GMCSF-Interleukin fusion cytokines induce novel immune effectors that can serve as biopharmaceuticals for treatment of autoimmunity and cancer
Bibliographic record
Abstract
Williams P, Galipeau J (Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, QC, Canada; and Winship Cancer Institute of Emory University, Atlanta, GA, USA). GMCSF-Interleukin fusion cytokines induce novel immune effectors that can serve as biopharmaceuticals for treatment of autoimmunity and cancer (Key Symposium). J Intern Med 2011; 269: 74–84. Abstract. We created the GIFTs, fusions of granulocyte-colony macrophage-stimulating-factor with IL-2, or IL-15 or IL-21, in order to stimulate distinct, but complimentary elements of the immune response. We found that the physical coupling of two functionally distinct cytokines as a bifunctional hybrid allowed for synergistic bioactivity not seen by the simple combined use of parent components. Indeed, despite how these interleukins are pro-inflammatory cytokines that serve essential roles in the maturation of CD8+ T cells and NK cells, the GIFTs were remarkably different from one another, with GIFT-2 and GIFT-21 promoting and GIFT-15 downregulating inflammation. The common denominator to the biochemistry of these fusokines was their ability to hijack the signalling machinery associated with common to their respective γ-chain interleukin receptors, radically altering the activation status of responding lymphomyeloid cells. By studying the GIFTs, we found that both secreted and cell surface factors presented by GIFT-activated lymphomyeloid cells were required to modulate the immune responses in murine models of multiple sclerosis and cancer. The ability of GIFTs to co-opt the normal signalling machinery of interleukin receptors leads to the acquisition of functional responder cell phenotypes unparalleled in nature. These novel properties provide opportunities to alter maladapted immune responses in health and disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".