Microfluidic technologies for immunotherapy studies on solid tumours
Bibliographic record
Abstract
Immunotherapy is a powerful and targeted cancer treatment that exploits the body's immune system to attack and eliminate cancerous cells. This form of therapy presents the possibility of long-term control and prevention of recurrence due to the memory capabilities of the immune system. Various immunotherapies are successful in treating haematological malignancies and have dramatically improved outcomes in melanoma. However, tackling other solid tumours is more challenging, mostly because of the immunosuppressive tumour microenvironment (TME). Current in vitro models based on traditional 2D cell monolayers and animal models, such as patient-derived xenografts, have limitations in their ability to mimic the complexity of the human TME. As a result, they have inadequate translational value and can be poorly predictive of clinical outcome. Thus, there is a need for robust in vitro preclinical tools that more faithfully recapitulate human solid tumours to test novel immunotherapies. Microfluidics and lab-on-a-chip technologies offer opportunities, especially when performing mechanistic studies, to understand the role of the TME in immunotherapy, and to expand the experimental throughput when using patient-derived tissue through its miniaturization capabilities. This review first introduces the basic concepts of immunotherapy, presents the current preclinical approaches used in immuno-oncology for solid tumours and then discusses the underlying challenges. We provide a rationale for using microfluidic-based approaches, highlighting the most recent microfluidic technologies and methodologies that have been used for studying cancer-immune cell interactions and testing the efficacy of immunotherapies in solid tumours. Ultimately, we discuss achievements and limitations of the technology, commenting on potential directions for incorporating microfluidic technologies in future immunotherapy studies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".