Improving genetic diagnostic yield in familial and sporadic cerebral cavernous malformations: detection of copy number and deep Intronic variants
Bibliographic record
Abstract
Cerebral cavernous malformations (CCMs) are intracranial vascular lesions associated with risk of haemorrhages and seizures. While the majority are sporadic and often associated with somatic variants in PIK3CA and MAP3K3, around 20% are familial with germline variants in one of three CCM genes-KRIT1/CCM1, CCM2 and PDCD10/CCM3. We performed comprehensive phenotyping and genetic analysis of nine multiplex families and ten sporadic individuals with CCM. In the familial cases, initial standard analyses had a low yield, we therefore searched for small copy number changes and deep intronic variants. Subsequently, pathogenic germline variants in KRIT1/CCM1 or CCM2 were identified in all 9 multiplex families. Single or multiple exon deletions or splice site variants in KRIT1/CCM1 were found in 3/9 families. Where cavernous malformation tissue was available, second hit somatic PIK3CA variants were identified in 4/7 individuals. These 4 individuals were from separate families with germline KRIT1/CCM1 variants. In 8/10 sporadic cases, we detected recurrent pathogenic somatic PIK3CA, MAP3K3 or CCM2 variants. All familial cases had multiple CCMs, whereas the sporadic cases had a single lesion only, which was in the temporal lobe in 9/10 individuals. Our comprehensive approach interrogating deep intronic variants combined with detection of small copy number variants warrants implementation in standard clinical genetic testing pipelines to increase diagnostic yield. We also build on the established second hit germline and somatic variant mechanism in some CCM lesions. Genetic diagnosis has clinical implications such as reproductive counselling and provides potential eligibility for precision medicine therapies to treat rapidly growing CCMs.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".