Germline and somaticNF1 gene mutation spectrum in NF1-associated malignant peripheral nerve sheath tumors (MPNSTs)
Bibliographic record
Abstract
About 10% of neurofibromatosis type 1 (NF1) patients develop malignant peripheral nerve sheath tumors (MPNSTs) and represent considerable patient morbidity and mortality. Elucidation of the genetic mechanisms by which inherited and acquired NF1 disease gene variants lead to MPNST development is important. A study was undertaken to identify the constitutional and somatic NF1 mutations in 34 MPNSTs from 27 NF1 patients. The NF1 germline mutations identified in 22 lymphocytes DNA from these patients included seven novel mutations and a large 1.4-Mb deletion. The NF1 germline mutation spectrum was similar to that previously identified in adult NF1 patients without MPNST. Somatic NF1 mutations were identified in tumor DNA from 31 out of 34 MPNSTs, of which 28 were large genomic deletions. The high prevalence (>90%) of such deletions in MPNST contrast with the =or<20% found in benign neurofibromas and is indicative of the involvement of different mutational mechanisms in these tumors. Coinactivation of the TP53 gene by deletion, or by point mutation along with NF1 gene inactivation, is known to exacerbate disease symptoms in NF1, therefore TP53 gene inactivation was screened. DNA from 20 tumors showed evidence for loss of heterozygosity (LOH) across the TP53 region in 11 samples, with novel TP53 point mutations in four tumors.
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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 | 0.001 |
| 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".