Direct Targeting of the Raf-MEK-ERK Signaling Cascade Inhibits Neuroblastoma Growth
Bibliographic record
Abstract
The Raf-MEK-ERK signaling network has been the subject of intense research due to its role in the development of human cancers, including pediatric neuroblastoma (NB). MEK and ERK are the central components of this signaling pathway and are attractive targets for cancer therapy. Approximately 3–5% of the primary NB samples and about 80% of relapsed samples contain mutations in the Raf-MEK-ERK pathway. In the present study, we analyzed the NB patient datasets and revealed that high RAF and MEK expression leads to poor overall survival and directly correlates with cancer progression and relapse. Further, we repurposed a specific small-molecule MEK inhibitor CI-1040 to inhibit the Raf-MEK-ERK pathway in NB. Our results show that CI-1040 potently inhibits NB cell proliferation and clonogenic growth in a dose-dependent manner. Inhibition of the Raf-MEK-ERK pathway by CI-1040 significantly enhances apoptosis, blocks cell cycle progression at the S phase, inhibits expression of the cell cycle-related genes, and significantly inhibits phosphorylation and activation of the ERK1/2 protein. Furthermore, CI-1040 significantly inhibits tumor growth in different NB 3D spheroidal tumor models in a dose-dependent manner and by directly inhibiting spheroidal tumor cells. Overall, our findings highlight that direct inhibition of the Raf-MEK-ERK pathway is a novel therapeutic approach for NB, and further developing repurposing strategies using CI-1040 is a clinically tractable strategy for effectively treating NB.
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 | 0.001 |
| 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".