A TRACER culture invasion assay to probe the impact of cancer associated fibroblasts on head and neck squamous cell carcinoma cell invasiveness
Bibliographic record
Abstract
Cancer associated fibroblasts (CAFs) are a major cellular component of the tumour stroma and have been shown to promote tumour cell invasion and disease progression. CAF-cancer cell interactions are bi-directional and occur via both soluble factor dependent and extracellular matrix (ECM) remodelling mechanisms, which are incompletely understood. Previously we developed the Tissue Roll for Analysis of Cellular Environment and Response (TRACER), a novel stacked paper tumour model in which cells embedded in a hydrogel are infiltrated into a porous cellulose scaffold that is then rolled around an aluminum core to generate a multi-layered 3D tissue. Here, we use the TRACER platform to explore the impact of CAFs derived from three different patients on the invasion of two head and neck squamous cell carcinoma (HNSCC) cell lines (CAL33 and FaDu). We find that co-culture with CAFs enhances HNSCC tumour cell invasion into an acellular collagen layer in TRACER and this enhanced migration occurs independently of proliferation. We show that CAF-enhanced invasion of CAL33 cells is driven by a soluble factor independent mechanism, likely involving CAF mediated ECM remodelling via matrix metalloprotenases (MMPs). Furthermore, we find that CAF-enhanced tumour cell invasion is dependent on the spatial pattern of collagen density within the culture. Our results highlight the utility of the co-culture TRACER platform to explore soluble factor independent interactions between CAFs and tumour cells that drive increased tumour cell invasion.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".