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Variance decomposition of protein profiles from antibody arrays using a longitudinal twin model.

ABSTRACT: BACKGROUND:The advent of affinity-based proteomics technologies for global protein profiling provides the prospect of finding new molecular biomarkers for common, multifactorial disorders. The molecular phenotypes obtained from studies on such platforms are driven by multiple sources, including genetic, environmental, and experimental components. In characterizing the contribution of different sources of variation to the measured phenotypes, the aim is to facilitate the design and interpretation of future biomedical studies employing exploratory and multiplexed technologies. Thus, biometrical genetic modelling of twin or other family data can be used to decompose the variation underlying a phenotype into biological and experimental components. RESULTS:Using antibody suspension bead arrays and antibodies from the Human Protein Atlas, we study unfractionated serum from a longitudinal study on 154 twins. In this study, we provide a detailed description of how the variation in a molecular phenotype in terms of protein profile can be decomposed into familial i.e. genetic and common environmental; individual environmental, short-term biological and experimental components. The results show that across 69 antibodies analyzed in the study, the median proportion of the total variation explained by familial sources is 12% (IQR 1-22%), and the median proportion of the total variation attributable to experimental sources is 63% (IQR 53-72%). CONCLUSION:The variability analysis of antibody arrays highlights the importance to consider variability components and their relative contributions when designing and evaluating studies for biomarker discoveries with exploratory, high-throughput and multiplexed methods.

SUBMITTER: Kato BS

PROVIDER: S-EPMC3247853 | biostudies-literature | 2011 Nov

REPOSITORIES: biostudies-literature

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Variance decomposition of protein profiles from antibody arrays using a longitudinal twin model.

Kato Bernet S BS Nicholson George G Neiman Maja M Rantalainen Mattias M Holmes Chris C CC Barrett Amy A Uhlén Mathias M Nilsson Peter P Spector Tim D TD Schwenk Jochen M JM

Proteome science 20111117

<h4>Background</h4>The advent of affinity-based proteomics technologies for global protein profiling provides the prospect of finding new molecular biomarkers for common, multifactorial disorders. The molecular phenotypes obtained from studies on such platforms are driven by multiple sources, including genetic, environmental, and experimental components. In characterizing the contribution of different sources of variation to the measured phenotypes, the aim is to facilitate the design and interp ...[more]

PMID: 22093360

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Project description:Sporadic Colorectal Cancer (sCRC) is the third leading cause of cancer death in the Western world, and the sCRC patients presenting with synchronic metastasis have the poorest prognosis. Genetic alterations accumulated in sCRC tumor cells translate into mutated proteins and/or abnormal protein expression levels, which contribute to the development of sCRC. Then, the tumor-associated proteins (TAAs) might induce the production of auto-antibodies (aAb) via humoral immune response. Here, Nucleic Acid Programmable Protein Arrays (NAPPArray) are employed to identify aAb in plasma samples from a set of 50 sCRC patients compared to seven healthy donors. Our goal was to establish a systematic workflow based on NAPPArray to define differential aAb profiles between healthy individuals and sCRC patients as well as between non-metastatic (n = 38) and metastatic (n = 12) sCRC, in order to gain insight into the role of the humoral immune system in controlling the development and progression of sCRC. Our results showed aAb profile based on 141 TAA including TAAs associated with biological cellular processes altered in genesis and progress of sCRC (e.g., FSCN1, VTI2 and RPS28) that discriminated healthy donors vs. sCRC patients. In addition, the potential capacity of discrimination (between non-metastatic vs. metastatic sCRC) of 7 TAAs (USP5, ML4, MARCKSL1, CKMT1B, HMOX2, VTI2, TP53) have been analyzed individually in an independent cohort of sCRC patients, where two of them (VTI2 and TP53) were validated (AUC ~75%). In turn, these findings provided novel insights into the immunome of sCRC, in combination with transcriptomics profiles and protein antigenicity characterizations, wich might lead to the identification of novel sCRC biomarkers that might be of clinical utility for early diagnosis of the tumor. These results explore the immunomic analysis as potent source for biomarkers with diagnostic and prognostic value in CRC. Additional prospective studies in larger series of patients are required to confirm the clinical utility of these novel sCRC immunomic biomarkers.

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Variance decomposition of protein profiles from antibody arrays using a longitudinal twin model.

Publications

Variance decomposition of protein profiles from antibody arrays using a longitudinal twin model.

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