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A proteasome inhibitor, bortezomib, inhibits breast cancer growth and reduces osteolysis by downregulating metastatic genes

PURPOSE: The incidence of bone metastasis in advanced breast cancer (BrCa) exceeds 70%. Bortezomib, a proteasome inhibitor used for the treatment of multiple myeloma, also promotes bone formation. We tested the hypothesis that proteasome inhibitors can ameliorate BrCa osteolytic disease. EXPERIMENTAL DESIGN: To address the potentially beneficial effect of bortezomib in reducing tumor growth in the skeleton and counteracting bone osteolysis, human MDA-MB-231 BrCa cells were injected into the tibia of mice to model bone tumor growth for in vivo assessment of treatment regimens before and after tumor growth. RESULTS: Controls exhibited tumor growth, destroying trabecular and cortical bone and invading muscle. Bortezomib treatment initiated following inoculation of tumor cells strikingly reduced tumor growth, restricted tumor cells mainly to the marrow cavity, and almost completely inhibited osteolysis in the bone microenvironment over a 3- to 4-week period as shown by [(18)F]fluorodeoxyglucose positron emission tomography, micro-computed tomography scanning, radiography, and histology. Thus, proteasome inhibition is effective in killing tumor cells within the bone. Pretreatment with bortezomib for 3 weeks before inoculation of tumor cells was also effective in reducing osteolysis. Our in vitro and in vivo studies indicate that mechanisms by which bortezomib inhibits tumor growth and reduces osteolysis result from inhibited cell proliferation, necrosis, and decreased expression of factors that promote BrCa tumor progression in bone. CONCLUSION: These findings provide a basis for a novel strategy to treat patients with BrCa osteolytic lesions, and represent an approach for protecting the entire skeleton from metastatic bone disease

Automated outbreak detection of hospital-associated pathogens: Value to infection prevention programs.

ObjectiveTo assess the utility of an automated, statistically-based outbreak detection system to identify clusters of hospital-acquired microorganisms.DesignMulticenter retrospective cohort study.SettingThe study included 43 hospitals using a common infection prevention surveillance system.MethodsA space-time permutation scan statistic was applied to hospital microbiology, admission, discharge, and transfer data to identify clustering of microorganisms within hospital locations and services. Infection preventionists were asked to rate the importance of each cluster. A convenience sample of 10 hospitals also provided information about clusters previously identified through their usual surveillance methods.ResultsWe identified 230 clusters in 43 hospitals involving Gram-positive and -negative bacteria and fungi. Half of the clusters progressed after initial detection, suggesting that early detection could trigger interventions to curtail further spread. Infection preventionists reported that they would have wanted to be alerted about 81% of these clusters. Factors associated with clusters judged to be moderately or highly concerning included high statistical significance, large size, and clusters involving Clostridioides difficile or multidrug-resistant organisms. Based on comparison data provided by the convenience sample of hospitals, only 9 (18%) of 51 clusters detected by usual surveillance met statistical significance, and of the 70 clusters not previously detected, 58 (83%) involved organisms not routinely targeted by the hospitals' surveillance programs. All infection prevention programs felt that an automated outbreak detection tool would improve their ability to detect outbreaks and streamline their work.ConclusionsAutomated, statistically-based outbreak detection can increase the consistency, scope, and comprehensiveness of detecting hospital-associated transmission