The adaptor protein VEPH1 interacts with the kinase domain of ERBB2 and impacts EGF signaling in ovarian cancer cells
Bibliographic record
Abstract
Upregulation of ERBB2 and activating mutations in downstream KRAS/BRAF and PIK3CA are found in several ovarian cancer histotypes. ERBB2 enhances signaling by the ERBB family of EGF receptors, and contains docking positions for proteins that transduce signaling through multiple pathways. We identified the adaptor protein ventricular zone-expressed pleckstrin homology domain-containing protein 1 (VEPH1) as a potential interacting partner of ERBB2 in a screen of proteins co-immunoprecipitated with VEPH1. In this study, we confirm a VEPH1 - ERBB2 interaction by co-immunoprecipitation and biotin proximity labelling and show that VEPH1 interacts with the juxtamembrane-kinase domain of ERBB2. In SKOV3 ovarian cancer cells, which bear a PIK3CA mutation and ERBB2 overexpression, ectopic VEPH1 expression enhanced EGF activation of ERK1/2, and mTORC2 activation of AKT. In contrast, in ES2 ovarian cancer cells, which bear a BRAFV600E mutation with VEPH1 amplification but low ERBB2 expression, loss of VEPH1 expression enabled further activation of ERK1/2 by EGF and enhanced EGF activation of AKT. VEPH1 expression in SKOV3 cells enhanced EGF-induced cell migration consistent with increased Snail2 and decreased E-cadherin levels. In comparison, loss of VEPH1 expression in ES2 cells led to decreased cell motility independent of EGF treatment despite higher levels of N-cadherin and Snail2. Importantly, we found that loss of VEPH1 expression rendered ES2 cells less sensitive to BRAF and MEK inhibition. This study extends the range of adaptor function of VEPH1 to ERBB2, and indicates VEPH1 has differential effects on EGF signaling in ovarian cancer cells that may be influenced by driver gene mutations.
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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) | 0.001 | 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".