Granulocyte-Macrophage Colony-Stimulating Factor and Interleukin-2 Fusion cDNA for Cancer Gene Immunotherapy
Bibliographic record
Abstract
Genetic engineering of tumor cells to express both granulocyte-macrophage colony-stimulating factor (GM-CSF) and interleukin (IL)-2 can induce synergistic immune antitumor effects. Paradoxically, the combination has also been reported to down-regulate certain immune functions, highlighting the unpredictability of dual cytokine use. We hypothesized that a GM-CSF and IL-2 fusion transgene (GIFT) could circumvent such limitations yet preserve synergistic features. We designed a fusion cDNA of murine GM-CSF and IL-2. Protein structure computer modeling of GIFT protein predicted for intact ligand binding domains for both cytokines. B16 mouse melanoma cells were gene modified to express GIFT (B16GIFT), and these cells were unable to form tumors in C57bl/6 mice. Irradiated B16GIFT whole-cell tumor vaccine could also induce absolute protective immunity against challenge by live B16 cells. In mice with established melanoma, B16GIFT therapeutic cellular vaccine significantly improved tumor-free survival when compared with B16 expressing both IL-2 and GM-CSF. We show that GIFT induced a significantly greater tumor site recruitment of macrophages than combined GM-CSF and IL-2 and that macrophage recruitment arises from novel chemotactic feature of GIFT. In contrast to suppression by GM-CSF of natural killer (NK) cell recruitment despite coexpression of IL-2, GIFT leads to significant functional NK cell infiltration as confirmed in NK-defective beige mice. In conclusion, we demonstrated that a fusion between GM-CSF and IL-2 can invoke greater antitumor effect than both cytokines in combination, and novel immunobiological properties can arise from such chimeric constructs.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".