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Nat Commun. 2016 Jun 28;7:11906. doi: 10.1038/ncomms11906.

Targeted proteomics identifies liquid-biopsy signatures for extracapsular prostate cancer.

Author information

1
Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada M5G 1L7.
2
Informatics and Bio-computing Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada M5G 0A3.
3
Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada M5G 1L7.
4
Department of Microbiology and Molecular Cell Biology, Eastern Virginia Medical School, Norfolk, Virginia 23507, USA.
5
Leroy T. Canoles Jr. Cancer Research Center, Eastern Virginia Medical School, Norfolk, Virginia 23507-1627, USA.
6
Department of Physiology, University of Toronto, Toronto, Ontario, Canada M5S 1A8.
7
Department of Urology, Eastern Virginia Medical School, Norfolk, Virginia 23462, USA.
8
Department of Cell and Molecular Pharmacology and Experimental Therapeutics, Medical University of South Carolina, Charleston, South Carolina 29425, USA.
9
Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario, Canada M5S 1A8.

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.

PMID:
27350604
PMCID:
PMC4931234
DOI:
10.1038/ncomms11906
[Indexed for MEDLINE]
Free PMC Article

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