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Review
. 2013 Aug 27;1(2):109-127.
doi: 10.3390/proteomes1020109.

Proteomic Workflows for Biomarker Identification Using Mass Spectrometry - Technical and Statistical Considerations during Initial Discovery

Dennis J Orton  1 , Alan A Doucette  2
Affiliations
Review

Proteomic Workflows for Biomarker Identification Using Mass Spectrometry - Technical and Statistical Considerations during Initial Discovery

Dennis J Orton et al. Proteomes. .

Abstract

Identification of biomarkers capable of differentiating between pathophysiological states of an individual is a laudable goal in the field of proteomics. Protein biomarker discovery generally employs high throughput sample characterization by mass spectrometry (MS), being capable of identifying and quantifying thousands of proteins per sample. While MS-based technologies have rapidly matured, the identification of truly informative biomarkers remains elusive, with only a handful of clinically applicable tests stemming from proteomic workflows. This underlying lack of progress is attributed in large part to erroneous experimental design, biased sample handling, as well as improper statistical analysis of the resulting data. This review will discuss in detail the importance of experimental design and provide some insight into the overall workflow required for biomarker identification experiments. Proper balance between the degree of biological vs. technical replication is required for confident biomarker identification.

Keywords: biomarker discovery; experimental design; high dimensional data; randomization; replication.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
(Top) The number of PubMed search results as a function of year; (Bottom) The growth in number of publications corresponds directly to the application of numerous technologies and methods [9,10,11,12,13,14,15,16] used to improve throughput and sensitivity.
Figure 2
Figure 2
A schematic representation of potential sources of biomarkers. Less complex model systems provide a simpler starting point for biomarker investigation; however, the clinical utility of the analysis improves by transitioning to more complex model systems.
Figure 3
Figure 3
The influence of randomization on quantitative analysis of potential biomarkers. In the figure, successive MS runs were assumed to contribute a 5% decrease in signal intensity. A randomized design allowed proper characterization of the true biomarker while avoiding improper characterization of the false biomarker. In a biased design, the samples were analyzed in an improper grouping, which led to an apparent difference in the observed concentration of the false biomarker.
Figure 4
Figure 4
Increasing the level of fractionation greatly improves the number of proteins identified, though longer analysis time is required. Conversely, biomarker experiments require analysis of larger sample sizes to improve the biological significance of the identified proteins. In the discovery phase, biomarker experimentation must find a balance between these extremes. Pooling samples is one method to reduce the analysis time, however will also limit biological relevance.
See this image and copyright information in PMC

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