A 3D, Compartmental Tumor-Stromal Microenvironment Model of Patient-Derived Bone Metastasis
Bibliographic record
Abstract
Bone is a frequent site of tumor metastasis. The bone-tumor microenvironment is heterogeneous and complex in nature. Such complexity is compounded by relations between metastatic and bone cells influencing their sensitivity/resistance to chemotherapeutics. Standard chemotherapeutics may not show efficacy for every patient, and new therapeutics are slow to emerge, owing to the limitations of existing 2D/3D models. We previously developed a 3D interface model for personalized therapeutic screening, consisting of an electrospun poly lactic acid mesh activated with plasma species and seeded with stromal cells. Tumor cells embedded in an alginate-gelatin hydrogel are overlaid to create a physiologic 3D interface. Here, we applied our 3D model as a migration assay tool to verify the migratory behavior of different patient-derived bone metastasized cells. We assessed the impact of two different chemotherapeutics, Doxorubicin and Cisplatin, on migration of patient cells and their immortalized cell line counterparts. We observed different migratory behaviors and cellular metabolic activities blocked with both Doxorubicin and Cisplatin treatment; however, higher efficiency or lower IC50 was observed with Doxorubicin. Gene expression analysis of MDA-MB231 that migrated through our 3D hybrid model verified epithelial-mesenchymal transition through increased expression of mesenchymal markers involved in the metastasis process. Our findings indicate that we can model tumor migration in vivo, in line with different cell characteristics and it may be a suitable drug screening tool for personalized medicine approaches in metastatic cancer treatment.
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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 | 0.001 | 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".