Neo-Organoid of Marrow Mesenchymal Stromal Cells Secreting Interleukin-12 for Breast Cancer Therapy
Bibliographic record
Abstract
Bone marrow-derived mesenchymal stromal cells (MSCs), beneficial for regenerative medicine applications due to their wide differentiation capabilities, also hold promise as cellular vehicles for the delivery of therapeutic plasma-soluble gene products due to their ease of handling, expansion, and genetic engineering. We hypothesized that MSCs, gene enhanced to express interleukin-12 (IL-12) and then embedded in a matrix, may act as an anticancer neo-organoid when delivered s.c. in autologous/syngeneic hosts. We performed such experiments in mice and noted that primary murine MSCs retrovirally engineered to secrete murine IL-12 can significantly interfere with growth of 4T1 breast cancer cells in vivo, with a more substantial anticancer action achieved when these cells are embedded in a matrix. Plasma of mice that received the IL-12 MSC-containing neo-organoids showed increased levels of IL-12 and IFN-gamma. Histopathologic analysis revealed less tumor cells in implants of 4T1 cells with IL-12 MSCs, and the presence of necrotic tumor islets and necrotic capillaries, suggesting antiangiogenesis. We also showed that the anticancer effect exerted by the IL-12 MSCs is immune mediated because it is absent in immunodeficient mice, is not due to systemic IL-12 delivery, and also occurs in a B16 melanoma model. This study therefore establishes the feasibility of using gene-enhanced MSCs in a cell-based neo-organoid approach for 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 | 0.000 | 0.000 |
| 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".