CCR2 silencing in sensory neurons blocks bone cancer progression
Bibliographic record
Abstract
Abstract The peripheral nervous system contributes to cancer growth, in part by shaping the immunological niche of the tumor. How the nervous system influences bone cancer progression, and whether the underlying neuroimmune pathways can be targeted therapeutically, remain unclear. Here we demonstrate a profound influence of the peripheral nervous system on tumor progression that can be countered by silencing chemokine receptor signaling in sensory neurons. Axotomy of the tumor-innervating femoral nerve inhibits tumor progression in animals bearing bone cancer, whereas intrathecal delivery of the tumor-associated proinflammatory chemokine CCL2 promotes both tumor growth and allodynia. Silencing CCR2 in dorsal root ganglion (DRG) neurons with a newly developed lipid nanoparticle-formulated Dicer-substrate siRNA impedes tumor progression and pathological bone remodeling, and relieves bone cancer-associated pain. Mechanistically, bone cancer drives CCR2-dependent transport of substance P and CGRP along the tumor-innervating femoral nerve, and these neuropeptides expand the tumor-associated macrophage population; silencing CCR2 in DRG neurons normalizes the neuropeptide milieu and ameliorates altered bone remodeling. We thus define a targetable neuroimmune axis that contributes to cancer progression. Highlights Cancer progression activates sensory neurons, driving pain hypersensitivity and neuropeptide release. Axotomy of the tumor-innervating femoral nerve impedes tumor progression. CCL2–CCR2 signaling in DRG neurons promotes pain hypersensitivity and cancer growth. Silencing CCR2 in the DRG reduces pain hypersensitivity, tumor-associated macrophage numbers and cancer growth.
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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) | 0.004 | 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".