Tumor invasion as non-equilibrium phase separation
Bibliographic record
Abstract
ABSTRACT Tumor invasion depends upon properties of both cells and of the extracellular matrix (ECM). Despite ample evidence that cancer cells can modulate their material state during invasion, underlying biophysical mechanisms remain unclear. Here, we show the potential for coexistence of – and transition between – solid-like, fluid-like, and gas-like phases in invading breast cancer spheroids. Epithelial spheroids are nearly jammed and solid-like in the core but unjam at the periphery to invade as a fluid-like collective. Conversely, post-metastatic spheroids are unjammed and fluid-like in the core and – depending on ECM density – can further unjam and invade as gas-like single cells, or re-jam to invade as a fluid-like collective. A novel jamming phase diagram predicts material phases that are superficially similar to inanimate systems at thermodynamic equilibrium, but here arising in living systems, which exist far from equilibrium. We suggest that non-equilibrium phase separation may provide a unifying physical picture of tumor invasion. TWO-SENTENCE SUMMARY Using tumor spheroids invading into an engineered three-dimensional matrix, we show here that the cellular collective exhibits coexistent solid-like, fluid-like, and gas-like phases. The spheroid interior develops spatial and temporal heterogeneities in material phase which, depending upon cell type and matrix density, ultimately result in a variety of phase separation patterns at the invasive front, as captured by a jamming phase diagram.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".