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Comparison of Proteomic Assessment Methods in Multiple Cohort Studies.

Citation
Raffield, L. M., et al. “Comparison Of Proteomic Assessment Methods In Multiple Cohort Studies.”. Proteomics, p. e1900278.
Center UCSD-UCLA
Author Laura M Raffield, Hong Dang, Katherine A Pratte, Sean Jacobson, Lucas A Gillenwater, Elizabeth Ampleford, Igor Barjaktarevic, Patricia Basta, Clary B Clish, Alejandro P Comellas, Elaine Cornell, Jeffrey L Curtis, Claire Doerschuk, Peter Durda, Claire Emson, Christine M Freeman, Xiuqing Guo, Annette T Hastie, Gregory A Hawkins, Julio Herrera, Craig Johnson, Wassim W Labaki, Yongmei Liu, Brett Masters, Michael Miller, Victor E Ortega, George Papanicolaou, Stephen Peters, Kent D Taylor, Stephen S Rich, Jerome I Rotter, Paul Auer, Alex P Reiner, Russell P Tracy, Debby Ngo, Robert E Gerszten, Wanda K O'Neal, Russell P Bowler, NHLBI Trans-Omics for Precision Medicine Consortium
Keywords antibody microarrays, biomarkers, multiplexings
Abstract

Novel proteomics platforms, such as the aptamer-based SOMAscan platform, can quantify large numbers of proteins efficiently and cost-effectively and are rapidly growing in popularity. However, comparisons to conventional immunoassays remain underexplored, leaving investigators unsure when cross-assay comparisons are appropriate. The correlation of results from immunoassays with relative protein quantification is explored by SOMAscan. For 63 proteins assessed in two chronic obstructive pulmonary disease (COPD) cohorts, subpopulations and intermediate outcome measures in COPD Study (SPIROMICS), and COPDGene, using myriad rules based medicine multiplex immunoassays and SOMAscan, Spearman correlation coefficients range from -0.13 to 0.97, with a median correlation coefficient of ≈0.5 and consistent results across cohorts. A similar range is observed for immunoassays in the population-based Multi-Ethnic Study of Atherosclerosis and for other assays in COPDGene and SPIROMICS. Comparisons of relative quantification from the antibody-based Olink platform and SOMAscan in a small cohort of myocardial infarction patients also show a wide correlation range. Finally, cis pQTL data, mass spectrometry aptamer confirmation, and other publicly available data are integrated to assess relationships with observed correlations. Correlation between proteomics assays shows a wide range and should be carefully considered when comparing and meta-analyzing proteomics data across assays and studies.

Year of Publication
2020
Journal
Proteomics
Volume
20
Issue
12
Number of Pages
e1900278
Date Published
12/2020
ISSN Number
1615-9861
DOI
10.1002/pmic.201900278
Alternate Journal
Proteomics
PMID
32386347
PMCID
PMC7425176
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