GIFT4 fusokine converts leukemic B cells into immune helper cells
Bibliographic record
Abstract
BACKGROUND: Chronic lymphocytic leukemia (CLL) remains incurable with standard therapy, and is characterized by excessive expansion of monoclonal abnormal mature B cells and more regulatory immune properties of T cell compartment. Thus, developing novel strategies to enhance immune function merits further investigation as a possible therapy for CLL. METHODS: We generated a fusion cytokine (fusokine) arising from the combination of human GM-CSF and IL-4 (named GIFT4). Primary CLL cells were treated with GIFT4 or GM-CSG and IL-4 in vitro. GIFT4-triggered STAT5 signaling in CLL cells was examined by Western blot. The phenotype and secretome of GIFT4-treated CLL cells (GIFT4-CLL cells), and the immune stimulatory function of GIFT4-CLL cells on autologous T cells were analyzed by flow cytometry and luminex assay. RESULTS: GIFT4-CLL up-regulated the expression of co-stimulatory molecules CD40, CD80 and CD86 and adhesion molecule CD54. GIFT4-CLL cells secreted IL-1β, IL-6, ICAM-1 and substantial IL-2 relative to unstimulated CLL cells. GIFT4 treatment led to JAK1, JAK2 and JAK3-mediated hyper-phosphorylation of STAT5 in primary CLL cells, which is essential for GIFT4-triggered conversion of CLL cells. GIFT4-CLL cells directly propelled the expansion of autologous IFN-γ-producing CD314(+) cytotoxic T cells in vitro, and that these could lyse autologous CLL cells. Furthermore, administration of GIFT4 protein promoted the expansion of human T cells in NOD-scid IL2Rγ(null) immune deficient mice adoptively pre-transferred with peripheral blood mononuclear cells from subjects with CLL. CONCLUSION: GIFT4 has potent capability to converts primary CLL cells into APC-like immune helper cells that initiate a T cell driven anti-CLL immune response.
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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".