Gene Expression Profiling Reveals Unique Molecular Subtypes of Neurofibromatosis Type I‐associated and Sporadic Malignant Peripheral Nerve Sheath Tumors
Bibliographic record
Abstract
Malignant peripheral nerve sheath tumors (MPNSTs) are highly aggressive Schwann cell neoplasms that are frequently associated with Type I Neurofibromatosis (NF1) and respond poorly to current therapeutic regimens. To better understand the molecular heterogeneity of these tumors, we performed gene expression profiling on 25 NF1-associated and 17 sporadic MPNSTs using oligonucleotide microarrays representing approximately 8100 unique human gene transcripts. Using several previously reported statistical approaches, we were unable to identify a molecular signature that could reliably distinguish between NF1-associated and sporadic MPNSTs in independent training and test sample sets. However, using an unsupervised clustering approach, we identified an extensive gene expression signature that distinguished 9 of the 42 tumors analyzed. This signature corresponded to relative overexpression of transcripts associated with neuroglial differentiation (NCAM, MBP, L1CAM, P1P) and relative down-regulation of proliferation and growth factor associated transcripts (IGF2, FGFR1, MDK, Ki67). All tumors with this gene expression signature lacked expression of EGFR and all but one tumor were derived from patients with NF1. However, there were no other obvious associations with histological grade, tumor site, metastasis, recurrence, age, or patient survival. We conclude that distinct molecular classes of MPNST exist and that the ability to stratify these tumors based on unique and biologically relevant gene expression profiles may be important for future targeted therapeutics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".