Harnessing the Power of the Immune System to Target Cancer

Metabolomics, the global interrogation of the biochemical components in a biological sample, has become an important complement to genomics and proteomics to aid in the understanding of pathophysiology. Major advantages of metabolomics are the size of the metabolome relative to the genome or proteome and the fact that it provides a view of the existing biochemical phenotype. As such, metabolomics is fast becoming an important discovery tool for new diagnostic and prognostic biomarkers. Although many methods exist for performing metabolomics, relatively few have led to successful development of new diagnostic tests. This review will aid the reader in understanding various metabolomic methods and their applications, as well as some of their inherent advantages and disadvantages. In addition, we present one example of the application of metabolomics to the identification of new fasting blood biomarkers for the diagnosis and monitoring of insulin resistance. 291 A nn u. R ev . M ed . 2 01 3. 64 :2 91 -3 05 . D ow nl oa de d fr om w w w . a nn ua lr ev ie w s. or g by D r. M ic ha el M ilb ur n on 0 1/ 18 /1 3. F or p er so na l u se o nl y. ME64CH21-Milburn ARI 12 December 2012 20:6 INTRODUCTION TO GLOBAL METABOLOMICS Over the past decade, “omics” technologies have become key to discovering novel biomarkers. Biomarkers can be nearly anything that distinguishes one individual from another. They can be based on a diagnostic test (e.g., cholesterol measurements), physical characteristic (e.g., body mass index), genetics (e.g., single nucleotide polymorphisms), or other distinguishing characteristics (e.g., diet). Discovery technologies such as transcriptomics (profiling mRNA), proteomics (profiling proteins), and others (e.g., genome wide association studies) start with an idea of the biomarker type needed to determine a condition, disease, or health risk. Unfortunately, general screening methods for biomarker discovery have been challenging, with few success stories. Biomarker validation is a relatively new and evolving concept. The challenges are mathematical, technological, and/or statistical (insufficient number of well-characterized samples). One technology with promise to overcome these issues is metabolomics, which profiles biochemicals and metabolites. This review introduces applications of metabolomics in the diagnosis of insulin resistance. The goal of metabolomics is to measure all of the small molecules (i.e., biochemicals, metabolites) in a sample. As such, it is grounded in biochemistry and provides a snapshot of the biochemical or metabolic status of the subject under investigation. The development of metabolomics as a tool to leverage the large body of available biochemical knowledge has been a major challenge. Most laboratories have focused on targeted metabolomic analysis, specializing in the measurement of 20–100 different metabolites which, most often, are within a common class of compounds. For example, a number of companies and academic labs have developed methods for detecting lipid compounds. Although lipids represent only a subset of the total biologically relevant metabolites, these data have proven useful for biomarker discovery efforts (1). Other groups have focused on methods to truly investigate all of the small molecules in samples. “Global” or “unbiased” metabolomics has been plagued by difficulties stemming from the diverse physical properties of small molecules. These properties can vary greatly, with significant differences in solubilities and with molecular weights ranging from 20 to >1,500 Da. It is therefore difficult to develop a single chromatography method to separate all of the compounds and even more difficult to analyze individual compounds without chromatographic separation. Further complications arise if studies are expected to be completed within a clinically useful turnaround time. These issues are currently being addressed through advanced multisystem approaches where the best separation and detection instrument technologies are developed to run in tandem. This approach allows for a comprehensive solution achieved by combining principles offered by various best-in-breed technologies. As this new technology develops and its use in biomarker detection studies increases, it is rapidly becoming clear that metabolomics will likely have a high impact in the diagnosis of disease, identification of drug targets, evaluation of the effects of drugs, and selection of patients most likely to respond to drug therapy (i.e., personalized medicine). To develop a viable technology platform that harnesses the power of metabolomics requires engineering a system of chromatographic separation coupled to mass spectrometry (2–4). One such metabolomics analysis process operates in essentially four steps (Figure 1). Step one is extraction of the small molecules from the biological sample. Step two is the chromatography coupled with mass spectrometry and data collection. Step three is the automated and manual quality control (QC) analysis of the data. Step four is the statistical and biological interpretation of the data. Numerous methods of analyzing a wide range of very polar to nonpolar compounds from as little as 50 μl of blood plasma have been published (5–7). For the case study 292 Milburn · Lawton A nn u. R ev . M ed . 2 01 3. 64 :2 91 -3 05 . D ow nl oa de d fr om w w w . a nn ua lr ev ie w s. or g by D r. M ic ha el M ilb ur n on 0 1/ 18 /1 3. F or p er so na l u se o nl y. ME64CH21-Milburn ARI 12 December 2012 20:6