Mesenchymal Stem Cell Therapy for Breast Cancer: Challenges Remaining
Bibliographic record
Abstract
The treatment of breast cancer, the most common malignancy among women worldwide, remains puzzling partly due to the resistance to therapeutics, which associates with the heterogeneity of case clinical presentations, and limits in the current understanding of the pathogenesis of solid cancers. Notably, it remains unclear: (i) whether breast cancer starts strictly as a local disease before metastasizing to the lymph nodes and distant organs, i.e. if cancer initiating cells are local cells that have undergone epithelial to mesenchymal transition; (ii) or if breast cancer is intrinsically a systemic disease started by malfunctioning circulating mesenchymal stem cells (MSCs) infiltrating the breast stroma to start tumorigenesis. Such limits in our understanding of breast cancer biology have been slowing the development of MSC-based therapies exploiting the ability of these cells to home into tumorigenic sites, kill cancer cells, stop neoangiogenesis, and repair damaged tissues, as well as therapeutic approaches using these cells as vehicle for gene therapy and for delivering anticancer therapeutics, which are potential game changing therapeutic approaches, particularly in currently incurable cancers and intractable cases. Major drawbacks to MSC-based therapy implementation and use in breast cancer are herein briefly discussed.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".