Targeted proteomics identifies liquid-biopsy signatures for extracapsular prostate cancer

Yunee Kim, Jouhyun Jeon, Salvador Mejia, Cindy Q Yao, Vladimir Ignatchenko, Julius O Nyalwidhe, Anthony O Gramolini, Raymond S Lance, Dean A Troyer, Richard R Drake, Paul C Boutros (), O. John Semmes () and Thomas Kislinger ()
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Yunee Kim: University of Toronto
Jouhyun Jeon: Informatics and Bio-computing Program, Ontario Institute for Cancer Research
Salvador Mejia: Princess Margaret Cancer Center, University Health Network
Cindy Q Yao: University of Toronto
Vladimir Ignatchenko: Princess Margaret Cancer Center, University Health Network
Julius O Nyalwidhe: Eastern Virginia Medical School
Anthony O Gramolini: University of Toronto
Raymond S Lance: Leroy T. Canoles Jr. Cancer Research Center, Eastern Virginia Medical School
Dean A Troyer: Eastern Virginia Medical School
Richard R Drake: Medical University of South Carolina
Paul C Boutros: University of Toronto
O. John Semmes: Eastern Virginia Medical School
Thomas Kislinger: University of Toronto

Nature Communications, 2016, vol. 7, issue 1, 1-10

Abstract: Abstract Biomarkers are rapidly gaining importance in personalized medicine. Although numerous molecular signatures have been developed over the past decade, there is a lack of overlap and many biomarkers fail to validate in independent patient cohorts and hence are not useful for clinical application. For these reasons, identification of novel and robust biomarkers remains a formidable challenge. We combine targeted proteomics with computational biology to discover robust proteomic signatures for prostate cancer. Quantitative proteomics conducted in expressed prostatic secretions from men with extraprostatic and organ-confined prostate cancers identified 133 differentially expressed proteins. Using synthetic peptides, we evaluate them by targeted proteomics in a 74-patient cohort of expressed prostatic secretions in urine. We quantify a panel of 34 candidates in an independent 207-patient cohort. We apply machine-learning approaches to develop clinical predictive models for prostate cancer diagnosis and prognosis. Our results demonstrate that computationally guided proteomics can discover highly accurate non-invasive biomarkers.

Date: 2016
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DOI: 10.1038/ncomms11906

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