An Engineered 3D Co-Culture Model of Macrophages and Pancreatic Cancer Organoids to Explore Cellular Interactions in the Tumour Microenvironment
Bibliographic record
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer and is currently the third leading cause of cancer-related deaths in Canada. Despite innovations in research and the advent of immunotherapies, PDAC remains one of the deadliest cancers. PDAC is extremely challenging to treat because of its highly dysregulated tumour microenvironment (TME) comprised of aggressive cancer cells and pro-tumorigenic immune cells that interact with hypoxic and molecular gradients to promote malignancy. Specifically, interactions between tumour associated macrophages (TAMs) and cancer cells in the hypoxic TME have been shown to play significant roles in disease progression. However, deciphering these complex interactions is challenging with current cancer models, which either do not capture the complexities of the TME or allow for the interrogation of cellular interactions in physiologically relevant microenvironmental gradients. In this work, we present a fully human engineered 3D co-culture model of primary macrophages and patient-derived pancreatic cancer organoids based on the Tissue Roll for Analysis of Cellular Environment and Response (TRACER). Here, we incorporate macrophages with PDAC organoids and show that cell-generated hypoxic gradients and expected responses to these gradients are observed in TRACER. Moreover, we report that the TRACER co-culture can be used to evaluate the impact of macrophage-cancer interactions on response to chemotherapies and T-cell inflammation. To decipher these cancer-immune cell relationships with more granularity, we performed bulk transcriptional analysis. Here, we show that transcriptional profiles, such as metabolic rewiring of tumour cells, were uniquely present in TRACER but not in cells cultured in hypoxia chambers. Importantly, we observe that macrophages in TRACER resembled pro-tumourigenic PDAC TAMs. Interestingly, we reveal that organoids in co-culture with macrophages in TRACER were less sensitive to gradient and hypoxia-driven perturbations, and that macrophages played a potentially dominant role in driving these changes. Overall, we anticipate that our TRACER co-culture model will uniquely enable the investigation of intersecting parameters between cancer cells, immune cells and microenvironmental gradients like hypoxia, and will provide valuable insight on how the PDAC TME can be modulated for therapeutic benefit – a feat that is not possible with other in vitro models or patient data alone.
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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 | 0.001 |
| 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".