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Record W7131891463 · doi:10.1093/noajnl/vdaf243

Single-cell transcriptomic profiling of malignant peripheral nerve sheath tumors

2025· article· en· W7131891463 on OpenAlexaff
Gui Chloe, Severa Bunda, Mira Li, Olivia Singh, Suganth Suppiah

Bibliographic record

VenueNeuro-Oncology Advances · 2025
Typearticle
Languageen
FieldMedicine
TopicNeurofibromatosis and Schwannoma Cases
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsMalignant peripheral nerve sheath tumorStromal cellSchwann cellTranscriptomeSarcomaNerve sheath neoplasmGene expression profiling

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.823

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.283
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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