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Record W4409973477 · doi:10.4110/in.2025.25.e17

Cytokines in Focus: IL-2 and IL-15 in NK Adoptive Cell Cancer Immunotherapy

2025· review· en· W4409973477 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueImmune Network · 2025
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsInstitute of Infection and ImmunityUniversity of Ottawa
FundersNational Research Foundation of KoreaCanadian Institutes of Health ResearchNational Research Foundation
KeywordsAdoptive immunotherapyCancer immunotherapyMedicineImmunotherapyAdoptive cell transferImmunologyInterleukin 15T cellCancer researchInterleukinCytokineImmune system

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.979
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.015
GPT teacher head0.281
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it