TUMOR-INFILTRATING NOCICEPTOR NEURONS PROMOTE IMMUNOSUPPRESSION
Bibliographic record
Abstract
Nociceptor neurons impact tumor immunity. Removing nociceptor neurons reduced myeloid-derived suppressor cell (MDSCs) tumor infiltration in mouse models of head and neck carcinoma and melanoma. Carcinoma-released small extracellular vesicles (sEVs) attract nociceptive nerves to tumors. sEV-deficient tumors fail to develop in mice lacking nociceptor neurons. Exposure of dorsal root ganglia (DRG) neurons to cancer sEVs elevated expression of Substance P, IL-6 and injury-related neuronal markers while treatment with cancer sEVs and cytotoxic CD8 T-cells induced an immunosuppressive state (increased exhaustion ligands and cytokines). Cancer patient sEVs enhanced DRG responses to capsaicin, indicating increased nociceptor sensitivity. Conditioned media from DRG and cancer cell co-cultures promoted expression of MDSC markers in primary bone marrow cells while DRG conditioned media together with cancer sEVs induced checkpoint expression on T-cells. Our findings indicate that nociceptor neurons facilitate CD8+ T cell exhaustion and enhance MDSC infiltration. Targeting nociceptor-released IL-6 emerges as a novel strategy to disrupt harmful neuro-immune interactions in cancer and enhance anti-tumor immunity.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".