The positive correlation between drug addiction and drug dosage in vemurafenib-resistant melanoma cells is underpinned by activation of ERK1/2-FRA-1 pathway
Bibliographic record
Abstract
Malignant melanoma is a kind of highly invasive and deadly diseases. The BRAF inhibitor (BRAFi) such as vemurafenib could achieve a high response rate in melanoma patients with BRAF mutation. However, melanoma cells could easily develop resistance as well as addiction to BRAFi. Based on the drug addiction, intermittent treatment has been proposed to select against BRAFi-resistant melanoma cells. Because different dosages of BRAFi might be used in patients, it is necessary to know about the relationship between drug dosage and the degree of addiction. To address the problem, four drug-resistant melanoma cell sublines (A375/R0.5, A375/R2.0, M14/R0.5 and M14/R2.0) were established by continuously exposure of melanoma A375 or M14 cells to 0.5 or 2.0 μM vemurafenib. Vemurafenib withdrawal resulted in much stronger suppression on clone formation in A375/R2.0 and M14/R2.0, compared with A375/R0.5 and M14/R0.5, respectively. Meanwhile, stronger upregulation of ERK1/2-FRA-1 pathway could be observed in A375/R2.0 and M14/R2.0. Further detection showed that some proinflammatory cytokines downstream of ERK1/2-FRA-1 pathway were upregulated after drug withdrawal, and the conditioned medium collected from the resistant A375 cells could inhibit clone formation. Furthermore, vemurafenib withdrawal resulted in suppressed cell proliferation rather than cell senescence, with stronger effect on A375/R2.0 compared with A375/R0.5. This study suggested that the depth of vemurafenib addiction in resistant melanoma cells is positively correlated to the drug dosage, which might be underpinned by the ERK1/2-FRA-1 pathway and the related cytokines.
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) | 0.002 | 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".