An Engineered Paper‐Based 3D Coculture Model of Pancreatic Cancer to Study the Impact of Tissue Architecture and Microenvironmental Gradients on Cell Phenotype
Bibliographic record
Abstract
The spatial configuration of cells in the tumor microenvironment (TME) affects both cancer and fibroblast cell phenotypes contributing to the clinical challenge of tumor heterogeneity and therapeutic resistance. This is a particular challenge in stroma-rich pancreatic ductal adenocarcinoma (PDAC). Here, a versatile system is described to study the impact of tissue architecture on cell phenotype using PDAC as a model system. This fully human system encompassing both primary pancreatic stellate cells and primary organoid cells using the TRACER platform to allow the creation of user-defined TME architectures that have been inferred from clinical PDAC samples and are analyzed by CyTOF to characterize cells extracted from the system. High dimensional characterization using CyTOF demonstrates that tissue architecture leads to distinct hypoxia and proliferation gradients. Furthermore, phenotypic markers for both cell types are also graded in ways that cannot be explained by either hypoxia or coculture alone. This demonstrates the importance of using complex models encompassing cancer cells, stromal cells, and allowing control over architecture to explore the impact of tissue architecture on cell phenotype. It is anticipated that this model will help decipher how tissue architecture and cell interactions regulate cell phenotype and hence cellular and tissue heterogeneity.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".