Multiparameter Urine Analysis for Quantitative Bladder Cancer Surveillance of Orthotopic Xenografted Mice - Beyond the 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. Last year, we developed a surgical grafting technique that can be used to simply and implant cancer cells into mouse bladders. However, the in vivo surveillance of the tumor in orthotopic models is often limited to a single light-based signal (luminescence or fluorescence).

Currently, bioluminescence imaging is the most popular method for the real-time surveillance of tumor progression in orthotopic xenograft models. However, it requires a very expensive animal imaging instrument for taking measurements as well as a genetic modification (luciferase expression) introduced to the cancer cell line prior to implantation. For the labs that do not have such ideal conditions, it would be hard to conduct research that includes real-time tumor progression monitoring.

Urine carries a vast amount of cellular and biomolecular information related to urinary diseases. It has been found that urinary biomarkers can be used to provide diagnostic and prognostic information for human bladder cancers. Therefore, detection of a panel of selected biomarkers in urine may provide a simple, cost-effective, and non-invasive means for tumor progression monitoring. However, due to the limited capability of the traditional biomarker quantification method—ELISA (enzyme-linked immunosorbent assay), especially in terms of sensitivity, speed, and sample consumption, the correlation between a panel of multiple urinary biomarkers and tumor progression in the orthotopic bladder cancer xenograft model had never been investigated.

 In this work, using a microfluidic chemiluminescent ELISA platform that has significantly enhanced sensitivity (~10X more sensitive than traditional ELISA) and much smaller sample consumption (12X less), we have successfully developed a quantitative 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.

In addition to the feasibility of real-time non-invasive tumor surveillance, our work also demonstrated strong potential in facilitating in vivo drug efficacy modeling in live animals. With our methodology, the necessity of luciferase transfection for patient-derived tumor cell lines can be reduced. This is especially beneficial for the research with patient-derived-xenograft (PDX) models, as the tumor growth can be monitored through urine measurements. It can ultimately lead to an enhancement in the throughput for and cost performance in personalized precision medicine therapies in clinical settings.

The same modeling concept can easily be adapted for other types of bladder cancer animal models. Carcinogen-based mouse models, syngeneic models, and PDX models could all benefit from this new method to quantify tumor burden in mice. Furthermore, this urine-based methodology should be applicable to the research with other types of urinary system carcinomas, such as renal cell carcinoma and prostatic carcinoma.

Written by: Xiaotian Tan, Luke J Broses, Menglian Zhou, Kathleen C Day, Wenyi Liu, Ziqi Li, Alon Z Weizer, Katherine A Munson, Maung Kyaw Khaing Oo, Mark L Day, Xudong Fan

Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA., Department of Urology, University of Michigan, Ann Arbor, MI, USA., and Rogel Comprehensive Cancer Center, University of Michigan, Ann Arbor, MI, USA., Optofluidic Bioassay, LLC, Ann Arbor, MI, USA.

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