HGG-18. GLIOMAS IN ADOLESCENTS AND YOUNG ADULTS SHARE SEVERAL MOLECULAR FEATURES WITH THEIR PEDIATRIC COUNTERPARTS
Bibliographic record
Abstract
The molecular features of both pediatric and adult gliomas have been extensively studied for their diagnostic and clinical value. However, a population-based characterization of the adolescent/young adult population (AYA) has not been well documented. It remains unclear whether the frequency and prognostic significance of common alterations in pediatric gliomas carry over to the AYA population. We assembled a cohort of 101 gliomas treated at St. Michael’s Hospital from which 86 samples had sufficient material for molecular characterization. MRI and pathology reviews confirmed accurate diagnosis. H3F3A, BRAF, and IDH1 mutations were detected using the Bio-Rad QX200 Droplet-Digital system. Our cohort consisted of 31 tumours diagnosed as low grade (23% grade I, 58% grade II, and 19% LGG, NOS) and 70 diagnosed as high grade (40% grade III, 59% grade IV, and 1% HGG, NOS). Median age of the patients was 29 years (18–39). Of the 86 tested for molecular alterations, 10 (12%) of the tumours tested positive for H3_K27M tumours, with a median age of 21.5 years (18–35). H3_K27M mutations occurred exclusively in midline tumours (10/32, 31%). K27M mutations were observed in both high and low grade tumours (7 and 3, respectively). BRAF_V600E was detected in 6 (7%) of patients with a median age of 26.5 years (18–37). BRAF_V600E mutations were detected in both midline and hemispheric tumours and across all histological grades. IDH1_R132H mutations were detected in 31 (36%) patients with a median age of 29 (21–39). IDH1 mutations appeared in both midline and hemispheric tumours and across all histologies. All mutations detected occurred mutually exclusive to one another. The work presented here shows the incidence of classically pediatric mutations in an AYA cohort. It is important that clinicians be aware that these mutations may be present in AYAs and may have an impact on their clinical course.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".