Abstract 229: Establishing reference intervals of human urine proteome for monitoring physiological and pathological changes

Urine as a true non-invasive sampling source holds great potential for biomarker discovery. But the lack of method for profiling urine proteome in high throughput and systematic evaluation of variations in urine proteomes based on large number of population have been being the major obstacles for successfully finding new biomarkers from urine. Due to low throughput, only limited number of urine samples can be measured in discovery phase of biomarker studies, which make it hard to determine whether proteins differentially expressed between groups represent actual differences between control and disease states or just physiological variations among individuals. So, candidate biomarkers often fail in validation phase. Here, we report a streamlined workflow with capacity of measuring 8 urine proteomes per day per MS at the coverage of more than 1500 proteins. With this workflow, we systematically evaluated variations in 497 human urine proteomes from 167 apparently healthy donors, allowing us to evaluate day to day and inter-personal variation in human urine proteome. Then personal and pan-human reference intervals (RIs) of urine proteome were established based on this large-scale dataset. We demonstrated that RIs can be used to monitor physiological changes by detecting transient outlier proteins, such as trans-continental travel and common flu. And it was also found that if the underlying cause is physiological variation, outlier proteins will fall back to the normal range in the follow up measurement, as exemplified in the intercontinental travel case. Persistent outlier proteins may be indicative of non-physiological conditions. These results indicate that periodical measurements of a person’s urine proteome could establish a personal health archive that would be valuable for detecting future health issues. Furthermore, we proposed a complete novel strategy dependent on RIs-based algorithm for biomarker discovery and validation to screen for diseases, which were exemplified by analyzing 154 urine proteomes from patients with 7 types of cancers. The algorithm can distinguish normal people from cancer patients with specificity of 0.95 and sensitivity of 0.85. This study paves a way to use urine proteome for health monitoring and disease screening. Citation Format: Wenchuan Leng, Xiaotian Ni, Changqing Sun, Anna Malovannaya, Yi Wang, Jun Qin. Establishing reference intervals of human urine proteome for monitoring physiological and pathological changes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 229. doi:10.1158/1538-7445.AM2017-229