Living human lung slices for ex vivo modelling of lung cancer
Bibliographic record
Abstract
The tumor microenvironment (TME) markedly affects cancer progression, yet traditional animal models do not fully recapitulate the situation in humans. To address this, we developed tumor-derived precision lung slices (TD-PCLS), an ex vivo platform for studying the lung TME and evaluating therapies. TD-PCLS, viable for 8-10 days, preserve the heterogeneity and metabolic activity of primary tumors, as confirmed by seahorse analysis. Using multispectral FACS and phenocycler multiplex imaging, we spatially profiled TME components and cancer cell functionality. Additionally, TD-PCLS revealed patient-specific responses to chemo- and immunotherapies. To complement TD-PCLS, we established tumor-cell-seeded PCLS (TCS-PCLS) by introducing tumor and immune cells into healthy lung slices. This model highlighted macrophage-tumor interactions as critical for tumor cell proliferation, migration, and immune modulation. Together, these platforms provide a robust tool for lung cancer research, enabling precision medicine and advancing therapeutic discovery.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".