SHH PATHWAY ACTIVATION IN DEDIFFERENTIATION DURING TUMOR PROGRESSION IN MPNST
Bibliographic record
Abstract
Abstract Malignant peripheral nerve sheath tumours (MPNST) are highly aggressive sarcomas with little progress on outcomes and treatment strategies. Previous work conducted in our lab used unsupervised analyses of methylome and transcriptome profiles of 108 peripheral nerve sheath tumours to uncover two subgroups of MPNSTs that predict progression-free survival, MPNST-G1 (characterized by SHH pathway activation) and MPNST-G2 (characterized by WNT/ß-catenin/CCND1 pathway activation). Further, single nuclear RNA-sequencing revealed that MPNST-G1 and MPNST-G2 cells resemble neural crest-like and Schwann cell precursor-like cells, respectively. Purpose & Hypothesis: To examine the expression of transcription factors (TWIST1, SOX9, SNAI2, OTX2, PAX3, and PAX6) known to play canonical roles in the early neural crest cell specification in MPNST-G1 cells. We speculate that MPNST-G1 cells will display overexpression of these transcription factors compared to MPNST-G2 cells and that Sonidegib (SMO inhibitor) will revert dedifferentiation by decreasing activation of the SHH pathway. METHODS: Dedifferentiation transcription factor expression and the effects of SMO activation and inhibition in MPNST-G1 and MPNST-G2 cells were analyzed using RT-PCR and western blotting. Alamar blue and Trypan blue assays were used to determine the effect of SMO inhibition on proliferation. RESULTS: Compared to MPNST-G2 cells, MPNST-G1 cells displayed elevated expression of dedifferentiation transcription factors, SMO inhibition was able to reverse these effects. Conversely SMO activation induced the expression of these factors and induced an increase in proliferation. CONCLUSIONS: The SHH pathway activation promotes the expression of important transcription factors in dedifferentiation. This finding provides insights into the transformation process of MPNST and novel therapeutic options for these lethal cancers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".