Abstract 5008: The acetylenic tricyclic bis(cyanoeneone), TBE-31 inhibits non-small cell lung cancer cell migration through direct binding with actin
Bibliographic record
Abstract
Abstract During metastasis tumor cells undergo epithelial to mesenchymal transition (EMT), where cell-cell junctions dissolve and actin stress fibers are formed. This transition unmasks the migratory and invasive potential of the tumor cells. Here we show that the tricyclic compound TBE-31 binds to purified actin as well as actin from cell lysates. Furthermore, TBE-31 inhibits linear and branched actin polymerization in vitro as well as stress fiber formation in fibroblasts. We also observed that TBE-31 inhibits stress fiber formation in non-small lung cancer cells during TGFβ-dependent EMT. Interestingly, TBE-31 does not interfere with TGFβ-dependent signaling or changes in E- and N-cadherin protein levels during EMT. Finally, we observed that TBE-31 inhibits non-small cell lung tumor cell migration. Our results suggest that TBE-31 targets linear actin polymerization to alter cell morphology and inhibit cell migration. Citation Format: Eddie Chan, Akira Saito, Tadashi Honda, John Di Guglielmo. The acetylenic tricyclic bis(cyanoeneone), TBE-31 inhibits non-small cell lung cancer cell migration through direct binding with actin. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 5008. doi:10.1158/1538-7445.AM2014-5008
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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 | 0.001 |
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".