Marrow Stromal Cells for Interleukin-2 Delivery in Cancer Immunotherapy
Bibliographic record
Abstract
Marrow stromal cells (MSCs) can be easily gene-modified and clonally expanded making them ideal candidates for transgenic cell therapy. However, recent reports suggest that MSCs possess immunosuppressive effects, which may limit their clinical applications. We investigated whether interleukin (IL)-2 gene-modified MSCs can be used to mount an effective immune response against the poorly immunogenic B16 melanoma model. We first show that primary MSCs mixed with B16 cells and injected subcutaneously in syngeneic recipients do not affect tumor growth. On the other hand, IL-2-producing MSCs mixed with B16 cells significantly delayed tumor growth in an IL-2 dose-dependent manner. Furthermore, we observed that matrix-embedded IL-2-producing MSCs injected in the vicinity of preestablished B16 tumors led to absence of tumor growth in 90% of treated mice (p < 0.001). We demonstrated that tumor-bearing mice treated with IL-2-producing MSCs developed CD8-mediated tumor-specific immunity and significantly delayed tumor growth of a B16 cell challenge (p < 0.05). In addition, treatment of cd8-/-, cd4-/- and beige mice revealed that CD8+ and natural killer (NK) cells, but not CD4+ cells, were required to achieve antitumor effect. In conclusion, MSCs can be exploited to deliver IL-2 and generate effective immune responses against melanoma in mice with normal immune systems.
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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".