Abstract A008: Evaluation of natural and synthetic compounds on epithelial to mesenchymal transition in triple negative breast cancer
Bibliographic record
Abstract
Abstract Epithelial to mesenchymal transition (EMT) is a biological process through which epithelial cells undergo cytoskeletal changes and transform into a mesenchymal phenotype. This phenotypical transformation leads to enhanced migratory capabilities and increased resistance to apoptosis and drug therapies. The EMT characterized in part by an increase in vimentin, a mesenchymal marker, and a decrease in E-cadherin, an epithelial marker, plays a significant role in cancer metastasis and progression. The MEK5/ERK5 pathway is essential in regulating cell survival, proliferation, and migration. Inhibition of the MEK5/ERK5 pathway by small molecule inhibitors decreases cellular proliferation and impedes migration of cancer cells, including triple negative breast cancer (TNBCs) cells. A type III allosteric inhibitor of MEK5 designed by our collaborators reverses the EMT and induces a mesenchymal to epithelial transition (MET). Using a TNBC cell line, MDA-MB-231, genetically engineered with RFP tagged vimentin, we established a cellular assay to evaluate analogues of our lead type III inhibitor to further investigate the role of the MEK5-ERK pathway in the EMT, design optimal compounds that induce an MET, and explore structure-activity relationships for kinase inhibition. This research will help identify targets for EMT and optimize compound modification to improve potency and efficiency. Citation Format: Asef Faruk, Saloni Patel, Ramez Hallak, Patrick Flaherty, Jane Cavanaugh. Evaluation of natural and synthetic compounds on epithelial to mesenchymal transition in triple negative breast cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Optimizing Therapeutic Efficacy and Tolerability through Cancer Chemistry; 2024 Dec 9-11; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(12_Suppl):Abstract nr A008.
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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) | 0.005 | 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".