Modeling the Role of Cancer-Associated Fibroblasts in Tumor Cell Invasion
Bibliographic record
Abstract
The major cause of cancer-related deaths can be attributed to the metastatic spread of tumor cells-a dynamic and complex multi-step process beginning with tumor cells acquiring an invasive phenotype to allow them to travel through the blood and lymphatic vessels to ultimately seed at a secondary site. Over the years, various in vitro models have been used to characterize specific steps in the cascade to collectively begin providing a clearer picture of the puzzle of metastasis. With the discovery of the TME's supporting role in activating tumor cell invasion and metastasis, these models have evolved in parallel to accommodate features of the TME and to observe its interactions with tumor cells. In particular, CAFs that reside in reactive tumor stroma have been shown to play a substantial pro-invasive role through their matrix-modifying functions; accordingly, this warranted further investigation with the development and use of invasion assays that could include these stromal cells. This review explores the growing toolbox of assays used to study tumor cell invasion, from the simple beginnings of a tumor cell and extracellular matrix set-up to the advent of models that aim to more closely recapitulate the interplay between tumor cells, CAFs and the extracellular matrix. These models will prove to be invaluable tools to help tease out the intricacies of tumor cell invasion.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".