Neuro-immune cross-talk in cancer
Bibliographic record
Abstract
The nervous and immune systems have co-evolved to detect and respond to internal and external threats, working together to restore homeostasis after tissue injury or infection. Sharing several receptors and ligands, they engage in direct cross-talk that substantially influences disease development. The emerging field of cancer neuro-immunity focuses on the intricate interactions between the nervous system, immune responses and tumour growth. Additional findings have revealed that nerve fibres infiltrating peripheral tumours can release neuromodulatory factors that shape both immune cell behaviour and tumour progression. Conversely, tumour-infiltrating immune cells can modify the activity of local neurons, including pain-transmitting nociceptive sensory neurons. Beyond sensory fibres, sympathetic signalling can foster immunosuppression by recruiting myeloid-derived suppressor cells and promoting T cell exhaustion. This Review summarizes current evidence on how neuronal signalling regulates peripheral antitumour immune responses within the tumour microenvironment. We describe the complex, reciprocal interactions among neurons, immune cells and malignant cells, highlighting the key parts played by the peripheral nervous system in modulating immunity against cancer. By understanding this neuro-immune axis, novel therapeutic approaches may be uncovered to strengthen antitumour immunity and enhance responses to existing cancer treatments. The nervous and immune systems have co-evolved to respond to threats, including cancer. In this Review, Amit et al. outline the reciprocal interactions among neurons, immune cells and tumour cells that regulate peripheral antitumour immune responses and discuss how these mechanisms could be leveraged to enhance immunotherapy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".