HER2-driven breast cancer suppression by the JNK signaling pathway
Bibliographic record
Abstract
The HER2 + subtype of human breast cancer is associated with the malignant transformation of luminal ductal cells of the mammary epithelium. The sequence analysis of tumor DNA identifies loss of function mutations and deletions of the MAP2 K4 and MAP2 K7 genes that encode direct activators of the JUN NH 2 -terminal kinase (JNK). We report that in vitro studies of human mammary epithelial cells with CRISPR-induced mutations in the MAPK and MAP2K components of the JNK pathway caused no change in growth in 2D culture, but these mutations promoted epithelial cell proliferation in 3D culture. Analysis of gene expression signatures in 3D culture demonstrated similar changes caused by HER2 activation and JNK pathway loss. The mechanism of signal transduction cross-talk may be mediated, in part, by JNK-suppressed expression of integrin α6β4 that binds HER2 and amplifies HER2 signaling. These data suggest that HER2 activation and JNK pathway loss may synergize to promote breast cancer. To test this hypothesis, we performed in vivo studies using a mouse model of HER2 + breast cancer with Cre/loxP -mediated ablation of genes encoding JNK ( Mapk8 and Mapk9 ) and the MAP2K ( Map2 k4 and Map2 k7 ) that activate JNK in mammary epithelial cells. Kaplan–Meier analysis of tumor development demonstrated that JNK pathway deficiency promotes HER2 + -driven breast cancer. Collectively, these data identify JNK pathway genes as potential suppressors for HER2 + breast cancer.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".