TY - JOUR
T1 - Proteomic biomarker discovery in 1000 human plasma samples with mass spectrometry
AU - Cominetti, Ornella
AU - Núñez Galindo, Antonio
AU - Corthésy, John
AU - Oller Moreno, Sergio
AU - Irincheeva, Irina
AU - Valsesia, Armand
AU - Astrup, Arne
AU - Saris, Wim H M
AU - Hager, Jörg
AU - Kussmann, Martin
AU - Dayon, Loïc
N1 - CURIS 2016 NEXS 065
PY - 2016/2/5
Y1 - 2016/2/5
N2 - The overall impact of proteomics on clinical research and its translation has lagged behind expectations. One recognized caveat is the limited size (subject numbers) of (pre)clinical studies performed at the discovery stage, the findings of which fail to be replicated in larger verification/validation trials. Compromised study designs and insufficient statistical power are consequences of the to-date still limited capacity of mass spectrometry (MS)-based workflows to handle large numbers of samples in a realistic time frame, while delivering comprehensive proteome coverages. We developed a highly automated proteomic biomarker discovery workflow. Herein, we have applied this approach to analyze 1000 plasma samples from the multicentered human dietary intervention study "DiOGenes". Study design, sample randomization, tracking, and logistics were the foundations of our large-scale study. We checked the quality of the MS data and provided descriptive statistics. The data set was interrogated for proteins with most stable expression levels in that set of plasma samples. We evaluated standard clinical variables that typically impact forthcoming results and assessed body mass index-associated and gender-specific proteins at two time points. We demonstrate that analyzing a large number of human plasma samples for biomarker discovery with MS using isobaric tagging is feasible, providing robust and consistent biological results.
AB - The overall impact of proteomics on clinical research and its translation has lagged behind expectations. One recognized caveat is the limited size (subject numbers) of (pre)clinical studies performed at the discovery stage, the findings of which fail to be replicated in larger verification/validation trials. Compromised study designs and insufficient statistical power are consequences of the to-date still limited capacity of mass spectrometry (MS)-based workflows to handle large numbers of samples in a realistic time frame, while delivering comprehensive proteome coverages. We developed a highly automated proteomic biomarker discovery workflow. Herein, we have applied this approach to analyze 1000 plasma samples from the multicentered human dietary intervention study "DiOGenes". Study design, sample randomization, tracking, and logistics were the foundations of our large-scale study. We checked the quality of the MS data and provided descriptive statistics. The data set was interrogated for proteins with most stable expression levels in that set of plasma samples. We evaluated standard clinical variables that typically impact forthcoming results and assessed body mass index-associated and gender-specific proteins at two time points. We demonstrate that analyzing a large number of human plasma samples for biomarker discovery with MS using isobaric tagging is feasible, providing robust and consistent biological results.
U2 - 10.1021/acs.jproteome.5b00901
DO - 10.1021/acs.jproteome.5b00901
M3 - Journal article
C2 - 26620284
SN - 1535-3893
VL - 15
SP - 389
EP - 399
JO - Journal of Proteome Research
JF - Journal of Proteome Research
IS - 2
ER -