It’s all downstream from here: RTK/Raf/MEK/ERK pathway resistance mechanisms in glioblastoma
Bibliographic record
Abstract
BACKGROUND: The receptor tyrosine kinase (RTK)/Ras/Raf/MEK/ERK signaling pathway is one of the most tumorigenic pathways in cancer, with its hyperactivation strongly linked to the aggressive nature of glioblastoma (GBM). Although extensive research has focused on developing therapeutics targeting this pathway, clinical success remains elusive due to the emergence of resistance mechanisms. OBJECTIVE: This review investigates how inhibition of the RTK/Ras/Raf/MEK/ERK pathway alters transcription factors, contributing to acquired resistance mechanisms in GBM. It also highlights the critical role of transcription factor dysregulation in therapeutic resistance. METHODS & RESULTS: Findings from key studies on the RTK/Ras/Raf/MEK/ERK pathway in GBM were synthesized to explore the role of transcription factor dysregulation in resistance to targeted therapies, radiation, and chemotherapy. The review highlights that transcription factors undergo significant dysregulation following RTK/Ras/Raf/MEK/ERK pathway inhibition, contributing to therapeutic resistance. CONCLUSION: Transcription factors are promising targets for overcoming treatment resistance in GBM, with cotreatment strategies combining RTK/Ras/Raf/MEK/ERK pathway inhibitors and transcription factor-targeted therapies presenting a novel approach. Despite the challenges of targeting complex structures and interactions, advancements in drug development and precision technologies hold great potential. Continued research is essential to refine these strategies and improve outcomes for GBM and other aggressive cancers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".