Enhancing effect of platelet‐derived microvesicles on the invasive potential of breast cancer cells
Bibliographic record
Abstract
BACKGROUND: Platelets (PLTs) have been postulated to play a role in cancer progression and metastasis. Recently, it was demonstrated that PLT-derived microvesicles (PMVs) transfer various surface receptors and/or adhesion molecules to target cells and modulate their biological responses. In this work, it was hypothesized that PMVs interact with breast cancer cells, increasing their invasiveness. STUDY DESIGN AND METHODS: PMVs (isolated from outdated PLT concentrates) were incubated with three human breast cancer cell lines (MDA-MB-231, BT-549, and T47D), and their effects on in vitro invasiveness of these cells (adhesion, expression of matrix metalloproteinases [MMPs], and chemoinvasion), as well as their interactions with stroma, were evaluated. RESULTS: We found that PMVs 1) transferred PLT-derived integrin CD41 to the surface of breast cancer cells and enhanced their adhesion to endothelial cells; 2) increased CXCR4 expression and chemotaxis toward a stromal-derived factor-1 gradient in invasive MDA-231 and BT-549 cells; 3) increased phosphorylation of the mitogen-activated protein kinase p42/44 and AKT signaling pathways; 4) stimulated the production of MMPs in invasive MDA-231 and BT-549 cells and their chemoinvasion across the reconstituted basement membrane Matrigel; and 5) induced the secretion of MMP-9 by marrow fibroblasts and stimulated the secretion of both MMP-2 and MMP-9 in cocultures of fibroblasts with MDA-MB-231 cells. CONCLUSION: It was shown than PMVs enhance the in vitro invasive potential of invasive breast cancer cell lines and therefore could mediate the progression of breast cancer. These findings warrant further evaluation of the implications of PLT transfusions in cancer patients.
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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) | 0.001 | 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".