Tumor Cell Invasion Is Promoted by Interstitial Flow-Induced Matrix Priming by Stromal Fibroblasts
Bibliographic record
Abstract
Interstitial flow emanates from tumors into the microenvironment where it promotes tumor cell invasion. Fibroblasts are key constituents of the tumor stroma that modulate the mechanical environment by matrix remodeling and contraction. Here, we explore how interstitial fluid flow affects fibroblast-tumor cell interactions. Using a 3-dimensional invasion assay and MDA-MB-435S cells cocultured with dermal fibroblasts in a collagen matrix, we showed a synergistic enhancement of tumor cell invasion by fibroblasts in the presence of interstitial flow. Interstitial flow also drove transforming growth factor (TGF)-β1 and collagenase-dependent fibroblast migration, consistent with previously described mechanisms in which flow promotes invasion through autologous chemotaxis and increased motility. Concurrently, migrating fibroblasts enhanced tumor cell invasion by matrix priming via Rho-mediated contraction. We propose a model in which interstitial flow promotes fibroblast migration through increased TGF-β1 activation and collagen degradation, positioning fibroblasts to locally reorganize collagen fibers via Rho-dependent contractility, in turn enhancing tumor cell invasion via mechanotactic cues. This represents a novel mechanism in which interstitial flow causes fibroblast-mediated stromal remodeling that facilitates tumor invasion.
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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".