Invasive cancer cells soften collagen networks and disrupt stress-stiffening via volume exclusion, contractility and adhesion
Bibliographic record
Abstract
Collagen networks form the structural backbone of the extracellular matrix in both healthy and cancerous tissues, exhibiting nonlinear mechanical properties that crucially regulate tissue mechanics and cell behavior. Here, we investigate how the presence of invasive breast cancer cells (MDA-MB-231) influences the polymerization kinetics and mechanics of collagen networks using bulk shear rheology and rheo-confocal microscopy. We show that embedded cancer cells delay the onset of collagen polymerization due to volume exclusion effects. During polymerization, the cells (at 4% volume fraction) cause an unexpected time-dependent softening of the network. We show that this softening effect arises from active remodeling via adhesion and contractility rather than from proteolytic degradation. At higher cell volume fractions, the dominant effect of the cells shifts to volume exclusion, causing a two-fold reduction of network stiffness. Additionally, we demonstrate that cancer cells suppress the characteristic stress-stiffening response of collagen. This effect (partially) disappears when cell adhesion and contractility are inhibited, and it is absent when the cells are replaced by passive hydrogel particles. These findings provide new insights into how active inclusions modify the mechanics of fibrous networks, contributing to a better understanding of the role of cells in the mechanics of healthy and diseased tissues like invasive tumors. STATEMENT OF SIGNIFICANCE: Understanding how cells influence tissue mechanics is crucial to unravel disease progression. While fibroblasts are known to stiffen tissues, the role of invasive cancer cells is less clear. Using collagen-based tissue models, we reveal that cancer cells unexpectedly soften the collagen matrix and disrupt its stress-stiffening response. By comparing active cells to passive particles and selectively blocking cell functions, we show that volume exclusion, adhesion, and contractility each play distinct roles in shaping tissue mechanics. This work sheds light on the physical impact of cancer cells on their environment, advancing our understanding on how cells dynamically alter the mechanical properties of tissues.
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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".