Preventing Surgery-Induced NK Cell Dysfunction Using Anti-TGF-β Immunotherapeutics
Bibliographic record
Abstract
Natural Killer (NK) cell cytotoxicity and interferon-gamma (IFNγ) production are profoundly suppressed postoperatively. This dysfunction is associated with increased morbidity and cancer recurrence. NK activity depends on the integration of activating and inhibitory signals, which may be modulated by transforming growth factor-beta (TGF-β). We hypothesized that impaired postoperative NK cell IFNγ production is due to altered signaling pathways caused by postoperative TGF-β. NK cell receptor expression, downstream phosphorylated targets, and IFNγ production were assessed using peripheral blood mononuclear cells (PBMCs) from patients undergoing cancer surgery. Healthy NK cells were incubated in the presence of healthy/baseline/postoperative day (POD) 1 plasma and in the presence/absence of a TGF-β-blocking monoclonal antibody (mAb) or the small molecule inhibitor (smi) SB525334. Single-cell RNA sequencing (scRNA-seq) was performed on PBMCs from six patients with colorectal cancer having surgery at baseline/on POD1. Intracellular IFNγ, activating receptors (CD132, CD212, NKG2D, DNAM-1), and downstream target (STAT5, STAT4, p38 MAPK, S6) phosphorylation were significantly reduced on POD1. Furthermore, this dysfunction was phenocopied in healthy NK cells through incubation with rTGF-β1 or POD1 plasma and was prevented by the addition of anti-TGF-β immunotherapeutics (anti-TGF-β mAb or TGF-βR smi). Targeted gene analysis revealed significant decreases in S6 and FKBP12, an increase in Shp-2, and a reduction in NK metabolism-associated transcripts on POD1. pSmad2/3 was increased and pS6 was reduced in response to rTGF-β1 on POD1, changes that were prevented by anti-TGF-β immunotherapeutics. Together, these results suggest that both canonical and mTOR pathways downstream of TGF-β mediate phenotypic changes that result in postoperative NK cell dysfunction.
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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".