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.
Highlights
Biomarkers are rapidly gaining importance in personalized medicine
Our previously generated discovery proteomic profiles from direct expressed prostatic secretions (EPSs) derived from extracapsular (EC) or OC prostate cancers laid the foundation for the current project[18]
Data mining of all peptides based on sequence and biophysical properties led to 232 proteotypic peptides suitable for evaluation by SRM-mass spectrometry (MS) in EPS urine samples (Fig. 1a)
Summary
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 are not useful for clinical application. Step-wise applications of these assays to two independent, richly annotated patient cohorts in conjunction with computational modelling identify liquid-biopsy signatures that accurately distinguish patients with organ-confined (OC) stage pT2 and extracapsular stage pT3 prostate cancers, before radical prostatectomy. These data highlight the value of readily accessible tissue proximal fluids and multiplexed quantitative proteomic signatures to identify extracapsular disease before invasive surgery, potentially modifying the treatment options for these patients
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