Multiparameter urine analysis for quantitative bladder cancer surveillance of orthotopic xenografted mice. Issue 3 (10th January 2020)

• Record Type: Journal Article
• Authors: Tan, Xiaotian; Broses, Luke J.; Zhou, Menglian; Day, Kathleen C.; Liu, Wenyi; Li, Ziqi; Weizer, Alon Z.; Munson, Katherine A.; Khaing Oo, Maung Kyaw; Day, Mark L.; Fan, Xudong
• Is Part Of: Lab on a chip. Volume 20:Issue 3(2020)
• Page Start: 634
• Page End: 646
• Publication Date: 2020-01-10
• Subjects: Miniature electronic equipment -- Periodicals; Combinatorial chemistry -- Periodicals; Biotechnology -- Periodicals; 543.0813
• DOI: 10.1039/c9lc01006h
• Languages: English
• ISSNs: 1473-0197

Abstract

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