**1.3 Biomarkers currently used in the practice of rheumatology**

136 Autoimmune Disorders – Current Concepts and Advances from Bedside to Mechanistic Insights

utility. These are distinct from '*clinical markers*' which rely on physical variables or symptoms such as joint count, pain assessment or radiological findings. Biomarker examples include: cardiovascular risk assessment through cholesterol checks; pituitary and target gland hormone determinations to assess endocrine function and dysfunction; hemoglobin A1c (HbA1c) evaluations to monitor blood glucose levels in diabetic patients; liver function tests (LFT) in liver disease; and, prostate-specific antigen (PSA) determinations to assess prostate cancer risk. Not surprisingly, there is considerable interest in developing additional clinical biomarkers in medicine; however, the path from their

Although genomic and transcriptomic methods are powerful, they cannot predict downstream events. Specifically, they can't predict what protein forms will be expressed in a particular tissue or biofluid, nor can they reliably estimate expressed protein levels. Because it is the gene products that contribute directly to physiological or pathological change, they alone provide the best clues to function in health and dysfunction in disease. Just as importantly, protein modifications including a plethora of post-translational changes are not evident upstream. As discussed later, proteins may require cleavage of a specific sequence to become biologically active, additional sidegroups may be added at specific amino acids including phosphorylation to propagate signal transduction or glycosylation to transport the molecule to a specific site. The analysis of hundreds if not thousands of proteins – proteomics – is therefore potentially the most illuminating of all multiplexed

Proteomics, however, is an imperfect science, and although its methods are rapidly evolving, it is important to acknowledge that all existing approaches have serious limitations. Notably, the available discovery tools offer poor precision, sensitivity, specificity and low throughput. An example of a low throughput proteomic platform is classic liquid chromatography separation of a single complex biological sample, coupled with electrospray ionization to generate of mass spectra. These limitations place severe constrains on study design (*e.g.,* small sample numbers) and they can lead investigators to place a disproportionate confidence in the data generated in a discovery setting. Regardless of these shortcomings, proteomic methods have been widely adopted and have generated many potential candidate markers. As we will illustrate, candidates identified using these techniques should be considered a set of ideas or leads from which the investigator can generate testable hypotheses. The study design and process of testing or validating these

candidates is especially important if the test is to be applied in a clinical setting.

This chapter focuses on several key areas of the biomarker development process and uses rheumatoid arthritis as a case model of autoimmune disease for discussion. We first discuss the issue of clinical need, *i.e.,* how biomarkers *are currently* used in the practice of rheumatology what biomarkers might offer in *the future* in a clinical setting, then highlight some clinical scenarios describing considerations for study design. Because discovery methods have been reviewed elsewhere (Gibson & Rooney, 2007; Hu et al., 2006; Tilleman et al., 2005) our emphasis here is on describing and evaluating the targeted proteomic methods that are essential to candidate validation. It is the authors' belief that the task of validation has not received adequate attention. We are overwhelmed with discovery studies and data arising from them; validation approaches are infrequently reviewed and relatively little

discovery to routine adoption is painstakingly complex and slow.

**1.2 Why target protein biomarkers** 

strategies.

The biomarkers currently used in the practice of rheumatology can be subdivided into three distinct molecular classes: nucleic acid, proteins, and metabolites. Some of these tests and their applications are included in Table 1. This is not an exhaustive list and while an invaluable armamentarium, it is limited in number, clinical utility and performance. With the exception of anti-CCP autoantibody and S100 proteins, the list has remained essentially static for the last 5-10 years. Additional potential biomarkers have been reported, but few, if any, have been adopted in routine practice. There is, however, no dispute that better tools are urgently required for both objective diagnosis and optimal management of rheumatoid arthritis.
