LGG-50. INTEGRATED MOLECULAR AND CLINICAL ANALYSIS OF 1,000 PEDIATRIC LOW-GRADE GLIOMAS UNCOVERS NOVEL SUBGROUPS FOR CLINICAL RISK STRATIFICATION
Bibliographic record
Abstract
Abstract Pediatric low-grade gliomas (pLGG) are primarily driven by genetic alterations in the RAS/MAPK pathway, most commonly involving BRAF of NF1. Despite their molecular convergence, pLGG often show unexplained variability in their clinical outcome. To address this, we molecularly characterized a cohort of >1,000 clinically annotated pLGG. 84% of cases harbored a detectable driver mutation. The remaining 16% of patients nonetheless showed RAS/MAPK pathway up-regulation at the RNA level. The clinical presentation and outcome of pLGG appeared highly variable and linked to the alteration type: re-arrangement or SNV. Re-arrangement-driven tumors were diagnosed at a younger age (6.6 versus 10.9 years, p<0.0001), enriched for WHO grade I histology (88% versus 66%, p<0.0001), infrequently progressed (27% versus 46%, p<0.0001), and rarely resulted in death (3 versus 13%, p<0.0001) as compared to SNV-driven tumors. These included the rarest molecular drivers of pLGG, for which we now have the clinicopathologic features of including MYB, MYBL1, FGFR2 fusions, FGFR1-TACC1, FGFR1 SNVs, IDH1 p.R132H, and H3.3 p.K27M. Utilizing this information, we suggest novel risk categories of pLGG that effectively predicted patient outcome. Low-risk tumors progressed infrequently and rarely succumbed to their disease (10-year PFS of 71% and OS of 98%). Intermediate-risk pLGG had a 10-year PFS and OS of 35% and 90%, respectively. High risk pLGG almost invariably progressed (10-year PFS of 0%) and these patients often succumbed to their disease (10-year OS of 41%). These data highlight the biological and clinical differences between pLGG subtypes and offers molecular based risk stratification to these cancers.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".