Melanomas require HEDGEHOG-GLI signaling regulated by interactions between GLI1 and the RAS-MEK/AKT pathways
Bibliographic record
Abstract
Melanoma is one of the most aggressive cancers, and its incidence is increasing. These tumors derive from the melanocyte lineage and remain incurable after metastasis. Here we report that SONIC HEDGEHOG (SHH)-GLI signaling is active in the matrix of human hair follicles, and that it is required for the normal proliferation of human melanocytes in culture. SHH-GLI signaling also regulates the proliferation and survival of human melanomas: the growth, recurrence, and metastasis of melanoma xenografts in mice are prevented by local or systemic interference of HH-GLI function. Moreover, we show that oncogenic RAS-induced melanomas in transgenic mice express Gli1 and require Hh-Gli signaling in vitro and in vivo. Finally, we provide evidence that endogenous RAS-MEK and AKT signaling regulate the nuclear localization and transcriptional activity of GLI1 in melanoma and other cancer cells. Our data uncover an unsuspected role of HH-GLI signaling in melanocytes and melanomas, demonstrate a role for this pathway in RAS-induced tumors, suggest a general integration of the RAS/AKT and HH-GLI pathways, and open a therapeutic approach for human melanomas.
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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".