Immune‐Infiltrated Cancer Spheroid Model with Vascular Recirculation Reveals Temporally Dependent and Tissue‐Specific Macrophage Recruitment
Bibliographic record
Abstract
Immune cell infiltration in tumors has been reported to influence tumor progression and clinical outcomes. Considerable efforts have been made to understand interactions between tumors and the immune system. However, current models are either not comprehensive or limited to short-term studies. Recognizing thedynamic and long-term nature of tumor-immune interactions, an immune-infiltrated cancer spheroid model is developed by continuously perfusing and recirculating immune cells with gravity-driven flow through a tubular blood vessel adjacent to a cancer spheroid. Fibroblasts and pericytes are embedded in the gel matrix to support endothelial cells and enhance the vascular barrier. With continuous monocyte recirculation, monocyte adhesion, transendothelium migration, differentiation, and macrophage recruitment into breast carcinoma and hepatoma spheroids is successfully demonstrated over a week. The macrophage recruitment process is temporally dependent and tissue-specific, leading to the formation of cancer-macrophage heterospheroids. Elevated secretion of granulocyte-macrophage colony-stimulating factor (GM-CSF), which regulates monocyte recruitment and macrophage activation, is observed in the breast carcinoma model. Increased levels of Interleukin 6 (IL-6) and Interleukin 8 (IL-8) are detected, indicating a pro-inflammatory environment associated with tumor progression and metastasis. This platform provides a valuable framework for investigating immune cell infiltration and differentiation within the tumor microenvironment, supporting the advancement of cancer immunotherapies.
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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) | 0.001 | 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".