Natural killer cell therapies in cancer: innovations, challenges, and future directions
Bibliographic record
Abstract
INTRODUCTION: Natural killer (NK) cells are innate immune effectors that can eliminate malignant cells without prior sensitization. By recognizing cellular stress signals and releasing inflammatory mediators, they contribute to immune surveillance and regulation. Their therapeutic potential lies in their ability to act across donor barriers with a reduced risk of graft-related complications; however, clinical translation remains challenging due to tumor immune evasion and limited persistence in suppressive environments. AREAS COVERED: This review summarizes the biological roles of NK cells in cancer immunity and examines recent therapeutic approaches that harness their cytotoxic and regulatory properties. We discuss barriers to clinical application, including immune suppression, antigen loss, and manufacturing limitations. In addition, we highlight emerging strategies, such as gene editing, rational combination therapies, and standardized clinical trial designs, aimed at improving therapeutic efficacy. EXPERT OPINION: NK cell-based therapies represent a promising avenue in cancer immunotherapy but require carefully designed solutions to overcome their inherent limitations. Advances in biomarker-guided patient selection, integration with existing treatment modalities, and international collaboration will be critical for translating NK cell biology into effective and durable clinical outcomes.
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.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".