Abstract B62: Proteomic dynamics of stromal exosomes during processing in cancer cells
Bibliographic record
Abstract
Abstract Within the tumor microenvironment, cancer-associated fibroblasts (CAFs) are capable of releasing exosomes, small extracellular vesicles, which are then used by breast cancer cells to aid in metastasis. Exosomes are uptaken by the cancer cells and gain Wnt11 through processing, thus allowing them to stimulate Wnt-signaling pathways that causes increased cell motility. Our current research has focused on understanding the events that exosomes undergo as they pass through breast cancer cells. Exosomes from L cells carry the tetraspanin, Cd81, and two of its known transmembrane interactors Igsf8 and Ptgfrn. By using immunoprecipitation, we were able to isolate Cd81-exosomes, and analyze them by mass spectrometry. Furthermore, comparison of Cd81-exosomes before and after processing in MDA-MB-231, a human breast cancer cell line, revealed differences in protein content of the exosomes. Additionally, since L cells are a mouse fibroblast cell line, we could identify the origin of each protein based on variance between human and mouse proteins. Several families of proteins were identified, such as RNA-binding proteins, kinases, phosphatases, and secretory and endocytic proteins. Our data allows us to postulate that Cd81-exosome processing involves the use of the cancer cell endocytic and secretory pathways. By determining the mechanism whereby breast cancer cells use Cd81-exosomes as signals, it may be possible to block this process and prevent or slow metastatic events. Citation Format: Ainsley Q. Underhill, Yingyi Amy Zhang, Liang Zhang, Valbona Luga, Jeff Wrana. Proteomic dynamics of stromal exosomes during processing in cancer cells. [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 B62.
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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) | 0.002 | 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".