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J Proteome Res. 2017 Apr 7;16(4):1617-1631. doi: 10.1021/acs.jproteome.6b00979. Epub 2017 Mar 23.

Large-Scale SRM Screen of Urothelial Bladder Cancer Candidate Biomarkers in Urine.

Author information

Genomics and Proteomics Research Unit, Department of Oncology, Luxembourg Institute of Health , 1 A-B rue Thomas Edison, L-1445 Strassen, Luxembourg.
Univ. Grenoble Alpes , BIG-BGE, F-38000 Grenoble, France.
CEA , BIG-BGE, F-38000 Grenoble, France.
INSERM , BGE, F-38000 Grenoble, France.
NorLux Neuro-Oncology Laboratory, Department of Oncology, Luxembourg Institute of Health (LIH) , Luxembourg L-1526, Luxembourg.
INSERM , U955 Eq07, Créteil 94000, France.
Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre (CNIO) , Madrid 28029, Spain.
Department of Biology, Institute of Molecular Systems Biology, ETHZ , Zürich 8093, Switzerland.
Institut Curie , Centre de Recherche, Paris 75005, France.
CNRS, UMR144, Equipe Oncologie Moléculaire , Paris 75248, France.


Urothelial bladder cancer is a condition associated with high recurrence and substantial morbidity and mortality. Noninvasive urinary tests that would detect bladder cancer and tumor recurrence are required to significantly improve patient care. Over the past decade, numerous bladder cancer candidate biomarkers have been identified in the context of extensive proteomics or transcriptomics studies. To translate these findings in clinically useful biomarkers, the systematic evaluation of these candidates remains the bottleneck. Such evaluation involves large-scale quantitative LC-SRM (liquid chromatography-selected reaction monitoring) measurements, targeting hundreds of signature peptides by monitoring thousands of transitions in a single analysis. The design of highly multiplexed SRM analyses is driven by several factors: throughput, robustness, selectivity and sensitivity. Because of the complexity of the samples to be analyzed, some measurements (transitions) can be interfered by coeluting isobaric species resulting in biased or inconsistent estimated peptide/protein levels. Thus the assessment of the quality of SRM data is critical to allow flagging these inconsistent data. We describe an efficient and robust method to process large SRM data sets, including the processing of the raw data, the detection of low-quality measurements, the normalization of the signals for each protein, and the estimation of protein levels. Using this methodology, a variety of proteins previously associated with bladder cancer have been assessed through the analysis of urine samples from a large cohort of cancer patients and corresponding controls in an effort to establish a priority list of most promising candidates to guide subsequent clinical validation studies.


bladder cancer; candidate biomarkers; large-scale SRM screen; mass spectrometry; noninvasive; targeted proteomics; urine

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