Back

CCR2 silencing in sensory neurons blocks bone cancer progression

Midavaine, E.; Cote, J.; Trepanier, A.; Kashem, S. W.; Dansereau, M.-A.; Longpre, J.-M.; Charbonneau, M.; Dubois, C.; Jacobi, A. M.; Rose, S. D.; Behlke, M. A.; Sarret, P.

2024-05-31 neuroscience
10.1101/2024.05.29.596531 bioRxiv
Show abstract

The peripheral nervous system has been shown to contribute to cancer growth by expanding the immunological niche. How the nervous system affects bone cancer progression and how neuroimmune pathways can be targeted for cancer treatment are not yet clear. Here, we demonstrate a profound influence of the peripheral nervous system on tumor progression, which can be targeted by silencing neuronal chemokine receptor signaling. We show that axotomy in animals with bone cancer inhibits tumor progression. Conversely, intrathecal injection of a known tumor-associated proinflammatory chemokine, CCL2, promotes tumor growth and allodynia. Silencing CCR2 in DRG neurons through a newly developed gene therapy successfully impedes tumor progression and bone remodeling and relieves bone cancer-associated pain. We demonstrate that the mechanism underlying CCR2-mediated tumor progression involves decreased neuropeptide secretion by peripheral nerves that promote expansion of the tumor-associated macrophage population. Silencing the CCR2 receptor in DRG neurons successfully normalizes the neuropeptide milieu and ameliorates altered bone remodeling. Thus, we have developed a novel therapeutic pathway for targeting a neuroimmune axis that contributes to cancer progression. HighlightsO_LICancer progression activates sensory neurons, inducing pain hypersensitivity and neuropeptide release. C_LIO_LIAxotomy impedes tumor progression. C_LIO_LICCL2 signaling in DRG neurons induces pain sensitivity and cancer growth. C_LIO_LISilencing CCR2 in the DRG reduces pain sensitivity, tumor-associated macrophage numbers and cancer growth. C_LI

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.