Tumor-infiltrating nociceptor neurons promote immunosuppression
Bibliographic record
Abstract
Small extracellular vesicles (sEVs) released from tumors recruit nociceptor neurons to the tumor bed. Here, we found that ablating these neurons in mouse models of head and neck carcinoma and melanoma reduced the infiltration of myeloid-derived suppressor cells (MDSCs). Moreover, sEV-deficient tumors failed to develop in mice lacking nociceptor neurons. We investigated the interplay between tumor-infiltrating nociceptors and immune cells in head and neck squamous cell carcinoma (HNSCC) and melanoma. Upon exposure to cancer-derived sEVs, mouse dorsal root ganglion (DRG) neurons secreted increased amounts of substance P, IL-6, and injury-associated neuronal markers. Patient-derived sEVs sensitized DRG neurons to capsaicin, implying enhanced nociceptor responsiveness. Furthermore, nociceptors cultured with sEVs induced an immunosuppressed state in CD8 + T cells. Incubation with conditioned medium from cocultures of neurons and cancer cells resulted in increased expression of markers of MDSCs and suppressive function in primary bone marrow cells, and the combination of neuron-conditioned medium and cancer sEVs promoted checkpoint receptor expression on T cells. Together, these findings reveal that nociceptor neurons facilitate CD8 + T cell exhaustion and bolster MDSC infiltration into HNSCC and melanoma. Consequently, targeting nociceptors may provide a strategy to disrupt detrimental neuroimmune cross-talk in cancer and potentiate antitumor 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 | 0.000 |
| 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".