Gels for Live Analysis of Compartmentalized Environments (GLAnCE): A Tissue Model to Probe Tumour Phenotypes at Tumour-Stroma Interfaces
Bibliographic record
Abstract
Abstract The interface between a tumour and the adjacent stroma is a site of great importance for tumour development. At this site, carcinoma cells are highly proliferative, undergo invasive phenotypic changes, and directly interact with surrounding stromal cells, such as cancer-associated fibroblasts (CAFs) which further exert pro-tumorigenic effects. Here we describe the development of GLAnCE (Gels for Live Analysis of Compartmentalized Environments), an easy-to-use hydrogel-culture platform for investigating CAF-tumour cell interaction dynamics in vitro at a tumour-stroma interface. GLAnCE enables observation of CAF-mediated enhancement of both tumour cell proliferation and invasion at the tumour-stroma interface in real time, as well as stratification between phenotypes at the interface versus in the bulk tumour tissue compartment. We found that CAF presence resulted in the establishment of an invasion-permissive, interface-specific matrix environment, that leads to carcinoma cell movement outwards from the tumour edge and tumour cell invasion. Furthermore, the spatial stratification capability of GLAnCE was leveraged to discern differences between tumour cell epithelial-to-mesenchymal (EMT) transition genes induced by paracrine signaling from CAFs versus genes induced by interface-specific, CAF-mediated microenvironment. GLAnCE combines high usability and tissue complexity, to provide a powerful in vitro platform to probe mechanisms of tumour cell movement specific to the microenvironment at the tumour-stroma interface.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".