A Novel Dendritic Cell Population Generated by Fusing Granulocyte-Macrophage Colony-Stimulating Factor and Interleukin-21 Induces Tumor-Antigen Specific Immunity
Bibliographic record
Abstract
Abstract Abstract 5177 Introduction: Cancer recruits the immune system to promote its growth and to inhibit reactions against itself. We generated a fusion of GMCSF and IL-21 (GIFT-21) with the aim of stimulating distinct, but complementary, elements of the innate and adaptive immune system against cancer. In a previous study, GIFT-21's aberrant interactions with its cognate receptors on macrophages resulted in an unanticipated pro-inflammatory response and tumor rejection in mice. Results: We further explored this phenomenon by treating mice with dendritic cells (DC) derived by treating monocytes with GIFT-21. B16 melanoma and D2F2/neu breast cancer growth was inhibited only in mice treated with a single injection of antigen naïve GIFT-21 DCs. This effect was lost in CD8-/- and CCR2-/- mice and when mice were treated with β2 microglobulin deficient GIFT-21 DCs, and we confirmed that GIFT-21 DCs migrated to and sampled from the tumors to present tumor antigens to CCL2 recruited CD8+ T cells via MHCI. Conclusion: When stimulated with GIFT-21 DCs, the immune system can identify cancer specifically, independently of cancer type. We conclude that GIFT-21 and its associated cellular products may serve as novel therapeutic platforms for the treatment of cancer. Disclosures: No relevant conflicts of interest to declare.
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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 | 0.001 |
| 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 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".