Addressing Natural Killer Cell Dysfunction and Plasticity in Cell-Based Cancer Therapeutics
Bibliographic record
Abstract
Natural killer (NK) cells are cytotoxic group 1 innate lymphoid cells (ILC), known for their role as killers of stressed, cancerous, and virally infected cells. Beyond this cytotoxic function, NK cell subsets can influence broader immune responses through cytokine production and have been linked to central roles in non-immune processes, such as the regulation of vascular remodeling in pregnancy and cancer. Attempts to exploit the anti-tumor functions of NK cells have driven the development of various NK cell-based therapies, which have shown promise in both pre-clinical disease models and early clinical trials. However, certain elements of the tumor microenvironment, such as elevated transforming growth factor (TGF)-β, hypoxia, and indoalemine-2,3-dioxygenase (IDO), are known to suppress NK cell function, potentially limiting the longevity and activity of these approaches. Recent studies have also identified these factors as contributors to NK cell plasticity, defined by the conversion of classical cytotoxic NK cells into poorly cytotoxic, tissue-resident, or ILC1-like phenotypes. This review summarizes the current approaches for NK cell-based cancer therapies and examines the challenges presented by tumor-linked NK cell suppression and plasticity. Ongoing efforts to overcome these challenges are discussed, along with the potential utility of NK cell therapies to applications outside cancer.
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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 | 0.001 | 0.001 |
| 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".