V600E inhibition resulted in a greater response. CONCLUSIONS: In the largest assembled cohort of molecularly characterized pediatric LGG to date, BRAF-V600E mutations identify a unique group of PLGG with distinct biology and survival. Early detection and targeted therapies may transform the management and outcome of these children.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".