Single-cell transcriptomic profiling of malignant peripheral nerve sheath tumors
Bibliographic record
Abstract
Malignant peripheral nerve sheath tumors (MPNSTs) are aggressive sarcomas arising from Schwann cells and characterized by marked cellular and molecular heterogeneity. Although bulk multi-omic studies have provided valuable insights into MPNST biology, recent advances in single-cell profiling have deepened our understanding of the tumor microenvironment and molecular mechanisms underlying malignant transformation. Single cell analyses have revealed distinct Schwann cell-like, malignant neural crest-like, immune, and stromal cellular subpopulations within MPNSTs and their precursor lesions. Comparative profiling of MPNSTs, neurofibromas, and atypical neurofibromatous neoplasms of uncertain biologic potential, suggest that MPNST progression involves Schwann cell dedifferentiation into a more primitive, stem-like state. In this review, we summarize key discoveries from single-cell characterization studies, and discuss how these findings illuminate MPNST tumorigenesis, cellular plasticity, and potential therapeutic vulnerabilities.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".