Discovery and Validation of Novel Protein Biomarkers in Ovarian Cancer Patient Urine. Issue 3 (2nd March 2018)

• Record Type: Journal Article
• Authors: Sandow, Jarrod J.; Rainczuk, Adam; Infusini, Giuseppe; Makanji, Ming; Bilandzic, Maree; Wilson, Amy L.; Fairweather, Nicole; Stanton, Peter G.; Garama, Daniel; Gough, Daniel; Jobling, Thomas W.; Webb, Andrew I.; Stephens, Andrew N.
• Is Part Of: Proteomics. Volume 12:Issue 3(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-03-02
• Subjects: biomarkers -- ovarian cancer -- urinary system; Proteomics -- Periodicals; 572.605
• DOI: 10.1002/prca.201700135
• Languages: English
• ISSNs: 1862-8346

Abstract

Abstract : Purpose: For the vast majority of ovarian cancer patients, optimal surgical debulking remains a key prognostic factor associated with improved survival. A standardized, biomarker‐based test, to preoperatively discriminate benign from malignant disease and inform appropriate patient triage, is highly desirable. However, no fit‐for‐purpose biomarkers have yet been identified. Experimental design: We conducted a pilot study consisting of 40 patient urine samples (20 from each group), using label‐free quantitative (LFQ) mass spectrometry, to identify potential biomarker candidates in urine from individual ovarian cancer patients. To validate these changes, we used parallel reaction monitoring (PRM) to investigate their abundance in an independent validation cohort ( n = 20) of patient urine samples. Results: LFQ analyses identified 4394 proteins (17 027 peptides) in a discovery set of 20 urine samples. Twenty‐three proteins were significantly elevated in the malignant patient group compared to patients with benign disease. Several proteins, including LYPD1, LYVE1, PTMA, and SCGB1A1 were confirmed to be enriched in the urine of ovarian cancer patients using PRM. We also identified the established ovarian cancer biomarkers WFDC2 (HE4) and mesothelin (MSLN), validating our approach. Conclusions and clinical relevance: This is the first application of a LFQ‐PRM workflow to identify and validate ovarian cancer–specific biomarkers in patient urine samples.