Controlled tumor heterogeneity in a co-culture system by 3D bio-printed tumor-on-chip model
Bibliographic record
Abstract
Abstract Background Cancer treatment resistance is a consequence of cell diversity and tumor heterogeneity. Tumor cell-cell and cell-microenvironment interactions significantly influence tumor progression and invasion, which have important implications for diagnosis, therapeutic treatment and chemoresistance. Method In this study, we develop 3D bioprinted in vitro models of the breast cancer tumor microenvironment (TME) made of co-cultured cells distributed in a hydrogel matrix with controlled architecture to model tumor heterogeneity. We hypothesize that the tumor could be represented by a cancer cell-laden co-culture hydrogel construct, whereas its microenvironment can be modeled in a microfluidic chip capable of producing a chemical gradient. Breast cancer cells (MCF7 and MDA-MB-231) and non-tumorigenic mammary epithelial cells (MCF10) were embedded in the alginate-gelatine hydrogels and printed using a multi-cartridge extrusion bioprinter. Results Our method gives special control on the cell positions in the co-culture system, whereas different tumor architectures can be designed. Cellularly heterogeneous samples comprised of two different cancer cells with controlled density are developed in specific initial locations, i.e. two cell types randomly mixed or positioned in sequential layers. A migration-inducing chemical microenvironment was created in a chamber with a gradual chemical gradient to study the cell migration in the complex tumor construct toward the chemoattractant. As a proof of concept, the different migration pattern of MC7 cells toward the epithelial growth factor gradient was studied with presence of MCF10 in different ratio in this device. Conclusion Combining 3D bioprinting with microfluidic device in our method provides a great tool to create different tumor architectures as can be seen in different patients, and study cancer cells behaviour with accurate special and temporal resolution.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".