Preventing Postoperative Metastatic Disease by Inhibiting Surgery-Induced Dysfunction in Natural Killer Cells
Bibliographic record
Abstract
Natural killer (NK) cell clearance of tumor cell emboli following surgery is thought to be vital in preventing postoperative metastases. Using a mouse model of surgical stress, we transferred surgically stressed NK cells into NK-deficient mice and observed enhanced lung metastases in tumor-bearing mice as compared with mice that received untreated NK cells. These results establish that NK cells play a crucial role in mediating tumor clearance following surgery. Surgery markedly reduced NK cell total numbers in the spleen and affected NK cell migration. Ex vivo and in vivo tumor cell killing by NK cells were significantly reduced in surgically stressed mice. Furthermore, secreted tissue signals and myeloid-derived suppressor cell populations were altered in surgically stressed mice. Significantly, perioperative administration of oncolytic parapoxvirus ovis (ORFV) and vaccinia virus can reverse NK cell suppression, which correlates with a reduction in the postoperative formation of metastases. In human studies, postoperative cancer surgery patients had reduced NK cell cytotoxicity, and we show for the first time that oncolytic vaccinia virus markedly increases NK cell activity in patients with cancer. These data provide direct in vivo evidence that surgical stress impairs global NK cell function. Perioperative therapies aimed at enhancing NK cell function will reduce metastatic recurrence and improve survival in surgical cancer patients.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".