Loss of neurofibromin accelerates uveal and dermal melanoma formation driven by GNAQ
Bibliographic record
Abstract
ABSTRACT Neurofibromin is a very large and complex tumor suppressor, whose loss can synergize with other MAPK pathway mutations to promote melanoma in the skin. In this paper, we investigated whether NF1 loss has a role in other melanomas, such as those that form in the dermis or eye (uveal tract). We found that heterozygous 17q11.2 loss that includes the NF1 locus is an uncommon, but recurrent phenomenon in human dermal and uveal melanomas described previously. We studied the effects of Nf1 haploinsufficiency in mice expressing oncogenic GNAQ Q209L in melanocytes and Schwann cells of peripheral nerves using the Plp1-creERT transgene, with tamoxifen given at 5 weeks of age. Nf1 haploinsufficiency accelerated dermal and uveal melanoma formation. We studied the effects of Nf1 loss in these melanomas using RNAseq. Many of the differentially expressed genes were homologous to genes whose expression correlates with prognosis in human uveal melanoma. Of particular interest was the up-regulation of cAMP signaling and its connection to protein kinase A, which is mutant in malignant melanotic nerve sheath tumors (MMNSTs). An unexpected finding was that oncogenic GNAQ was sufficient by itself to drive peripheral nerve sheath-like neoplasms in the mice. Hence, these studies reveal new insight into both melanocyte and Schwann cell transformation.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".