Cytokines in Focus: IL-2 and IL-15 in NK Adoptive Cell Cancer Immunotherapy
Bibliographic record
Abstract
NK cell adoptive cell therapy (ACT) has emerged as a promising strategy for cancer immunotherapy, offering advantages in scalability, accessibility, efficacy, and safety.Ex vivo activation and expansion protocols, incorporating feeder cells and cytokine cocktails, have enabled the production of highly functional NK cells in clinically relevant quantities.Advances in NK cell engineering, including CRISPR-mediated gene editing and chimeric Ag receptor technologies, have further enhanced cytotoxicity, persistence, and tumor targeting.Cytokine support post-adoptive transfer, particularly with IL-2 and IL-15, remains critical for promoting NK cell survival, proliferation, and anti-tumor activity despite persistent challenges such as regulatory T cell expansion and cytokine-related toxicities.This review explores the evolving roles of IL-2 and IL-15 in NK cell-based ACT, evaluating their potential and limitations, and highlights strategies to optimize these cytokines for effective cancer immunotherapy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".