한빛사논문
- 조회1,837
Abstract
Yunee Kim1,*, Jouhyun Jeon2,*, Salvador Mejia3, Cindy Q. Yao1,2, Vladimir Ignatchenko3, Julius O. Nyalwidhe4,5, Anthony O. Gramolini6, Raymond S. Lance5,7, Dean A. Troyer4,5, Richard R. Drake8, Paul C. Boutros1,2,9, O. John Semmes4,5 & Thomas Kislinger1,3
1Department 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. 4Department of Microbiology and Molecular Cell Biology, Eastern Virginia Medical School, Norfolk, Virginia 23507, USA. 5 Leroy T. Canoles Jr. Cancer Research Center, Eastern VirginiaMedical School, Norfolk, Virginia 23507-1627, USA. 6Department of Physiology, University of Toronto, Toronto, Ontario, CanadaM5S 1A8. 7Department of Urology, Eastern Virginia Medical School, Norfolk, Virginia 23462, USA. 8Department 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. * These authors contributed equally to this work.
Correspondence to : Paul C Boutros or O. John Semmes or Thomas Kislinger
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.
논문정보
- Research article
- 2016년 06월 (BRIC 등록일 2016-07-04)
- 국외 연구진
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