CNSC-53. SHH ACTIVATION IN DEDIFFERENTIATION DURING TUMOR PROGRESSION IN MPNST
Bibliographic record
Abstract
Abstract BACKGROUND Malignant peripheral nerve sheath tumors (MPNST) are highly aggressive sarcomas with little progress on outcomes and treatment strategies. Previously, unsupervised analyses of methylome and transcriptome profiles of 108 peripheral nerve sheath tumors uncovered 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 whether the activation of the SHH pathway in MPNST-G1 cell lines induces overexpression of factors known to play canonical roles in early neural crest cell specification (TWIST1, SNAI2, PAX3, PAX6, SOX9, OTX2) and to investigate the role of Src activation on SHH pathway-induced dedifferentiation. We hypothesize that MPNST-G1 cells will exhibit SHH pathway activation, synergizing with Src to induce the overexpression of early neural crest cell (ENCC) transcription factors. METHODS SHH pathway activation, expression of ENCC transcription factors, and effects of inhibitors were analyzed in MPNST-G1 cells using RT-PCR, western blotting, Alamar Blue assay, Trypan Blue assay, and Soft Agar Transformation assay. RESULTS Compared to MPNST-G2 cells, MPNST-G1 cells displayed SHH-pathway activation, SMO-dependent activation of Src, and elevated expression of the ENCC transcription factors. Sonidegib (SMO inhibitor) and Dasatinib (Src inhibitor) were able to revert dedifferentiation. CONCLUSIONS The SHH-pathway cooperates with Src to promote expression of important transcription factors in dedifferentiation. This finding provides insights into the transformation process of MPNST and novel therapeutic options.
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 | 0.000 | 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) | 0.002 | 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".