The Human Ortholog of Granulocyte Macrophage Colony-Stimulating Factor and Interleukin-2 Fusion Protein Induces Potent <i>Ex vivo</i> Natural Killer Cell Activation and Maturation
Bibliographic record
Abstract
Natural killer (NK) cells are appealing cellular pharmaceuticals for cancer therapy because of their innate ability to recognize and kill tumor cells. Therefore, the development of methods that can enhance the potency in their anticancer effect would be desirable. We have previously shown that a murine granulocyte macrophage colony-stimulating factor (GM-CSF)/interleukin 2 (IL-2) fusion protein displays novel antitumor properties in vivo compared with both cytokines in combination due to recruitment of NK cells. In the present work, we have found that human ortholog of the GM-CSF/IL-2 fusion protein (a.k.a. hGIFT2) induces robust NK cell activation ex vivo with significant secretion of RANTES and a 37-fold increase in IFNgamma production when compared with either IL-2 or GM-CSF single cytokine treatment or their combination. Moreover, hGIFT2 upregulates the expression of NK cell activating receptors NKp44, NKp46, and DNAM-1 (CD226), as well as CD69, CD107a, and IL-2Rbeta expression. In addition, hGIFT2 promotes NK cell maturation, based on the downregulation of CD117 expression and upregulation of CD11b. This phenotype correlates with significantly greater cytotoxicity against tumor cells. At the molecular level, hGIFT2 leads to a potent activation of Janus-activated kinases (JAK) downstream of both IL-2 and GM-CSF receptors (JAK1 and JAK2, respectively) and consequently leads to a hyperphosphorylation of signal transducers and activators of transcription (STAT)1, STAT3, and STAT5. In conclusion, hGIFT2 fusokine possesses unique biochemical properties distinct from IL-2 and GM-CSF, constitutes a novel and potent tool for ex vivo NK cell activation and maturation, and may be of use for cancer cell immunotherapy.
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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) | 0.001 | 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 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".