Cells, tissues, and organs on chips: challenges and opportunities for the cancer tumor microenvironment
Bibliographic record
Abstract
The transition to increasingly sophisticated microfluidic systems has led to the emergence of "organ-on-chip" technology that can faithfully recapitulate organ-level function. Given the rapid progress at the interface between microfluidics and cell biology, there is need to provide a focused evaluation of the state-of-the-art in microfluidic systems for cancer research to advance development, accelerate discovery of novel insights, and facilitate cooperation between engineers, biologists and oncologists in the clinic. Here, we provide a focused review of microfluidics technology from cells- and tissues- to organs-on-chips with application toward studying the tumor microenvironment. Key aspects of the tumor microenvironment including angiogenesis, hypoxia, biochemical gradients, tumor-stromal interactions, and the extracellular matrix are summarized for both solid tumors and non-solid hematologic malignancies. An overview of microfluidic systems designed specifically to answer questions related to different aspects of the tumor microenvironment is provided, followed by an examination of how these systems offer new opportunities to study outstanding challenges related to the major cancer hallmarks. Challenges also remain for microfluidics engineers, but it is hoped that cooperation between engineers and biologists at the intersection of their respective fields will lead to significant impact on the utility of organs-on-chips in cancer research.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".