Abstract 1548: The effects of synthetic triterpenoids on GSK3β and focal adhesions
Bibliographic record
Abstract
Abstract Synthetic triterpenoids are a class of anti-cancer compounds that are efficacious in targeting multiple cellular functions including apoptosis, growth inhibition, anti-inflammation and cytoprotection in both cell culture and animal tumor models. However, its effects on cell migration, a precursor event to cancer metastasis, remain poorly understood. Previously, we have shown that the methyl ester derivative of 2-cyano-3,12-dioxooleana-1,9-dien-28-oic acid (CDDO-Me) inhibits cell migration. The objective of our current study is to examine in further detail the mechanism by which CDDO-Me blocks cell migration. Using various proteomic approaches as well as affinity pull down assays, Glycogen Synthase Kinase 3 Beta (GSK3β) was identified and confirmed as a triterpenoid-binding protein. Since GSK3β has been shown to be an important regulator of cell adhesion, a process that is intricately connected to cell migration, we hypothesize that CDDO-Me may target GSK3β and affect cell adhesion dynamics to inhibit cell migration. Indeed, we have observed that triterpenoid treated cells have decreased GSK3β activity. We also observed that the size and shape of focal adhesions were altered in the presence of synthetic triterpenoids. The molecular link between synthetic triterpenoids, GSK3β and focal adhesions are currently being investigated to better describe how this promising anti-cancer compound may act via GSK3β to affect focal adhesions and cell migration. 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 1548. doi:10.1158/1538-7445.AM2011-1548
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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) | 0.004 | 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".