Jab1 is a target of EGFR signaling in ERα-negative breast cancer
Bibliographic record
Abstract
INTRODUCTION: c-Jun activation domain-binding protein-1 (Jab1) is a multifunctional signaling protein that previously has been shown to be a master regulator of a poor prognostic gene signature in invasive breast cancer and to mediate the action of S100A7. Since epidermal growth factor receptor (EGFR), like S100A7, is often expressed in estrogen receptor-alpha-negative (ERalpha-) breast cancer, we set out to investigate the role of Jab1 in mediating EGFR signaling, another facet of the ERalpha- phenotype. METHODS: MDA-MB-231 and MDA-MB-468 ERalpha-/EGFR+ cell lines were assessed for localization of Jab1 and levels of downstream genes by immunofluorescence and nuclear protein extract assay following treatment with epidermal growth factor (EGF) and extracellular signal-regulated kinase (ERK) pathway inhibitor. A cohort of 424 human breast tumors was also assessed by immunohistochemistry. RESULTS: EGF treatment of cell lines resulted in increased Jab1 nuclear expression. This effect was inhibited by the ERK pathway inhibitor, PD98059. EGF treatment was also associated with colocalization of pERK (phosphorylated ERK) and Jab1 as well as regulation of the Jab1 downstream target gene, p27. When Jab1 activity was knocked down, p27 levels were restored to pre-EGF treatment level. Analysis of EGFR and Jab1 expression in a cohort of invasive breast tumors by tissue microarray and immunohistochemistry confirmed a relationship between EGFR and increased nuclear Jab1 within the ERalpha- subset (n = 154, P = 0.019). The same association was also confirmed for S100A7 and Jab1 (P = 0.036), and high Jab1 nuclear expression was most frequent in tumors that were positive for both EGFR and S100A7 (P = 0.004). CONCLUSION: Jab1 is a target of EGFR signaling in ERalpha- cell lines and breast tumors and therefore may be a common central factor and potential therapeutic target for important cell signaling pathways in ERalpha- 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 | 0.001 |
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".