HG-71ULTRA-SENSITIVE DETECTION OF HOTSPOT MUTATIONS IN PEDIATRIC THALAMIC TUMOURS TO REVEAL NEGATIVE PROGNOSTIC MARKERS
Bibliographic record
Abstract
Pediatric gliomas are the most commonly diagnosed brain cancer in children. Despite accounting for only 5% of pediatric brain neoplasms, gliomas arising in the thalamus present a significant challenge for treating physicians. We collected a population-based cohort of 66 radiologically confirmed thalamic tumours with long-term follow-up. No bi-thalamic cases were included. On central review, the cohort consisted of 43 low grade astrocytomas (LGA) and 23 high grade astrocytomas (HGA). Genomic alterations were characterized using newly optimized digital droplet and nanoString assays. Five (12%) LGAs and eleven (48%) HGAs tested positive for H3-K27M. Kaplan-Meier analysis revealed significantly worse survival of thalamic glioma patients harbouring the H3-K27M mutation versus wild type samples (log rank p < 0.001) with a median survival of 1.02 vs. 9.12 years, respectively. When separated by histologic grade, H3-K27M status remained a prognostic marker in LGA (p < 0.0001), but failed to maintain statistical significance in the HGA cohort (p = 0.0599). BRAF-V600E and BRAF-Fusion events were detected in 10 and 14 patients respectively. Interestingly, tumours with BRAF-fusion events showed robust survival, with no patients succumbing to their disease (median follow-up 11.5 years). Multivariate analysis demonstrated H3-K27M status and histology to be the most significant predictors of survival with hazard ratios of 8.7 and 12.7 (P < 0.001), respectively. This work substantiates the need for H3-K27M testing in conjunction with histological grading in dictating appropriate diagnosis. Further, we show that low grade malignancies harbouring H3-K27M mutations represent a newly identified subset of thalamic tumours with a unique survival pattern when compared to low grade H3-WT cases.
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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) | 0.001 | 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".