Somatic Activating <i>KRAS</i> Mutations in Arteriovenous Malformations of the Brain
Bibliographic record
Abstract
BACKGROUND: Sporadic arteriovenous malformations of the brain, which are morphologically abnormal connections between arteries and veins in the brain vasculature, are a leading cause of hemorrhagic stroke in young adults and children. The genetic cause of this rare focal disorder is unknown. METHODS: We analyzed tissue and blood samples from patients with arteriovenous malformations of the brain to detect somatic mutations. We performed exome DNA sequencing of tissue samples of arteriovenous malformations of the brain from 26 patients in the main study group and of paired blood samples from 17 of those patients. To confirm our findings, we performed droplet digital polymerase-chain-reaction (PCR) analysis of tissue samples from 39 patients in the main study group (21 with matching blood samples) and from 33 patients in an independent validation group. We interrogated the downstream signaling pathways, changes in gene expression, and cellular phenotype that were induced by activating KRAS mutations, which we had discovered in tissue samples. RESULTS: ) in endothelial cells in vitro induced increased ERK (extracellular signal-regulated kinase) activity, increased expression of genes related to angiogenesis and Notch signaling, and enhanced migratory behavior. These processes were reversed by inhibition of MAPK (mitogen-activated protein kinase)-ERK signaling. CONCLUSIONS: We identified activating KRAS mutations in the majority of tissue samples of arteriovenous malformations of the brain that we analyzed. We propose that these malformations develop as a result of KRAS-induced activation of the MAPK-ERK signaling pathway in brain endothelial cells. (Funded by the Swiss Cancer League and others.).
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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 | 0.000 |
| 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".