Data from Epidermal Growth Factor Receptor Signaling Synergizes with Hedgehog/GLI in Oncogenic Transformation via Activation of the MEK/ERK/JUN Pathway
Bibliographic record
Abstract
<div>Abstract<p>Persistent activation of the Hedgehog (HH)/GLI signaling pathway has been implicated in the development of a number of human cancers. The GLI zinc finger transcription factors act at the end of the HH signaling cascade to control gene expression, and recent studies have shown that the activity of GLI proteins can be additionally modified by integration of distinct signals, such as the MEK/extracellular signal-regulated kinase (ERK) and phosphinositide-3 kinase (PI3K)/AKT pathway. However, little is known about the identity of the upstream activators of these HH/GLI interacting signaling pathways in cancer. Here, we provide evidence that integration of the HH/GLI and epidermal growth factor receptor (EGFR) pathway synergistically induces oncogenic transformation, which depends on EGFR-mediated activation of the RAS/RAF/MEK/ERK but not of the PI3K/AKT pathway. EGFR/MEK/ERK signaling induces JUN/activator protein 1 activation, which is essential for oncogenic transformation, in combination with the GLI activator forms GLI1 and GLI2. Furthermore, pharmacologic inhibition of EGFR and HH/GLI efficiently reduces growth of basal cell carcinoma (BCC) cell lines derived from mice with activated HH/GLI signaling. The results identify the synergistic integration of GLI activator function and EGFR signaling as a critical step in oncogenic transformation and provide a molecular basis for therapeutic opportunities relying on combined inhibition of the HH/GLI and EGFR/MEK/ERK/JUN pathway in BCC. [Cancer Res 2009;69(4):1284–92]</p></div>
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".