Tumor-infiltrating nociceptor neurons promote immunosuppression
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it