The size-speed-force relationship governs migratory cell response to tumorigenic factors
Bibliographic record
Abstract
Tumor development progresses through a complex path of biomechanical changes leading first to cell growth and contraction and then cell deadhesion, scattering, and invasion. Tumorigenic factors may act specifically on one of these steps or have a wider spectrum of actions, leading to a variety of effects and thus sometimes to apparent contradictory outcomes. Here we used micropatterned lines of collagen type I/fibronectin on deformable surfaces to standardize cell behavior and measure simultaneously cell size, speed of motion and magnitude of the associated traction forces at the level of a single cell. We analyzed and compared the normal human breast cell line MCF10A in control conditions and in response to various tumorigenic factors. In all conditions, a wide range of biomechanical properties was identified. Despite this heterogeneity, normal and transformed motile cells followed a common trend whereby size and contractile forces were negatively correlated with cell speed. Some tumorigenic factors, such as activation of ErbB2 or loss of the βsubunit of casein kinase 2, shifted the whole population toward a faster speed and lower contractility state. Treatment with transforming growth factor β induced some cells to adopt opposing behaviors such as extremely high versus extremely low contractility. Thus tumor transformation amplified preexisting population heterogeneity and led some cells to exhibit biomechanical properties that were more extreme than those observed with normal cells.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".