**1.4 The future: additional markers & rheumatology practice**

Several areas of medicine lack objective, quantitative measures of a disease and its response to therapy and the inflammatory forms of arthritis are no exception. Below, we define three distinct stages in the clinical progression to chronic inflammatory disease exemplified by some autoimmune disorders and outline where and how biomarkers could aid in managing the RA patient.


Validation of Protein Biomarkers to Advance the Management of Autoimmune Disorders 139

iii. *The disease management stage*: Early commencement of effective therapy is essential if joint damage and other complications are to be avoided. Historically, monitoring response to treatment is a composite of clinical findings and laboratory markers such as erythrocyte sedimentation rate (ESR), C-reactive protein (CRP) and disease activity score (e.g. DAS28) . Treatment is modified according to these parameters. However disease response can take many months, indeed years. Thus by the time the patients disease is deemed unresponsive substantial joint damage can have occurred. The identification of biomarkers that would predict disease response would have an enormous impact on outcome. Treatment may also be discontinued due poor tolerability. Identifying such patients in advance would improve patient care and reduce stress. Biologic drugs, as third line therapy such as anti-TNF have revolutionised the treatment of rheumatic diseases and systematic reviews have confirmed their efficacy and relative safety (Alonso-Ruiz et al. 2008). However these drugs are extremely expensive. Months of treatment can be required before the clinician knows whether they are effective. This is both costly and inefficient. The identification of biomarkers that would predict the response of individual patients to these expensive agents would help patients, clinicians and funding agencies alike. Finally there are concerns associated with the use of such targeted therapy i.e. the risk of life threatening infections using and more worryingly the long term risk of malignancy (Bongartz et al. 2006). The ability to identify such patients in advance would protect them from such

There is no dispute that new biomarkers would advance the diagnosis and management of autoimmune disorders. The ongoing challenge, however, is how to discover candidate markers and how to validate them, *i.e.,* define their performance characteristics when

A major impetus for increased interest in biomarkers has been the introduction of the omics technologies. In a single study these allow interrogation of hundreds (or thousands) of independent variables, such as genes, mRNA, metabolites or proteins and given the volume of information generated from such studies, many have anticipated candidate biomarkers would flow quickly from each new investigation. The reality, however, is otherwise. Comparing the levels of hundreds (or thousands) of data points in several distinct groups, especially when the sample numbers are small, gives rise to many apparent differences, only some of which are related to biology. Chance alone gives rise to many apparent "distinguishing features" - the trick is identifying the biologically relevant differences and

For example, consider a proteomics experiment in which 200 proteins are measured simultaneously in a control and a test sample. At P < 0.05, 10 proteins will appear to be different by chance alone and unrelated to the treatment or condition. This consideration is not presented to undermine the value or utility of omics research, but rather, to underscore the importance of verifying any observed differences in follow-up studies. In more conventional scientific studies it is typical to examine a single dependent variable, run

replicates, and to use standard statistical approaches to analyze the outcomes.

serious adverse reactions.

adopted in a routine clinical setting.

**2.1 Overview** 

ignoring the others.

**2. The proteomics biomarkers development pipeline** 


Table 1. Biomarkers routinely used in the diagnosis & treatment of arthritides and some potential future markers-arthritides.

iii. *The disease management stage*: Early commencement of effective therapy is essential if joint damage and other complications are to be avoided. Historically, monitoring response to treatment is a composite of clinical findings and laboratory markers such as erythrocyte sedimentation rate (ESR), C-reactive protein (CRP) and disease activity score (e.g. DAS28) . Treatment is modified according to these parameters. However disease response can take many months, indeed years. Thus by the time the patients disease is deemed unresponsive substantial joint damage can have occurred. The identification of biomarkers that would predict disease response would have an enormous impact on outcome. Treatment may also be discontinued due poor tolerability. Identifying such patients in advance would improve patient care and reduce stress. Biologic drugs, as third line therapy such as anti-TNF have revolutionised the treatment of rheumatic diseases and systematic reviews have confirmed their efficacy and relative safety (Alonso-Ruiz et al. 2008). However these drugs are extremely expensive. Months of treatment can be required before the clinician knows whether they are effective. This is both costly and inefficient. The identification of biomarkers that would predict the response of individual patients to these expensive agents would help patients, clinicians and funding agencies alike. Finally there are concerns associated with the use of such targeted therapy i.e. the risk of life threatening infections using and more worryingly the long term risk of malignancy (Bongartz et al. 2006). The ability to identify such patients in advance would protect them from such serious adverse reactions.

#### **2. The proteomics biomarkers development pipeline**

#### **2.1 Overview**

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

(Pepys & Hiirschfield, 2003).

juvenile idiopathic arthritis patients.

progression in RA patients (Meyer et al., 2003).

(Wittkowski et al., 2008).

(Kavanaugh et al., 2002).

severe disease (Wagner et al., 1997).

influencing TRAF1/C5 function.

Table 1. Biomarkers routinely used in the diagnosis & treatment of arthritides and some

et al., 2010).

*Creatinine* is used together with liver enzyme determinations (AST and ATL) as an index of drug toxicity and kidney function (Anders

A plasma protein routinely measured as a non-specific index of acute inflammation. CRP levels are typically integrated into clinical response scores, such as DAS28, which help to titer drug dosage

Phagocyte derived proteins found in a variety of inflammatory diseases , with the ability to discriminate between diseases

Used along with other tests, ANA helps with the diagnosis of arthritides. ANA titers and nuclear stain patterns can vary between patients depending on the condition (≈ 95% of systemic lupus erythrematosis (SLE)) (Wilk, 2005). Specific subsets of ANA's can be used to distinguish the type of autoimmune disease, *e.g.,* Sjögren's syndrome. ANA positivity increases the risk of eye disease in

RF antibodies target the Fc region of IgG and are detected in 60-80% of RA patients, though are present in other inflammatory and connective tissue diseases (Chen et al. , 1987).The presence of RF early in the course of RA is associated with more active disease (Nakamura, 2000).

Anti-CCP antibody positivity predicts the development of RA and may occur long before the onset of symptoms (Nielen, 2004). Anti-CCP is associated with severe erosive disease and can predict disease

Anti dsDNA antibodies are highly specific for the diagnosis of SLE with a specificity of 95% but with a low sensitivity of <60%

Human leukocyte antigen genes have been found to be associated with RA. This association is particularly strong for HLA-DRB-1 alleles which share a similar amino acid sequence known as the shared epitope (van der Horst-Bruinsma et al., 1999). The presence of these alleles both increases the risk of RA and associate with more

The protein tyrosine phosphatase, non-receptor 22 (PTPN22) allele is a major risk factor for several autoimmune diseases. The protein product increases the tyrosine dephosphorylation of T-cell receptor resulting in decreased signaling via this pathway (Vang et al., 2005).

TNF-receptor-associated factor-1 is one of many single nucleotide polymorphisms involved in the pathway of tumour necrosis factor alpha. The protein encoded by TRAF1 mediates signal transduction from the family of TNF receptors (Kurreeman et al., 2007). Increased susceptibility to and severity of RA is associated with this SNP, by

Pharmacogenomic tests which use cellular transcript measurements to predict drug response are yet to be implemented in the clinic. Interesting data has emerged on the association between anti-TNF antagonist response and genetic variation (Bowes et al., 2009; Potter

**Marker Molecular Class Example Application**

Protein- Autoantibody

Protein- Autoantibody

Protein- Autoantibody

Protein-Autoantibody

Nucleic acid-Gene

Nucleic Acid-Transcript

potential future markers-arthritides.

*PTPN22* Nucleic acid-Gene

*TRAF1/C5* Nucleic acid-Gene

et al., 2002).

*Creatinine* Metabolite

*protein (CRP)* Protein

*S100 proteins* Protein

*C-reactive* 

*Anti-nuclear antibody (ANA)*

*Rheumatoid Factor (RF)* 

*Anti-cyclic citrullinated peptide (CCP)* 

*Anti-Ds DNA antibodies* 

*HLA-DR4 or HLA-DRB* 

*TLR/ TNFR/ NF-κB* 

There is no dispute that new biomarkers would advance the diagnosis and management of autoimmune disorders. The ongoing challenge, however, is how to discover candidate markers and how to validate them, *i.e.,* define their performance characteristics when adopted in a routine clinical setting.

A major impetus for increased interest in biomarkers has been the introduction of the omics technologies. In a single study these allow interrogation of hundreds (or thousands) of independent variables, such as genes, mRNA, metabolites or proteins and given the volume of information generated from such studies, many have anticipated candidate biomarkers would flow quickly from each new investigation. The reality, however, is otherwise. Comparing the levels of hundreds (or thousands) of data points in several distinct groups, especially when the sample numbers are small, gives rise to many apparent differences, only some of which are related to biology. Chance alone gives rise to many apparent "distinguishing features" - the trick is identifying the biologically relevant differences and ignoring the others.

For example, consider a proteomics experiment in which 200 proteins are measured simultaneously in a control and a test sample. At P < 0.05, 10 proteins will appear to be different by chance alone and unrelated to the treatment or condition. This consideration is not presented to undermine the value or utility of omics research, but rather, to underscore the importance of verifying any observed differences in follow-up studies. In more conventional scientific studies it is typical to examine a single dependent variable, run replicates, and to use standard statistical approaches to analyze the outcomes.

Validation of Protein Biomarkers to Advance the Management of Autoimmune Disorders 141

differences may be non-specific and associated with the consequences of end-stage disease, rather than related to RA itself. Although widely used, for this reason alone the case-control study design is frequently problematic. Case and control samples need to be age- and lifestyle-matched, detailed clinical histories must be available on both cohorts, and strict inclusion/exclusion criteria are required. The value of any markers identified by this approach, and especially those that are not specific to autoimmune disorders alone, can only be assessed through large-scale hypothesis-driven studies performed in a defined clinical

*Prognostic or predictive biomarkers:* For a prognostic or predictive biomarker study, suitable samples must be available both before and after the measured outcome from each subject. Often these studies use samples collected from completed studies that addressed a different clinical question, but are then used to identify and track a molecular signature (biomarker profile) that may have predictive/prognostic value. (*i.e.,* They employ retrospective samples.) However, in all instances, prospective (purpose-driven) sample collection is preferred. Although logistically difficult, this approach affords greater control over preanalytical variables including storage time, storage conditions and use of additives. With foresight and planning, a randomized controlled trial with longitudinal sample collection can incorporate multiple nested outcome studies relating, for example, to therapeutic

It is important to state the obvious: a biomarker study can only be as good as the clinical samples and their associated records. If there are errors with annotation, if patient details are inaccurate, or if the samples themselves have not been collected and stored properly, the exercise of biomarker development may be futile (Poste, 2011). Although there are limited numbers of biobanks available at this time, thankfully, more and more investigators, research centers and commercial entities are committing to establishing and maintaining

With respect to samples, factors such as the timing from sample collection to freezer, the complexity and reproducibility of any sample handling steps, length of storage time, storage temperature and freeze-thaw cycles, may affect the stability of some analytes. For example, samples sourced from different cohorts at different locations may 'carry forward' preanalytical background signals with discriminating features unrelated to the biology of the disease (Addona et al., 2009; Davis et al., 2010; Ransohoff, 2010). Ideally, samples should be processed immediately, then aliquoted into airtight tubes and frozen in liquid nitrogen or - 70°C freezers. Similarly, multiple freeze-thaw cycles can affect the stability of potential biomarkers (Flower et al., 2000; Rai et al., 2005). Protease inhibition may help preserve sample integrity, but this approach is not without its complications. For example, irreversibly-binding to sample components can have undesirable downstream

Fluids or tissues proximal to the sites of pathology can act as biomarker sinks. As a result, these should also be considered, when available, alongside plasma or serum as a means of focusing the search on pathologically-relevant candidates. For example, in the case of arthritis, synovial fluid, cartilage and synovium are potential sources for biomarker discovery. Here protein variants unique to the site and pathology can be measured before

response, disease progression or disease recurrence.

high-quality sample repositories linked to accurate clinical records.

**2.3 Samples and sampling considerations** 

they escape into plasma (Gibson et al., 2009).

setting.

consequences.

Omics studies are very different. Data sets are typically of high dimensionality, but the sample size is small. There are typically very few if any replicates and any interesting trends are hidden within the combination the variables. Under these circumstances the probability of finding associations by random chance is high. Although multivariant statistics can help with the analysis of these complex data sets, there is no easy way out. Any single omics study is best considered as an observational investigation that aids in generating novel hypotheses that can direct their future studies. Contrary to what was hoped, the omics methods do not provide a fast-track to biomarkers or shortcut the scientific process. They do, however, allow an investigator to operate independent of existing knowledge and to be less dependent on insight, instinct or experience. A single omics study can provide data from which dozens of testable hypotheses can be formulated or, put another way, it can identify dozens of biomarker *candidates*. Accordingly, the validation of each candidate biomarker is analogous to hypothesis testing where the investigator sets out to falsify (or disprove) the claim that candidate "x" is a valid biomarker in defined clinical scenario "y".

In the sections that follow some of the other important components of the biomarker development pipeline are discussed and we highlight the primary concerns that are necessary to optimize the success of biomarker development.

#### **2.2 The clinical objective and study design considerations**

Several types of biomarkers can be developed, but in each instance the process requires a different study design and unique sample sets. Biomarker development in autoimmune disease must incorporate a cross-section of patients representing the full spectrum of the specific disorder, and given its complex and heterogeneous nature, a panel of markers, not a single marker, will likely be necessary to reflect all relevant clinical parameters. Depending on the groups incorporated into the study and the comparisons made, integrated panels of individual biomarkers may be identified that provide valuable screening/diagnostic, predictive or prognostic information.

*Screening biomarkers:* Biomarkers that can be used to screen and identify disease before the onset of any symptoms are the Holy Grail of autoimmune disease; however, their discovery and validation presents substantial challenges. While some of the biomarkers evident in symptomatic disease *may* be present early on in asymptomatic individuals, it is more likely that these will be low abundance and masked by more abundant, non-specific biomarkers of inflammatory and secondary processes related to the disease. Therefore, reliable identification of the biochemical events that earmark early stage, asymptomatic disease requires access to biobanks with adequate numbers of samples to give statistical power collected from affected individuals well before disease onset. (In the case of juvenile RA these samples should ideally be collected from birth onwards.) When samples are available from the same individual both before and after disease onset, patients can serve as their own controls and therefore changes characteristic of the disease can be measured against a relatively constant biochemical background. Sample sets for these studies are, however, difficult to come by and require substantial long-term logistical and financial investment. Consequently, a compromised study design incorporating a relevant control group and early stage (symptomatic) subjects is more commonly adopted to meet this objective.

*Diagnostic biomarkers:* Typically, a case-control approach is used in this setting, such that a cohort of disease-free controls is compared to a similarly-sized cohort of diseased subjects. Comparisons of this type require careful design and implementation because observed differences may be non-specific and associated with the consequences of end-stage disease, rather than related to RA itself. Although widely used, for this reason alone the case-control study design is frequently problematic. Case and control samples need to be age- and lifestyle-matched, detailed clinical histories must be available on both cohorts, and strict inclusion/exclusion criteria are required. The value of any markers identified by this approach, and especially those that are not specific to autoimmune disorders alone, can only be assessed through large-scale hypothesis-driven studies performed in a defined clinical setting.

*Prognostic or predictive biomarkers:* For a prognostic or predictive biomarker study, suitable samples must be available both before and after the measured outcome from each subject. Often these studies use samples collected from completed studies that addressed a different clinical question, but are then used to identify and track a molecular signature (biomarker profile) that may have predictive/prognostic value. (*i.e.,* They employ retrospective samples.) However, in all instances, prospective (purpose-driven) sample collection is preferred. Although logistically difficult, this approach affords greater control over preanalytical variables including storage time, storage conditions and use of additives. With foresight and planning, a randomized controlled trial with longitudinal sample collection can incorporate multiple nested outcome studies relating, for example, to therapeutic response, disease progression or disease recurrence.

#### **2.3 Samples and sampling considerations**

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

Omics studies are very different. Data sets are typically of high dimensionality, but the sample size is small. There are typically very few if any replicates and any interesting trends are hidden within the combination the variables. Under these circumstances the probability of finding associations by random chance is high. Although multivariant statistics can help with the analysis of these complex data sets, there is no easy way out. Any single omics study is best considered as an observational investigation that aids in generating novel hypotheses that can direct their future studies. Contrary to what was hoped, the omics methods do not provide a fast-track to biomarkers or shortcut the scientific process. They do, however, allow an investigator to operate independent of existing knowledge and to be less dependent on insight, instinct or experience. A single omics study can provide data from which dozens of testable hypotheses can be formulated or, put another way, it can identify dozens of biomarker *candidates*. Accordingly, the validation of each candidate biomarker is analogous to hypothesis testing where the investigator sets out to falsify (or disprove) the claim that candidate "x" is a valid biomarker in defined clinical scenario "y". In the sections that follow some of the other important components of the biomarker development pipeline are discussed and we highlight the primary concerns that are

Several types of biomarkers can be developed, but in each instance the process requires a different study design and unique sample sets. Biomarker development in autoimmune disease must incorporate a cross-section of patients representing the full spectrum of the specific disorder, and given its complex and heterogeneous nature, a panel of markers, not a single marker, will likely be necessary to reflect all relevant clinical parameters. Depending on the groups incorporated into the study and the comparisons made, integrated panels of individual biomarkers may be identified that provide valuable screening/diagnostic,

*Screening biomarkers:* Biomarkers that can be used to screen and identify disease before the onset of any symptoms are the Holy Grail of autoimmune disease; however, their discovery and validation presents substantial challenges. While some of the biomarkers evident in symptomatic disease *may* be present early on in asymptomatic individuals, it is more likely that these will be low abundance and masked by more abundant, non-specific biomarkers of inflammatory and secondary processes related to the disease. Therefore, reliable identification of the biochemical events that earmark early stage, asymptomatic disease requires access to biobanks with adequate numbers of samples to give statistical power collected from affected individuals well before disease onset. (In the case of juvenile RA these samples should ideally be collected from birth onwards.) When samples are available from the same individual both before and after disease onset, patients can serve as their own controls and therefore changes characteristic of the disease can be measured against a relatively constant biochemical background. Sample sets for these studies are, however, difficult to come by and require substantial long-term logistical and financial investment. Consequently, a compromised study design incorporating a relevant control group and

early stage (symptomatic) subjects is more commonly adopted to meet this objective.

*Diagnostic biomarkers:* Typically, a case-control approach is used in this setting, such that a cohort of disease-free controls is compared to a similarly-sized cohort of diseased subjects. Comparisons of this type require careful design and implementation because observed

necessary to optimize the success of biomarker development.

**2.2 The clinical objective and study design considerations** 

predictive or prognostic information.

It is important to state the obvious: a biomarker study can only be as good as the clinical samples and their associated records. If there are errors with annotation, if patient details are inaccurate, or if the samples themselves have not been collected and stored properly, the exercise of biomarker development may be futile (Poste, 2011). Although there are limited numbers of biobanks available at this time, thankfully, more and more investigators, research centers and commercial entities are committing to establishing and maintaining high-quality sample repositories linked to accurate clinical records.

With respect to samples, factors such as the timing from sample collection to freezer, the complexity and reproducibility of any sample handling steps, length of storage time, storage temperature and freeze-thaw cycles, may affect the stability of some analytes. For example, samples sourced from different cohorts at different locations may 'carry forward' preanalytical background signals with discriminating features unrelated to the biology of the disease (Addona et al., 2009; Davis et al., 2010; Ransohoff, 2010). Ideally, samples should be processed immediately, then aliquoted into airtight tubes and frozen in liquid nitrogen or - 70°C freezers. Similarly, multiple freeze-thaw cycles can affect the stability of potential biomarkers (Flower et al., 2000; Rai et al., 2005). Protease inhibition may help preserve sample integrity, but this approach is not without its complications. For example, irreversibly-binding to sample components can have undesirable downstream consequences.

Fluids or tissues proximal to the sites of pathology can act as biomarker sinks. As a result, these should also be considered, when available, alongside plasma or serum as a means of focusing the search on pathologically-relevant candidates. For example, in the case of arthritis, synovial fluid, cartilage and synovium are potential sources for biomarker discovery. Here protein variants unique to the site and pathology can be measured before they escape into plasma (Gibson et al., 2009).

Validation of Protein Biomarkers to Advance the Management of Autoimmune Disorders 143

size calculation) and the hunt for significant and reproducible patterns in the data. Their objective is to find reproducible differences which correlate with a defined clinical outcome and that are independent of the influence of experimental bias, over-fitting and statistical

The incorporation of a randomization strategy in sample analysis reduces bias by accounting for the day-to-day variations in the analytical technique. Similarly, it is prudent to calibrate and record the performance characteristics of the instruments used in the analyses. Calibration in proteomic analyses entails, for example in mass spectrometry, initialising the mass accuracy to a standard mixture of purified proteins or peptides of known mass. Routine calibration of sensitive instruments subject to 'drift' in measurement over a period of time should become part of good laboratory practice. Further, in the discovery phase, the objective is to have sufficient sample numbers to provide confidence that the list of protein candidates is worthy of follow-up during the validation phase. Typically in this phase of biomarker development, the sample size is small due to the cost and time of analysis, and sometimes because of the difficulty associated with obtaining samples. However, the number of proteins (independent variables) measured in each sample is typically very large. This ratio of samples to variable size is contrary to the traditional application of multivariate statistics and leads to some unique considerations that have been discussed by others (Dowsey et al., 2009; Karp & Lilley, 2007). Conversely in the validation phase this relationship is inverted so that patient cohorts are much larger (typically 100's -1000's) and the number of biomarker candidates carried over from discovery are reduced depending on the strength of their relationship to the clinical outcome or measure being assessed. The costs incurred by the validation phase therefore sit in a multi-million dollar range far exceeding the costs of discovery. The financial implications alone may account for the relative dearth of publications on this phase as the main players, large pharmaceutical or diagnostic corporations, having invested large amounts of time and money likely strive to protect the resultant intellectual property prior

Bias, or any discrimination occurring due to a non-biological signal, can potentially confound discovery. For example, spurious results may arise because of differences in how patient samples are collected, *e.g.,* type of blood collection tube, time taken to freeze sample, or the order in which the samples are analyzed. Over-fitting can occur when regression analysis tools are used to 'fit' (too) many variables to a limited set of outcomes. The discriminating 'pattern' or 'signature' then becomes an artifact of the patient cohorts. To resolve issues of bias, statistical analyses must consider the biology of the system being analyzed and take into account the assumptions and limitations of the methods (Ransohoff, 2009). Statistical tests capable of gauging the level of false positives across multiple comparisons include the student's unpaired t-test (for two group comparisons), ANOVA (for three or more group comparisons) and linear regression (for quantitative or correlative studies) (Dowsey et al., 2009). Alternatively, if the data are not normally distributed, nonparametric Mann Whitney and Kruskal-Wallis tests should be substituted (Karp & Lilley, 2007). These methods can be used to analyze the features one-at-a-time and then to compile a ranked list of them based on a combination of p values and effect size. As noted earlier, a longitudinal design can minimize the potential for bias relative to a typical case-control study. Nevertheless, false biomarker leads are common and therefore rigorous validation

to further clinical testing and pre-market approval.

chance.

essential.

Studies indicate that plasma is likely a better substrate for proteome analysis than serum due to the obfuscation of results associated with the high proportion (>40%) of clot-related proteins and peptides in serum (Haab et al., 2005; Rai et al., 2005; Tammen, 2005). Less invasive samples also amenable to protein biomarker discovery include urine, saliva and tear fluid. Although putative biomarkers can come from discovery work, candidates can also come from literature searches and genomic or transcriptomic mining. All candidates, however, must undergo subsequent verification and validation (Pepe et al., 2008).
