SHH PATHWAY ACTIVATION DRIVES MALIGNANT TRANSFORMATION IN A SUBSET OF MPNSTS AND IS A POTENTIAL THERAPEUTIC TARGET
Bibliographic record
Abstract
Abstract BTFC travel award recipient Malignant peripheral nerve sheath tumors (MPNSTs) are highly aggressive Schwann-cell derived sarcomas and the leading cause of mortality in Neurofibromatosis Type 1. The molecular pathways driving malignant transformation is not well understood. In this study, we leveraged multi-platform genomic and epigenomic profiling of human neurofibromas and MPNSTs to identify targetable molecular pathways that lead to malignant transformation. Fresh-frozen tumors (N = 108) were studied including methylation profiling, RNA sequencing, and whole exome sequencing. Unsupervised consensus clustering of methylome and transcriptome data identified 2 distinct MPNST subgroups. Pathway analysis showed that MPNST-G1 tumors are characterized by SHH pathway activation (NES=1.9952, p<0. 0001, FDR<0.001), while MPNST-G2 tumors are characterized by WNT pathway activation (NES=1.7630, p<0.0001, FDR=0.0058). We observed significantly higher rates of PTCH1 deletions, a negative regulator of SHH pathway, in MPNST-G1 (62.5% vs 0%, Fisher Exact Test=0.0065, p<0.05) and PTCH1 promoter hypermethylation (mean beta value: 0.2899 vs. 0.06119, p<0.05). A computational drug screen identified sonedigib, a SHH pathway inhibitor, as a potential treatment option for MPSNT-G1. We validated the importance of SHH pathway in malignant transformation by knocking out PTCH1 in neurofibroma cell lines and demonstrated increased cellular proliferation, cellular migration and invasion. In addition, treatment with sonedigib decreased cellular viability in MPNST cell lines and improved survival in mouse xenograft models. These results suggest a targeted approach should be taken for treating MPNSTs, and SHH and WNT pathway inhibition may be promising avenue for therapeutic development.
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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) | 0.001 | 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".