Data from c-Jun-NH<sub>2</sub>-kinase-1 Inhibition Leads to Antitumor Activity in Ovarian Cancer
Bibliographic record
Abstract
Abstract Purpose: To show the functional, clinical, and biological significance of c-Jun-NH2-kinase (JNK)-1 in ovarian carcinoma. Experimental Design: Analysis of the impact of JNK on 116 epithelial ovarian cancers was conducted. The role of JNK in vitro and in experimental models of ovarian cancer was assessed. We studied the role of N-5-[4-(4-methyl piperazine methyl)-benzoylamido]-2-methylphenyl-4-[3-(4-methyl)-pyridyl]-2-pyrimidine amine (WBZ_4), a novel JNK inhibitor redesigned from imatinib based on targeting wrapping defects, in cell lines and in experimental models of ovarian cancer. Results: We found a significant association of pJNK with progression-free survival in the 116 epithelial ovarian cancers obtained at primary debulking therapy. WBZ_4 led to cell growth inhibition and increased apoptosis in a dose-dependent fashion in four ovarian cancer cell lines. In vivo, whereas imatinib had no effect on tumor growth, WBZ_4 inhibited tumor growth in orthotopic murine models of ovarian cancer. The antitumor effect was further increased in combination with docetaxel. Silencing of JNK-1 with systemically administered siRNA led to significantly reduced tumor weights compared with nonsilencing siRNA controls, indicating that indeed the antitumor effects observed were due to JNK-1 inhibition. Conclusions: These studies identify JNK-1 as an attractive therapeutic target in ovarian carcinoma and that the redesigned WBZ_4 compound should be considered for further clinical development. Clin Cancer Res; 16(1); 184–94
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".