TABLE 2-1 Examples of Biomarker Categories and High-Throughput Methods of Discovery

Biomarker Category

Examples of Methods

Genomics

 

DNA-based

 

Copy number/loss of heterozygosity

Various DNA arrays

Sequence variation

Various sequencing methods

Epigenetic variation

 

Genome rearrangements

 

RNA-based

 

mRNA signatures

Various DNA arrays

miRNA signatures

 

Proteomics

Mass spectrometry

Proteins

Liquid chromatogrpahy

Peptides

Protein arrays

Metabolomics

 

Metabolites

Mass spectrometry

Lipids

Liquid chromatography

Carbohydrates

Nuclear magnetic resonance

SOURCE: Derived from IOM, 2006a.

Analysis of these large datasets requires sophisticated algorithms and bioinformatics to identify individual markers of interest or to derive signatures or patterns of many markers (reviewed by Cristoni and Bernardi, 2004; Englbrecht and Facius, 2005; Tinker et al., 2006). Although these methods are continually evolving and being improved, there is still a great need for novel approaches to data analysis, especially with regard to network oriented models that can incorporate many different types of data to fully integrate the vast complexity of biology in health and disease. However, identifying biomarker patterns or specific changes in genes or the products of gene expression in tumors is only the beginning of the process to develop cancer biomarkers.

Before a candidate biomarker can be put into use, it must undergo several stages of confirmation, validation, and qualification for use (Wagner, 2002; Feng et al., 2004; Ransohoff, 2004, 2005; Simon, 2005; De Bortoli and Biglia, 2006). Analytical validation is the process of assessing the assay or measurement performance characteristics, while qualification is the evidentiary process of linking a biomarker with the biology and clinical end-



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