Abstract B67: Elucidating the mechanism of targeting WNT ligands to exosomes
Bibliographic record
Abstract
Abstract It has been demonstrated that secreted vesicles called exosomes derived from stromal fibroblasts pick up Wnt ligands from breast cancer cells and facilitate cell migration and metastasis in vivo. However, how Wnt ligands are loaded onto exosomes remains unclear. Interestingly, Wnt ligands are posttranslationally modified by acylation and thus tether tightly to the cell membrane. I will present my work on defining the molecular mechanisms through which Wnt is targeted to exosomes. In particular, I am studying the molecular determinants in Wnt ligands, including the palmitoylation and glycosylation sites that regulate Wnt sorting to exosome membranes. Elucidation of Wnt mobilization in the extracellular space has significant implications in the fields of Wnt signaling in both development and cancer. Citation Format: Ying Yi Zhang, Ainsley Underhill, Liang Zhang, Jeffrey Wrana. Elucidating the mechanism of targeting WNT ligands to exosomes. [abstract]. In: Proceedings of the Third AACR International Conference on Frontiers in Basic Cancer Research; Sep 18-22, 2013; National Harbor, MD. Philadelphia (PA): AACR; Cancer Res 2013;73(19 Suppl):Abstract nr B67.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".