Tumor-specific cholinergic CD4+ T lymphocytes guide immunosurveillance of hepatocellular carcinoma
Bibliographic record
Abstract
Abstract Cholinergic nerves are involved in tumor progression and dissemination. In contrast to other visceral tissues, cholinergic innervation in the hepatic parenchyma is poorly detected. It remains unclear whether there is any form of cholinergic regulation of liver cancer. Here, we show that cholinergic T cells curtail the development of liver cancer by supporting antitumor immune responses. In a mouse multihit model of hepatocellular carcinoma (HCC), we observed activation of the adaptive immune response and induction of two populations of CD4+ T cells expressing choline acetyltransferase (ChAT), including regulatory T cells and dysfunctional PD-1+ T cells. Tumor antigens drove the clonal expansion of these cholinergic T cells in HCC. Genetic ablation of Chat in T cells led to an increased prevalence of preneoplastic cells and exacerbated liver cancer due to compromised antitumor immunity. Mechanistically, the cholinergic activity intrinsic in T cells constrained Ca2+–NFAT signaling induced by T cell antigen receptor engagement. Without this cholinergic modulation, hyperactivated CD25+ T regulatory cells and dysregulated PD-1+ T cells impaired HCC immunosurveillance. Our results unveil a previously unappreciated role for cholinergic T cells in liver cancer immunobiology.
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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".