Killers 2.0: NK cell therapies at the forefront of cancer control
Bibliographic record
Abstract
Natural killer (NK) cells are innate cytotoxic lymphocytes involved in the surveillance and elimination of cancer. As we have learned more and more about the mechanisms NK cells employ to recognize and eliminate tumor cells, and how, in turn, cancer evades NK cell responses, we have gained a clear appreciation that NK cells can be harnessed in cancer immunotherapy. Here, we review the evidence for NK cells' critical role in combating transformed and malignant cells, and how cancer immunotherapies potentiate NK cell responses for therapeutic purposes. We highlight cutting-edge immunotherapeutic strategies in preclinical and clinical development such as adoptive NK cell transfer, chimeric antigen receptor-expressing NK cells (CAR-NKs), bispecific and trispecific killer cell engagers (BiKEs and TriKEs), checkpoint blockade, and oncolytic virotherapy. Further, we describe the challenges that NK cells face (e.g., postsurgical dysfunction) that must be overcome by these therapeutic modalities to achieve cancer clearance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".