Targeted proteomics identifies liquid-biopsy signatures for extracapsular prostate cancer

• Published in: Nature communications Vol. 7; no. 1; p. 11906
• Main Authors: Kim, Yunee, Jeon, Jouhyun, Mejia, Salvador, Yao, Cindy Q, Ignatchenko, Vladimir, Nyalwidhe, Julius O, Gramolini, Anthony O, Lance, Raymond S, Troyer, Dean A, Drake, Richard R, Boutros, Paul C, Semmes, O. John, Kislinger, Thomas
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 28.06.2016; Nature Publishing Group; Nature Portfolio
• Subjects: 631/553/117; 631/61/475; 631/67/589/466; 692/308; 82/16; 82/58; Biomarkers; Biomarkers - urine; Biopsy; Cancer research; Humanities and Social Sciences; Humans; Liquid Biopsy; Male; Mass Spectrometry; Medical prognosis; Medical research; Middle Aged; multidisciplinary; Patients; Peptides; Prostate - pathology; Prostate cancer; Prostatic Neoplasms - pathology; Prostatic Neoplasms - urine; Proteins; Proteome; Proteomics; Science; Science (multidisciplinary); Scientific imaging; Tumors; Urine; Canada; Toronto Ontario Canada; United States > US; Virginia
• DOI: 10.1038/ncomms11906
• ISSN: 2041-1723

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. Proteomic technologies are capable of identifying thousands of proteins in biological samples, but biomarker applications are lagging. Here the authors use Multiple Reaction Monitoring Mass Spectrometry to delineate peptide signatures that accurately distinguish between defined prostate cancer patient risk groups.