High prevalence of KRAS/BRAF somatic mutations in brain and spinal cord arteriovenous malformations
Bibliographic record
Abstract
Brain and spinal arteriovenous malformations are congenital lesions causing intracranial haemorrhage or permanent disability especially in young people. We investigated whether the vast majority or all brain and spinal arteriovenous malformations are associated with detectable tumour-related somatic mutations. In a cohort of 31 patients (21 with brain and 10 with spinal arteriovenous malformations), tissue and paired blood samples were analysed with ultradeep next generation sequencing of a panel of 422 common tumour genes to identify the somatic mutations. We used droplet digital polymerase chain reaction to confirm the panel sequenced mutations and identify the additional low variant frequency mutations. The association of mutation variant frequencies and clinical features were analysed. The average sequencing depth was 1077 ± 298×. High prevalence (87.1%) of KRAS/BRAF somatic mutations was found in brain and spinal arteriovenous malformations with no other replicated tumour-related mutations. The prevalence of KRAS/BRAF mutation was 81.0% (17 of 21) in brain and 100% (10 of 10) in spinal arteriovenous malformations. We detected activating BRAF mutations and two novel mutations in KRAS (p.G12A and p.S65_A66insDS) in CNS arteriovenous malformations for the first time. The mutation variant frequencies were negatively correlated with nidus volumes of brain (P = 0.038) and spinal (P = 0.028) arteriovenous malformations but not ages. Our findings support a causative role of somatic tumour-related mutations of KRAS/BRAF in the overwhelming majority of brain and spinal arteriovenous malformations. This pathway homogeneity and high prevalence implies the development of targeted therapies with RAS/RAF pathway inhibitors without the necessity of tissue genetic diagnosis.10.1093/brain/awy307_video1awy307media15978667388001.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".