Targeted proteomics identifies liquid-biopsy signatures for extracapsular prostate cancer

• DOI: 10.1038/ncomms11906
• Publication Date: 2016-06-28T10:49:06Z
• Authors: 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; Thomas Kislinger
• Keywords: Urologic Diseases; Male; Proteomics; Aging; Proteome; Science; Clinical Sciences; Oncology and Carcinogenesis; 610; Article; Mass Spectrometry; 576; 03 medical and health sciences; Clinical Research; Genetics; Humans; Cancer; screening and diagnosis; 0303 health sciences; Biomedical and Clinical Sciences; Prevention; Prostate Cancer; Human Genome; Q; Liquid Biopsy; Prostate; Prostatic Neoplasms; Middle Aged; 4.1 Discovery and preclinical testing of markers and technologies; 3. Good health; Detection; Good Health and Well Being; Biomarkers; Biotechnology; 4.2 Evaluation of markers and technologies

AbstractBiomarkers 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.