Recapitulating Pancreatic Tumor Microenvironment through Synergistic Use of Patient Organoids and Organ‐on‐a‐Chip Vasculature
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Tumor progression relies heavily on the interaction between the neoplastic epithelial cells and their surrounding stromal partners. This cell cross-talk affects stromal development, and ultimately the heterogeneity impacts drug efflux and efficacy. To mimic this evolving paradigm, we have micro-engineered a three-dimensional (3D) vascularized pancreatic adenocarcinoma tissue in a tri-culture system composed of patient derived pancreatic organoids, primary human fibroblasts and endothelial cells on a perfusable InVADE platform situated in a 96-well plate. Uniquely, through synergistic engineering we combined the benefits of cellular fidelity of patient tumor derived organoids with the addressability of a plastic organ-on-a-chip platform. Validation of this platform included demonstrating the growth of pancreatic tumor organoids by monitoring the change in metabolic activity of the tissue. Investigation of tumor microenvironmental behavior highlighted the role of fibroblasts in symbiosis with patient organoid cells, resulting in a six-fold increase of collagen deposition and a corresponding increase in tissue stiffness in comparison to fibroblast free controls. The value of a perfusable vascular network was evident in drug screening, as perfusion of gemcitabine into a stiffened matrix did not show the dose-dependent effects on tumor viability as those under static conditions. These findings demonstrate the importance of studying the dynamic synergistic relationship between patient cells with stromal fibroblasts, in a 3D perfused vascular network, to accurately understand and recapitulate the tumor microenvironment.
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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 | 0.001 |
| 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) | 0.001 | 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 it