An Engineered 3D Co-culture Model of Primary Macrophages and Patient-Derived Tumour Cells to Explore Cellular Responses in the Graded Hypoxic Microenvironment of Pancreatic Cancer
Bibliographic record
Abstract
Abstract In pancreatic ductal adenocarcinoma (PDAC), tumour associated macrophages (TAMs) are a heterogeneous immune cell population that interact with cancer cells to promote malignancy, chemo-resistance, and immunosuppression. Aside from TAMs, hypoxia is a prominent feature of PDAC that can rewire cells to survive and enhance malignancy in the tumour microenvironment (TME). Deciphering the interactions between macrophages, cancer cells and hypoxia could lead to the development of effective immune-targeted therapies for PDAC. However, there are only a few models that physiologically recapitulate the PDAC TME and allow for meaningful interrogation of cancer-immune cell interactions in hypoxia. Here, we develop a model of primary macrophages and PDAC patient organoid-derived cells by adapting TRACER, a paper-based, engineered 3D model that allows snapshot analysis of cellular response in hypoxia. In this study, we establish a direct co-culture method of primary macrophages and PDAC organoid cells in TRACER and demonstrate that TRACER co-cultures generate hypoxic gradients and show expected phenotypic responses to this hypoxic gradient. Moreover, we report for the first time in a human in vitro model that hypoxic macrophages exert a graded chemoprotective effect on gemcitabine-treated PDAC organoid cells, and that interactions between cancer cells and macrophages from the inner layers of TRACER indirectly attenuate the inflammatory response of donor-derived T-cells. Overall, the TRACER co-culture system is a novel, fully human 3D in vitro cancer-immune model for evaluating the response of macrophages and cancer cells in a hypoxic gradient.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".