Engineering Biomaterials to Model Immune‐Tumor Interactions In Vitro
Bibliographic record
Abstract
Engineered biomaterial scaffolds are becoming more prominent in research laboratories to study drug efficacy for oncological applications in vitro, but do they have a place in pharmaceutical drug screening pipelines? The low efficacy of cancer drugs in phase II/III clinical trials suggests that there are critical mechanisms not properly accounted for in the pre-clinical evaluation of drug candidates. Immune cells associated with the tumor may account for some of these failures given recent successes with cancer immunotherapies; however, there are few representative platforms to study immune cells in the context of cancer as traditional 2D culture is typically monocultures and humanized animal models have a weakened immune composition. Biomaterials that replicate tumor microenvironmental cues may provide a more relevant model with greater in vitro complexity. In this review, the authors explore the pertinent microenvironmental cues that drive tumor progression in the context of the immune system, discuss how these cues can be incorporated into hydrogel design to culture immune cells, and describe progress toward precision oncological drug screening with engineered tissues.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".