Bioprinted cancer-stromal in-vitro models in a decellularized ECM-based bioink exhibit progressive remodeling and maturation
Bibliographic record
Abstract
Abstract Constant matrix remodeling and cellular heterogeneity in cancer are key contributors to its development and can profoundly alter treatment efficacy. Developing in-vitro models containing relevant features that can recapitulate these aspects of the tumor microenvironment and that are well characterized can circumvent the limitations of conventional 2D cultures and animal models. Automated fabrication methods combined with biomimetic biomaterials have provided the opportunity to create platforms that can potentially incorporate a heterogeneous population of cells in a 3D environment that allows cell–cell and cell-ECM interactions with reproducibility. This study used 3D extrusion bioprinting and a composite bioink containing a reinforced decellularized extracellular matrix (ECM) hydrogel to fabricate a head and neck cancer in-vitro model. The constituents of this model included fibroblasts and active ECM proteins to represent the stroma, along with HNSCC cells to represent the tumor component. The topographical characterization of the bioink showed a fibrous network with nanometer-sized pores. After cell encapsulation and model fabrication, we observed spheroid development and growth over time with cancer cells in the core and fibroblasts in the periphery. Our model is compatible with matrix metalloproteinase (MMP) quantification techniques and showed significant differences in the presence of MMP-9 and MMP-10 compared to the control groups. This characterized model is proposed as a tool for further translational and drug discovery applications since it provides a biomimetic scenario that allows the study of the tumor microenvironment in-vitro using nondestructive longitudinal monitoring over time.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".