Human Umbilical Cord Perivascular Cells Prevent Tumor Growth in a Melanoma Tumor-Bearing Mouse Model and Modulate Breast Cancer and Melanoma Cells in a Cell Line-Dependent Manner In Vitro
Bibliographic record
Abstract
First trimester (FTM) and term human umbilical cord perivascular cells are promising mesenchymal stromal cell candidates to mitigate side effects of oncotherapy, but their safety for cancer patients remains to be determined. This study was designed to determine if human umbilical cord perivascular cells modulate tumor growth when injected systemically in a tumor-bearing mouse model. Immunodeficient mice-bearing palpable subcutaneous SK-MEL-28 human melanoma tumors were randomized to receive a tail vein injection of three human umbilical cord perivascular cell lines resuspended in hank’s buffer saline solution (vehicle) or vehicle only, as a control. Fibroblast cells were included as a cell control in some experiments. Tumor size was monitored weekly and weighed at 3-weeks postinjection. Cell fate and tumor cell proliferation, apoptosis, vascularization as well as tumor-associated immune cells were assessed using immunostaining and flow cytometry. Serum tumor necrosis factor alpha and C-reactive protein levels were measured using enzyme-linked immunosorbent assays. Transwell coculture models were used to study the paracrine effects of multiple lines of human umbilical cord cells on human melanoma cell lines as well as breast cancer cell lines. Systemic administration of FTM and term human umbilical cord perivascular cells, but not fibroblast cells, prevented melanoma tumor growth in a tumor-bearing animal model by modulating tumor cell proliferation and systemic inflammatory mechanisms. Cancer cell- and donor-dependent paracrine effects on cancer cell growth were observed in vitro. Our preclinical studies thus suggest that, with regards to its effects on tumor growth, systemic administration of FTM and term human umbilical cord perivascular cells may be a safe cell therapy to address the side effects of cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".