Perioperative Influenza Vaccination Reduces Postoperative Metastatic Disease by Reversing Surgery-Induced Dysfunction in Natural Killer Cells
Bibliographic record
Abstract
PURPOSE: Surgical removal of solid primary tumors is an essential component of cancer treatment. Surgery-induced dysfunction in natural killer (NK) cells has been linked to the development of metastases in animal models and patients with cancer. We investigated the activation of NK cells using influenza vaccine in the perioperative period to eradicate micrometastatic disease. EXPERIMENTAL DESIGN: Both the B16lacZ and 4T1 tumor models in immunocompetent mice were used to assess the in vivo efficacy of perioperative influenza vaccine administration. In healthy human donors and cancer surgery patients, we assessed NK cell function pre- and post-influenza vaccination using both in vivo and ex vivo assays. RESULTS: Using the TLR3 agonist poly(I:C), we showed as proof-of-principle that perioperative administration of a nonspecific innate immune stimulant can inhibit surgery-induced dysfunction in NK cells and attenuate metastases. Next, we assessed a panel of prophylactic vaccines for NK cell activation and determined that inactivated influenza vaccine was the best candidate for perioperative administration. Perioperative influenza vaccine significantly reduced tumor metastases and improved NK cytotoxicity in preclinical tumor models. Significantly, IFNα is the main cytokine mediator for the therapeutic effect of influenza vaccination. In human studies, influenza vaccine significantly enhanced NK cell activity in healthy human donors and cancer surgery patients. CONCLUSION: These results provide the preclinical rationale to pursue future clinical trials of perioperative NK cell activation, using vaccination in cancer surgery patients. Research into perioperative immune therapy is warranted to prevent immune dysfunction following surgery and eradicate metastatic disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".