Abstract 4593: Analysis of transforming growth factor β receptor trafficking on different signaling transduction pathways
Bibliographic record
Abstract
Abstract Transforming growth factor beta (TGFβ) is a cytokine that regulates cellular adhesion, proliferation and apoptosis. Its canonical downstream effectors include receptor-regulated Smad2/3 proteins, which are phosphorylated and then translocated to the nucleus to alter transcription. Additionally, other atypical pathways are simultaneously initiated by TGFβ, including that of p38 (a mitogen activated protein kinase). TGFβ acts as a tumour suppressor in its normal epithelial environment, but in tumor cells, it promotes epithelial-mesenchymal transition and cell migration. The manner in which tumor cells overcome the growth suppressive effects of TGFβ is not well-understood, especially since downstream TGFβ signaling is still apparent in these cells. This may point to selective pathway activation as a reason for its dual roles. Thus, it is important to consider membrane trafficking—a central process that directs signaling between various cascades. Previous studies from our lab demonstrate that perturbation of aPKC signaling alters TGFβ receptor trafficking and Smad signal transduction. Furthermore, we have also observed increased access to the p38 MAPK pathway in cells that have been silenced for aPKC expression. We are currently assessing the trafficking of the TGFβ receptor complex in order to investigate the mechanism(s) responsible differential access to specific signaling pathways. This will be addressed using techniques such as co-immunoprecipitation, immunofluorescence microscopy, subcellular fractionation and western blotting. Determining these mechanisms will further our understanding on cancerous TGFβ pathway regulation, and may lead to the discovery of promising therapeutic targets. Citation Format: Evelyn Ng, John Di Guglielmo. Analysis of transforming growth factor β receptor trafficking on different signaling transduction pathways. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4593.
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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 | 0.001 | 0.001 |
| 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) | 0.006 | 0.002 |
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".