The Potential for Cancer Immunotherapy in Targeting Surgery-Induced Natural Killer Cell Dysfunction
Bibliographic record
Abstract
Natural Killer (NK) cells are granular lymphocytes of the innate immune system that are able to recognize and kill tumor cells without undergoing clonal selection. Discovered over 40 years ago, they have since been recognized to possess both cytotoxic and cytokine-producing effector functions. Following trauma, NK cells are suppressed and their effector functions are impaired. This is especially important for cancer patients undergoing the removal of solid tumors, as surgery has shown to contribute to the development of metastasis and cancer recurrence postoperatively. We have recently shown that NK cells are critical mediators in the formation of metastasis after surgery. While research into the mechanism(s) responsible for NK cell dysfunction is ongoing, knowledge of these mechanisms will pave the way for perioperative therapeutics with the potential to improve cancer outcomes by reversing NK cell dysfunction. This review will discuss mechanisms of suppression in the postoperative environment, including hypercoagulability, suppressive soluble factors, the expansion of suppressive cell populations, and how this affects NK cell biology, including modulation of cell surface receptors, the potential for anergy, and immunosuppressive NK cell functions. This review will also outline potential immunotherapies to reverse postoperative NK dysfunction, with the goal of preventing surgery-induced metastasis.
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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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".