Targeted therapy for capillary-venous malformations
Bibliographic record
Abstract
Sporadic venous malformations are genetic conditions primarily caused by somatic gain-of-function mutation of PIK3CA or TEK, an endothelial transmembrane receptor signaling through PIK3CA. Venous malformations are associated with pain, bleedings, thrombosis, pulmonary embolism, esthetic deformities and, in severe cases, life-threatening situations. No authorized medical treatment exists for patients with venous malformations. Here, we created a genetic mouse model of PIK3CA-related capillary venous malformations that replicates patient phenotypes. We showed that these malformations only partially signal through AKT proteins. We compared the efficacy of different drugs, including rapamycin, a mTORC1 inhibitor, miransertib, an AKT inhibitor and alpelisib, a PI3Kα inhibitor at improving the lesions seen in the mouse model. We demonstrated the effectiveness of alpelisib in preventing vascular malformations' occurrence, improving the already established ones, and prolonging survival. Considering these findings, we were authorized to treat 25 patients with alpelisib, including 7 children displaying PIK3CA (n = 16) or TEK (n = 9)-related capillary venous malformations resistant to usual therapies including sirolimus, debulking surgical procedures or percutaneous sclerotherapies. We assessed the volume of vascular malformations using magnetic resonance imaging (MRI) for each patient. Alpelisib demonstrated improvement in all 25 patients. Vascular malformations previously considered intractable were reduced and clinical symptoms were attenuated. MRI showed a decrease of 33.4% and 27.8% in the median volume of PIK3CA and TEK malformations respectively, over 6 months on alpelisib. In conclusion, this study supports PI3Kα inhibition as a promising therapeutic strategy in patients with PIK3CA or TEK-related capillary venous malformations.
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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 | 0.000 | 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".