Pathway-guided monitoring of the disease course in bladder cancer with longitudinal urine proteomics

Abstract

BackgroundMonitoring bladder cancer over time requires invasive and costly procedures. Less invasive approaches are required using readily available biological samples such as urine. In this study, we demonstrate a method for longitudinal analysis of the urine proteome to monitor the disease course in patients with bladder cancer.MethodsWe compared the urine proteomes of patients who experienced recurrence and/or progression (n = 13) with those who did not (n = 17). We identified differentially expressed proteins within various pathways related to the hallmarks of cancer. The variation of such pathways during the disease course was determined using our differential personal pathway index (dPPi) calculation, which could indicate disease progression and the need for medical intervention.ResultsSeven hallmark pathways are used to develop the dPPi. We demonstrate that we can successfully longitudinally monitor the disease course in bladder cancer patients through a combination of urine proteomic analysis and the dPPi calculation, over a period of 62 months.ConclusionsUsing the information contained in the patient's urinary proteome, the dPPi reflects the individual's course of bladder cancer, and helps to optimise the use of more invasive procedures such as cystoscopy. Plain language summaryBladder cancer must be closely monitored for progression, but this requires expensive and invasive procedures such as cystoscopy. Less invasive procedures using readily available samples such as urine are needed. Here, we present an approach that measures the levels of various proteins in the urine. We compare protein levels at different points during the disease course in patients with bladder cancer, and show this helps to flag disease recurrence and the need for medical intervention. Our approach could help clinicians to determine which patients require more invasive testing and treatment. Carvalho et al. develop an analysis pipeline for label-free urine proteomics data. Their approach allows monitoring of the disease course in patients with bladder cancer and flags the need for medical intervention.