Targeted detection of genetic alterations reveal the prognostic impact of H3K27M and MAPK pathway aberrations in paediatric thalamic glioma
Bibliographic record
Abstract
Paediatric brain tumours arising in the thalamus present significant diagnostic and therapeutic challenges to physicians due to their sensitive midline location. As such, genetic analysis for biomarkers to aid in the diagnosis, prognosis and treatment of these tumours is needed. Here, we identified 64 thalamic gliomas with clinical follow-up and characterized targeted genomic alterations using newly optimized droplet digital and NanoString-based assays. The median age at diagnosis was 9.25 years (range, 0.63-17.55) and median survival was 6.43 (range, 0.01-27.63) years. Our cohort contained 42 and 22 tumours reviewed as low and high grade gliomas, respectively. Five (12 %) low grade and 11 (50 %) high grade gliomas were positive for the H3F3A/HIST1H3B K27M (H3K27M) mutation. Kaplan-Meier survival analysis revealed significantly worse overall survival for patients harbouring the H3K27M mutation versus H3F3A/HIST1H3B wild type (H3WT) samples (log-rank p < 0.0001) with a median survival of 1.02 vs. 9.12 years. Mitogen-activated protein kinase (MAPK) pathway activation via BRAF or FGFR1 hotspot mutations or fusion events were detected in 44 % of patients, and was associated with long-term survival in the absence of H3K27M (log-rank p < 0.0001). Multivariate analysis demonstrated H3K27M status and high grade histology to be the most significant independent predictors of poor overall survival with hazard ratios of 6.945 and 7.721 (p < 0.0001), respectively. In contrast, MAPK pathway activation is a predictor of favourable patient outcome, although not independent of other clinical factors. Importantly, we show that low grade malignancies may harbour H3K27M mutations and that these tumours show a dismal survival compared to low grade H3WT cases. Our data strongly supports the inclusion of targeted genetic testing in childhood thalamic tumours to most accurately stratify patients into appropriate risk groups.
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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 | 0.000 |
| 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) | 0.001 | 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".