Matrix stiffness regulates microvesicle-induced fibroblast activation
Bibliographic record
Abstract
Extracellular vesicles released by cancer cells have recently been implicated in the differentiation of stromal cells to their activated, cancer-supporting states. Microvesicles, a subset of extracellular vesicles released from the plasma membrane of cancer cells, contain biologically active cargo, including DNA, mRNA, and miRNA, which are transferred to recipient cells and induce a phenotypic change in behavior. While it is known that microvesicles can alter recipient cell phenotype, little is known about how the physical properties of the tumor microenvironment affect fibroblast response to microvesicles. Here, we utilized cancer cell-derived microvesicles and synthetic substrates designed to mimic the stiffness of the tumor and tumor stroma to investigate the effects of microvesicles on fibroblast phenotype as a function of the mechanical properties of the microenvironment. We show that microvesicles released by highly malignant breast cancer cells cause an increase in fibroblast spreading, α-smooth muscle actin expression, proliferation, cell-generated traction force, and collagen gel compaction. Notably, our data indicate that these phenotypic changes occur only on stiff matrices mimicking the stiffness of the tumor periphery and are dependent on the cell type from which the microvesicles are shed. Overall, these results show that the effects of cancer cell-derived microvesicles on fibroblast activation are regulated by the physical properties of the microenvironment, and these data suggest that microvesicles may have a more robust effect on fibroblasts located at the tumor periphery to influence cancer progression.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".