Data from c-Jun-NH<sub>2</sub>-kinase-1 Inhibition Leads to Antitumor Activity in Ovarian Cancer
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Bibliographic record
Abstract
<div>Abstract<p><b>Purpose:</b> To show the functional, clinical, and biological significance of c-Jun-NH<sub>2</sub>-kinase (JNK)-1 in ovarian carcinoma.</p><p><b>Experimental Design:</b> Analysis of the impact of JNK on 116 epithelial ovarian cancers was conducted. The role of JNK <i>in vitro</i> and in experimental models of ovarian cancer was assessed. We studied the role of <i>N</i>-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.</p><p><b>Results:</b> 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. <i>In vivo</i>, 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.</p><p><b>Conclusions:</b> 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</p></div>
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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 it