Abstract 4042: Activation of ErbB2 results in decreased Par6-associated aPKC kinase activity
Bibliographic record
Abstract
Abstract Loss of epithelial cell polarity is often associated with the development of carcinoma. Polarized MDCK cells and MCF-10A mammary epithelial cells grown in 3D are useful model systems to study the regulation of cell polarity in vitro and to investigate the interaction of oncogenes and cell polarity proteins. Using these experimental models, we have previously demonstrated that activation of the oncogene ErbB2 disrupts apical-basal polarity in mammary epithelial cells. Activated ErbB2 associates with the Par6-aPKC polarity complex and the interaction between Par6 and aPKC is indispensible for ErbB2-mediated disruption of cell polarity and inhibition of apoptosis. Following ErbB2 activation, we find a decrease in Par6-associated aPKC kinase activity. Furthermore, kinase dead aPKC cooperates with activated ErbB2 to transform mammary epithelial cells in vitro, without inducing any changes in proliferation of cells grown on plastic dishes. Taken together, our data implicate a target (or targets) of aPKC as a likely determinant of ErbB2-mediated disruption of cell polarity and transformation. Identification of these substrates could lead to the development of novel biomarkers to monitor the progression of ErbB2 positive tumors. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4042. doi:10.1158/1538-7445.AM2011-4042
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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".