Designer Biomaterials to Model Cancer Cell Invasion In Vitro: Predictive Tools or Just Pretty Pictures?
Bibliographic record
Abstract
Abstract Metastasis is the leading cause of mortality in cancer patients. Underlying this process is the invasion and colonization of cancer cells into healthy tissues. Engineered hydrogel models of tumor microenvironments present an opportunity to understand the microenvironmental determinants of cellular invasion. The biochemical and mechanical cues, presented in the form of adhesion sites, degradable cues, matrix stiffness, and architecture, have significant effects on the extent of cancer cell migration, and the mechanisms employed by these cells to move through their matrix. Coculture with stromal cells such as cancer associated fibroblasts, endothelial cells, and immune cells that are associated with poor prognosis demonstrate that these cells exacerbate cancer cell invasion. With these models, researchers aim not only to recapitulate known cancer cell behaviors in a dish, but also to uncover new insights into mechanisms underlying these phenomena, paving the way for novel treatment strategies. In this perspective, the design of engineered models that are used to study cancer cell invasion and metastasis in vitro is discussed. To this end, the authors seek to understand and put into perspective: do these models reveal relevant mechanisms of cancer cell migration, or are they simply pretty pictures with little biological translatability?
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".