Extracellular Signal-Regulated Kinases Modulate DNA Damage Response - A Contributing Factor to Using MEK Inhibitors in Cancer Therapy
Bibliographic record
Abstract
The Raf-MEK-ERK pathway is commonly activated in human cancers, largely attributable to the extracellular signal-regulated kinases (ERKs) being a common downstream target of growth factor receptors, Ras, and Raf. Elevation of these up-stream signals occurs frequently in a variety of malignancies and ERK kinases play critical roles in promoting cell proliferation. Therefore, inhibition of MEK-mediated ERK activation is very appealing in cancer therapy. Consequently, numerous MEK inhibitors have been developed over the years. However, clinical trials have yet to produce overwhelming support for using MEK inhibitors in cancer therapy. Although complex reasons may have contributed to this outcome, an alternative possibility is that the MEK-ERK pathway may not solely provide proliferation signals to malignancies, the central scientific rationale in developing MEK inhibitors for cancer therapy. Recent developments may support this alternative possibility. Accumulating evidence now demonstrated that the MEK-ERK pathway contributes to the proper execution of cellular DNA damage response (DDR), a major pathway of tumor suppression. During DDR, the MEK-ERK pathway is commonly activated, which facilitates the proper activation of DDR checkpoints to prevent cell division. Inhibition of MEK-mediated ERK activation, therefore, compromises checkpoint activation. As a result, cells may continue to proliferate in the presence of DNA lesions, leading to the accumulation of mutations and thereby promoting tumorigenesis. Alternatively, reduction in checkpoint activation may prevent efficient repair of DNA damages, which may cause apoptosis or cell catastrophe, thereby enhancing chemotherapy's efficacy. This review summarizes our current understanding of the participation of the ERK kinases in DDR.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".