Molecular Characterization of the Spectrum of Peripheral Nerve Sheath Tumors
Bibliographic record
Abstract
Malignant peripheral nerve sheath tumor (MPNST) is a highly aggressive sarcoma, and the most lethal form of cancer in the context of neurofibromatosis type 1. The current standard of care includes surgery plus radiation, with no effective chemo or targeted therapeutic options. Here we examined the genomic and epigenomic drivers of MPNSTs, analyzing 108 tumors spanning the full spectrum of peripheral nerve sheath tumors, using a multiplatform integrated approach to identify drivers of MPNST. We established through multiple unsupervised analyses of methylome and transcriptome profiles that there exist two distinct pathways of malignant transformation leading to MPNSTs, one through SHH pathway activation (MPNST-G1) and the other through WNT pathway activation (MPNST-G2). We discovered distinct copy number aberration, mutational profiles, and targetable oncogenic programs that define each sub-group. Further, single nuclear RNA sequencing characterizes the complex cellular architecture that defines each subgroup, with MPNST-G1 and MPNST-G2 resembling neural crest-like and Schwann cell precursor-like cells, respectively. Additionally, in-vitro and in-vivo models confirm that inhibition of the SHH pathway can prevent growth and malignant progression of MPNSTs, proving sonidegib to be novel therapeutic option in these lethal cancers.
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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 | 0.001 | 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) | 0.002 | 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".