Cancer associated proteins in blood plasma: Determining normal variation. Issue 13 (2nd June 2016)

• Record Type: Journal Article
• Authors: Stenemo, Markus; Teleman, Johan; Sjöström, Martin; Grubb, Gabriel; Malmström, Erik; Malmström, Johan; Niméus, Emma
• Is Part Of: Proteomics. Volume 16:Issue 13(2016)
• Page Start: 1928
• Page End: 1937
• Publication Date: 2016-06-02
• Subjects: Biomarkers -- Biomedicine -- Blood proteome -- Cancer associated proteins -- Mass spectrometry -- Normal variation; Proteins -- Separation -- Periodicals; Bioinformatics -- Periodicals; Proteomics -- Periodicals; Genomes -- Periodicals; Molecular genetics -- Periodicals; 572.605
• DOI: 10.1002/pmic.201500204
• Languages: English
• ISSNs: 1615-9853

Abstract

Abstract : Protein biomarkers have the potential to improve diagnosis, stratification of patients into treatment cohorts, follow disease progression and treatment response. One distinct group of potential biomarkers comprises proteins which have been linked to cancer, known as cancer associated proteins (CAPs). We determined the normal variation of 86 CAPs in 72 individual plasma samples collected from ten individuals using SRM mass spectrometry. Samples were collected weekly during 5 weeks from ten volunteers and over one day at nine fixed time points from three volunteers. We determined the degree of the normal variation depending on interpersonal variation, variation due to time of day, and variation over weeks and observed that the variation dependent on the time of day appeared to be the most important. Subdivision of the proteins resulted in two predominant protein groups containing 21 proteins with relatively high variation in all three factors (day, week and individual), and 22 proteins with relatively low variation in all factors. We present a strategy for prioritizing biomarker candidates for future studies based on stratification over their normal variation and have made all data publicly available. Our findings can be used to improve selection of biomarker candidates in future studies and to determine which proteins are most suitable depending on study design.