Metastatic infiltration of nervous tissue and periosteal nerve sprouting in multiple myeloma induced bone pain
Diaz-delCastillo, M.; Palasca, O.; Nemler, T. T.; Thygesen, D. M.; Chavez-Saldana, N. A.; Vazquez-Mora, J. A.; Ponce Gomez, L. Y.; Juhl Jensen, L.; Evans, H.; Andrews, R. E.; Mandal, A.; Neves, D.; Mehlen, P.; Caruso, J. P.; Dougherty, P. M.; Price, T. J.; Chantry, A.; Lawson, M. A.; Andersen, T. L.; Jimenez-Andrade, J. M.; Heegaard, A.-M.
Show abstract
Multiple myeloma (MM) is a neoplasia of B plasma cells that often induces bone pain. However, the mechanisms underlying myeloma-induced bone pain (MIBP) are mostly unknown. Using a syngeneic MM mouse model, we show that periosteal nerve sprouting of calcitonin-gene related protein (CGRP+) and growth associated protein 43 (GAP43+) fibres occurs concurrent to the onset of nociception and its blockade provides transient pain relief. MM patient samples also showed increased periosteal innervation. Mechanistically, we investigated MM induced gene expression changes in the dorsal root ganglia (DRG) innervating the MM-bearing bone and found alterations in pathways associated with cell cycle, immune response and neuronal signalling. The MM transcriptional signature was consistent with metastatic MM infiltration to the DRG, a never-before described feature of the disease that we further demonstrated histologically. In the DRG, MM cells caused loss of vascularization and neuronal injury, which may contribute to late-stage MIBP. Interestingly, the transcriptional signature of a MM patient was consistent with MM cell infiltration to the DRG. Overall, our results suggest that MM induces a plethora of peripheral nervous system alterations that may contribute to the failure of current analgesics and suggest neuroprotective drugs as appropriate strategies to treat early onset MIBP. Significance statementMultiple myeloma is a painful bone marrow cancer that significantly impairs the quality of life of the patients. Analgesic therapies for myeloma-induced bone pain (MIBP) are limited and often ineffective, and the mechanisms of MIBP remain unknown. In this manuscript, we describe cancer-induced periosteal nerve sprouting in a mouse model of MIBP, where we also encounter metastasis to the dorsal root ganglia (DRG), a never-before described feature of the disease. Concomitant to myeloma infiltration, the lumbar DRGs presented blood vessel damage and transcriptional alterations, which may mediate MIBP. Explorative studies on human tissue support our preclinical findings. Understanding the mechanisms of MIBP is crucial to develop targeted analgesic with better efficacy and fewer side effects for this patient population.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Lipolysis of bone marrow adipocytes is required to fuel bone and the marrow niche during energy deficits 93%
- Profiling sensory neuron microenvironment after peripheral and central axon injury reveals key pathways for neural repair 93%
- Specific targeting of inflammatory osteoclastogenesis by the probiotic yeast S. boulardii CNCM I-745 reduces bone loss in osteoporosis 93%
Similar papers in this journal
- Lysophosphatidyl-choline 16:0 mediates persistent joint pain through Acid-Sensing Ion Channel 3: preclinical and clinical evidences 94%
- The impact of bone cancer on the peripheral encoding of mechanical pressure stimuli 93%
- A transcriptional toolbox for exploring peripheral neuro-immune interactions. 93%
Similar papers in this journal
- Chronic circadian disruption modulates breast cancer cell stemness and their immune microenvironment to drive metastasis in mice 94%
- Basigin Links Altered Skeletal Stem Cell Lineage Dynamics with Glucocorticoid-induced Bone Loss and Impaired Angiogenesis 94%
- Nerve Growth Factor Receptor Limits Inflammation to Promote Remodeling and Repair of Osteoarthritic Joints 93%
"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.