3D Multicellular Stem-Like Human Breast Tumor Spheroids Enhance Tumorigenicity of Orthotopic Xenografts in Athymic Nude Rat Model
Bibliographic record
Abstract
Therapeutic targeting of stem cells needs to be strategically developed to control tumor growth and prevent metastatic burden successfully. Breast cancer presents a unique clinical problem because of the variety of cellular subtypes present, including cancer stem cells (CSCs). The development of 3D stem-like properties of human breast tumor spheroids in stem cell factor conditioned media was investigated in orthotopic xenografts for enhanced tumorgenicity in the athymic nude rat model. MCF-7, ZR-75-1, and MDA-MB-231 breast cancer cell lines were cultured in serum-free, stem cell factor-supplemented medium under non-adherent conditions and passaged to generate 3rd generation spheroids. The spheroids were co-cultured with fetal lung fibroblast (FLF) cells before orthotopic heterotransplantation into the mammary fat pads of athymic nude rats. Excised xenografts were assessed histologically by H&E staining and immunohistochemistry for breast cancer marker (ERB1), proliferation marker (Ki67), mitotic marker (pHH3), hypoxia marker (HIF-2α), CSC markers (CD47, CD44, CD24, and CD133), and vascularization markers (CD31, CD34). Breast cancer cells cultured in stem cell factor supplemented medium generated 3D spheroids exhibited increased stem-like characteristics. The 3D stem-like spheroids co-cultured with FLF as supporting stroma reproducibly and efficiently established orthotopic breast cancer xenografts in the athymic nude rat.
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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 | 0.001 | 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 | 0.001 |
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".