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Record W4388589457 · doi:10.1093/neuonc/noad179.0158

CSIG-02. SHH PATHWAY ACTIVATION DRIVES NEURAL CREST CELL SIGNATURE IN A SUBGROUP OF MPNSTS

2023· article· en· W4388589457 on OpenAlexaff
Suganth Suppiah, Jeffrey Liu, Mira Li, Mohsen Heidari, Rebeca Yakubov, Severa Bunda, Gelareh Zadeh

Bibliographic record

VenueNeuro-Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicNeuroblastoma Research and Treatments
Canadian institutionsToronto Western HospitalMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsNeural crestCancer researchBiologyNeurofibromaNeural stem cellNeurofibromatosisCell biologyPathologyMedicineStem cellGenetics

Abstract

fetched live from OpenAlex

Abstract INTRODUCTION Neurofibromatosis Type 1 is hereditary tumor predisposition syndrome that results in the development of innumerable nerve sheath tumors that fall within the spectrum of benign, premalignant and malignant lesions. Our previous work has demonstrated that SHH pathway activation drives malignant transformation in a subset of malignant peripheral nerve sheath tumors (MPNSTs). Here, we demonstrate that SHH pathway activation induces a neural crest cell-like state in a subset of MPNSTs. METHODS We performed single nuclear RNA sequencing on 6 tumors (5 MPNSTS and 1 atypical neurofibroma). We performed in vitro experiments to assess the correlation between SHH pathway activation and neural crest signatures. We inhibited SHH pathway with sonidegib, a SMO inhibitor, in MPNST cell lines and performed RT-PCR to assess markers of neural crest cell signatures. In addition, we assessed cellular viability with treatment. RESULTS We observed that cells that express SMO at high levels, with SHH pathway activation, express neural crest cell makers. Single cell trajectory analysis further demonstrates a pseudotemporal continuum from atypical neurofibromas (Schwann cells), MPNSTs (Schwann cell precursor cells) and other MPNSTs (neural crest cells), which supports that these tumors fall along the developmental trajectory of neural crest cell lineage. SHH pathway activity was high in S462TY MPNST cell line and low in STS-26T MPNST cell line. S462TY also demonstrated prominent elevation in expression of neural crest cell markers TWIST1, SOX9, SNAI2, OTX2, PAX4 and PAX6 . We found that treatment with sonidegib treatment attenuates the expression of neural crest cell signatures. Finally, inhibiting SHH pathway activation reduced cellular viability in MPNST cell lines. CONCLUSION SHH pathway activation drives dedifferentiation into a neural crest cell-like state in a subset of MPNSTs. We confirm that inhibiting SHH pathway activity in a subset of MPNSTS prevent growth and malignant progression, providing a rational for future clinical trials.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0020.001

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 head0.024
GPT teacher head0.316
Teacher spread0.292 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2023
Admission routes1
Has abstractyes

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