NK Cell Metabolism and the Potential Offered for Cancer Immunotherapy
Bibliographic record
Abstract
Abstract Immunotherapy may provide a future where curing metastatic cancer is not a goal but a reality. Significant progress has already been made with the clinical success of check-point inhibitors and more recently, CAR-T cells. It is likely that combination immune-therapies will be required for optimal success against cancer. It is also likely that conventional cell based approaches that focus on T cells may not provide expected dividends, and that harnessing the innate immune response might bring greater rewards. In this regard, Natural Killer (NK) cells, occasionally considered the innate counterpart of T cells, offer huge potential. This review considers how and why this may be so and discusses the current state of play with respect to NK cell therapies for cancer. Importantly, it will detail our current knowledge of NK cell metabolism and demonstrate how this information can synergise with the rapidly evolving field of immunometabolism, to provide new, exciting and effective ways to treat a range of cancer types.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".