Cancer-Associated Fibroblasts in a 3D Engineered Tissue Model Induce Tumor-like Matrix Stiffening and EMT Transition
Bibliographic record
Abstract
A tumor microenvironment is characterized by its altered mechanical properties. However, most models remain unable to faithfully recreate the mechanical properties of a tumor. Engineered models based on the self-assembly method have the potential to better recapitulate the stroma architecture and composition. Here, we used the self-assembly method based on a bladder tissue model to engineer a tumor-like environment. The tissue-engineered tumor models were reconstituted from stroma-derived healthy primary fibroblasts (HFs) induced into cancer-associated fibroblast cells (iCAFs) along with an urothelium overlay. The iCAFs-derived extracellular matrix (ECM) composition was found to be stiffer, with increased ECM deposition and remodeling. The urothelial cells overlaid on the iCAFs-derived ECM were more contractile, as measured by quantitative polarization microscopy, and displayed increased YAP nuclear translocation. We further showed that the proliferation and expression of epithelial-to-mesenchymal transition (EMT) marker in the urothelial cells correlate with the increased stiffness of the iCAFs-derived ECM. Our data showed an increased expression of EMT markers within the urothelium on the iCAFs-derived ECM. Together, our results demonstrate that our tissue-engineered tumor model can achieve stiffness levels comparable to that of a bladder tumor, while triggering a tumor-like response from the urothelium.
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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) | 0.000 | 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) | 0.000 | 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".