Cancer-on-chip models for metastasis: importance of the tumor microenvironment
Bibliographic record
Abstract
Cancer-on-chip (CoC) models, based on microfluidic chips harboring chambers for 3D tumor-cell culture, enable us to create a controlled tumor microenvironment (TME). CoC models are therefore increasingly used to systematically study effects of the TME on the various steps in cancer metastasis. Moreover, CoC models have great potential for developing novel cancer therapies and for predicting patient-specific response to cancer treatments. We review recent developments in CoC models, focusing on three main TME components: (i) the anisotropic extracellular matrix (ECM) architectures, (ii) the vasculature, and (iii) the immune system. We aim to provide guidance to biologists to choose the best CoC approach for addressing questions about the role of the TME in metastasis, and to inspire engineers to develop novel CoC technologies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".