Development of an IL-21 based TriKE for in vivo NK cell expansion and cytotoxicity against solid tumors 4799
Bibliographic record
Abstract
Abstract Description Natural Killer (NK) cells are a promising adoptive cell therapy given their ability to target tumors in an antigen independent manner. NK cells can be expanded ex vivo using membrane bound IL-21 (mbIL-21) feeder cells enhancing their anti-tumor activity. We have also shown that expanded NK cells are metabolically reprogrammed, allowing them to withstand the metabolically hostile tumor microenvironment. While this approach to expand NK cells has demonstrated promising results in preclinical models and early clinical trials, ex vivo expansion requires resources and GMP-grade manufacturing facilities, increasing the cost and accessibility of this treatment. To overcome these challenges, we are developing a tri-specific killer engagers (TriKE) to activate and expand NK cells directly in cancer patients. Our TriKE consists of a HER-2 protein binding domain, an NKp30 binding domain, and an activating cytokine, IL-21 to induce NK activation and cytotoxicity against HER-2+ cancers. We demonstrate that in vitro, the TriKE elicits robust STAT-3 mediated signaling. Additionally, in a mouse model for HER-2 positive ovarian cancer, following adoptive transfer of human NK cells, mice treated with the TriKE showed increased NK cell accumulation in the peritoneum compared to untreated mice. Overall, we provide evidence that our TriKE is a promising strategy to expand NK cells in vivo to target solid tumors. Topic Categories Vaccines and Immunotherapy (VAC)
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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) | 0.002 | 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".