Proof-of-principle of NF1 Gene Therapy in plexiform neurofibroma mice models
Bibliographic record
Abstract
ABSTRACT Neurofibromatosis type I is a rare neurocutaneous syndrome characterized by the development of disfiguring neurofibroma tumors with unmet clinical needs. As Neurofibromatosis Type I is a monogenic disease, the development of gene therapy is highly attractive, but it is currently unknown if rescuing the NF1 gene in established neurofibroma is sufficient for tumor regression. Here, we test this hypothesis by building two novel NF1 mouse models with reversible NF1 expression. In the first model, the human NF1 −/- Schwann cells named ipNF95.11b were genetically modified with a doxycycline-inducible full-length mouse Nf1 gene. One month after cells implantation in the sciatic nerve, mice were split into 2 groups. Strikingly, all sciatic nerves from mice allowed to drink doxycycline water for one month display complete normalization of the sciatic nerve histologically (n=6 sciatic nerves) whereas 83% (5 out of 6 sciatic nerves) develop or maintain a neurofibroma when drinking regular water. In the second model, the human NF1 +/- Schwann cells named ipNF95.11c were genetically modified with a doxycycline-inducible potent shRNA against the NF1 mRNA transcript. Strikingly, doxycycline withdrawal after neurofibroma establishment allowed complete normalization (n=4 sciatic nerves), whereas all sciatic nerves showed evidence of neurofibroma when kept on doxycycline (n=4 sciatic nerves). Thus, we proof-of-principle NF1 Gene Therapy in plexiform neurofibroma mice models.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".