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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
Published on: August 16, 2020
Enhanced Bone Metastases in Skeletally Immature Mice
Henry R Haley1, Nathan Shen1, Tonela Qyli1
1Department of Radiology, University of Michigan Medical School, Ann Arbor, MI.
None:
Bone constitutes the most common site of breast cancer metastases either at time of presentation or recurrent disease years after seemingly successful therapy. Bone metastases cause substantial morbidity, including life-threatening spinal cord compression and hypercalcemia. Given the high prevalence of patients with breast cancer, health-care costs of bone metastases (>$20,000 per episode) impose a tremendous economic burden on society. To investigate mechanisms of bone metastasis, we developed femoral artery injection of cancer cells as a physiologically relevant model of bone metastasis. Comparing young (~6 weeks), skeletally immature mice to old (~6 months) female mice with closed physes (growth plates), we showed significantly greater progression of osteolytic metastases in young animals. Bone destruction increased in the old mice following ovariectomy, emphasizing the pathologic consequences of greater bone turnover and net loss. Despite uniform initial distribution of breast cancer cells throughout the hind limb after femoral artery injection, we observed preferential formation of osteolytic bone metastases in the proximal tibia. Tropism for the proximal tibia arises in part because of TGF-β, a cytokine abundant in both physes of skeletally immature mice and matrix of bone in mice of all ages. We also showed that age-dependent effects on osteolytic bone metastases did not occur in male mice with disseminated breast cancer cells in bone. These studies establish a model system to specifically focus on pathophysiology and treatment of bone metastases and underscore the need to match biologic variables in the model to relevant subsets of patients with breast cancer.
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