Multimodal prehabilitation enhances innate antitumor immunity via NK cell recruitment
Bibliographic record
Abstract
Abstract BACKGROUND While the clinical benefits of multimodal prehabilitation in cancer patients are well defined, the underlying immune modulations have not been studied. The objective of this study was to examine how prehabilitation can alter lung cancer immunity. METHODS Newly diagnosed lung cancer patients were referred to the prehabilitation clinic for preoperative personalized multimodal intervention (exercise training, nutritional optimization, and anxiety reduction) and blood samples were collected at baseline and surgery. Tumor samples were collected at surgery and compared to matched control samples from patients who did not receive prehabilitation. An animal model was used to study prehabilitation and tumor growth kinetics. RESULTS Twenty-eight lung cancer patients who underwent multimodal prehabilitation were included (McGill University Health Centre Research Ethics Board #2023-9005). After prehabilitation, patient-isolated peripheral blood mononuclear cells (PBMCs) showed significantly increased cytotoxicity against cancer cells ( p < 0.0001) and significantly increased circulating natural killer (NK) cells in cohort ( p = 0.0290) and paired analyses ( p = 0.0312). Compared to matched controls, patients who received prehabilitation had significantly more intra-tumor NK cells ( p = 0.0172). In vivo , we observed a significant increase in circulating NK cells ( p = 0.0364) and slower tumor growth ( p = 0.0396) with prehabilitation. When NK cells were depleted in prehabilitated mice, we observed a decrease in the protective effects of prehabilitation ( p = 0.0314) and overall, we observed a significant correlation between circulating NK cells and reduced tumor volume ( p = 0.0203, r = -0.5143). CONCLUSIONS Multimodal prehabilitation may play a role in antitumor immunity by increasing peripheral and tumour-infiltrating NK cells leading to a reduced cancer burden. Future studies on the protective effect of prehabilitation on postoperative immunity should be conducted.
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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) | 0.004 | 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".