Immunostimulatory Properties of the Human Ortholog of the GMCSF/IL2 Fusion Protein for Cell Based Therapy.
Bibliographic record
Abstract
Abstract NK cells constitute a potential candidate for cancer cell therapy because they express a diverse array of inhibitory and activating receptors, which recognize and kill infected or tumor cells without prior immune sensitization. However, autologous NK cell mediated adoptive immunotherapy is restricted due to insufficient cytolytic activity of NK cells from patient with aggressive malignancies. In contrast, the infusion of alloreactive NK cells has shown more successful outcomes in the treatment of cancer, but this approach also presents difficulties such as the high doses of cytokines required to induce NK cell expansion ex vivo, which may also sensitize NK cells to apoptosis. Therefore, a critical issue for NK cell based therapy is the use of appropriate growth factors or cytokines that promote NK cell expansion and activation. We have previously shown that a murine GM-CSF/IL-2 fusion protein (aka GIFT2) displays novel antitumor properties in vivo compared to both cytokines in combination regarding tumor site recruitment of macrophages and significant functional NK cell infiltration [Stagg et al., Cancer Research (December 2004)]. In the present work, we found that human GIFT2 will lead to a substantial two fold proliferation of human blood-derived NK cells which is significantly (p<0.05) superior to either IL2 or GMCSF single cytokine treatment or both cytokines combined at equimolar concentration. In addition, we observed that GIFT2 leads to robust expression of NK-cell activation markers CD69 and CD107a. In conclusion, the human GIFT2 fusokine is a novel and potent tool for ex vivo expansion of activated NK cells which may be of use in cell-based immunotherapy of cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".