Multiparameter urine analysis for quantitative bladder cancer surveillance of orthotopic xenografted mice.

摘要: The human-derived orthotopic xenograft mouse model is an effective platform for performing in vivo bladder cancer studies to examine tumor development, metastasis, and therapeutic effects of drugs. To date, the surveillance progression real time xenografts highly dependent on semi-quantitative imaging technologies such as bioluminescence. While these can estimate progression, they are burdened with requirements anesthetics, specialized equipment, genetic modification injected cell line. Thus, a convenient non-invasive technology quantitatively monitor growth desired. In this work, using microfluidic chemiluminescent ELISA platform, we have successfully developed rapid, multiparameter urine-based biomolecular prognostic xenografts. This method consists two steps. First, concentrations panel four urinary biomarkers quantified from urine mice bearing Second, machine learning principal component analysis (PCA) algorithms applied analyze biomarkers, subsequently, score assigned indicate growth. With methodology, monitored human that was inoculated low, medium, high numbers. We also employed performed proof principle experiment efficacy EGFR inhibitor, dacomitinib.