Systemic lupus erythematosus (SLE) is a chronic autoimmune disease associated with diverse clinical manifestations. Accurate diagnosis, prediction of disease activity, organ involvement and management remains problematic owing to a lack of reliable biomarkers. This article reviews traditional and a few promising candidate biomarkers in SLE with specific clinical implications.
by Dr Anne E. Tebo
Background
Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by heterogeneity in disease manifestations as well as diversity in immunologic and therapeutic responses. Despite longstanding research efforts, precise diagnosis and prediction of response to treatment remain problematic owing to variable disease presentation and course as well as a lack of sensitive and specific biomarkers [1, 2]. Several factors contribute to the immune abnormality and clinical heterogeneity that occurs in SLE; these include genetic, epigenetic, environmental, and hormonal influences. The interplay between these elements drives the production of a variety of autoantibodies, complement products, inflammatory markers and other mediators identified as biomarkers to diagnose, monitor, stratify and/or predict disease risk, course or response to treatment [1–7]. These biomarkers – genetic, biologic, biochemical or molecular – may correlate with disease pathogenesis or specific clinical manifestations and can be evaluated qualitatively or quantitatively in laboratories. Notable amongst these are diverse autoantibodies and complement products which (in addition to clinical manifestations such as skin lesions, arthritis, renal disorder, hematologic changes, neurologic disorder amongst others) are traditionally considered hallmarks of disease [8, 9]. This review article highlights traditional and a few promising candidate serologic, cellular and urine biomarkers for diagnosing and predicting disease activity as well as renal involvement in SLE.
Biomarkers for the diagnosis of SLE
The updated American College of Rheumatology (ACR) revised criteria for the classification of SLE [8] is largely used in clinical practice to diagnose patients. In addition to specific clinical manifestations, the guidelines recommend testing for antinuclear antibodies (ANA), anti-double stranded deoxyribonucleic acid (anti-dsDNA), anti-Smith (anti-Sm) and antiphospholipid antibodies (lupus anticoagulant, IgG and IgM antibodies to cardiolipin (aCL) and beta2 glycoprotein I (anti-β2GPI). Recently, the Systemic Lupus Collaborating Clinics proposed the SLICC criteria for SLE in view of recent knowledge of the immunology of SLE [9]. Based on the SLICC rule for the classification of SLE, a patient must satisfy at least four criteria, including at least one clinical criterion and one immunologic criterion or must have biopsy-proven lupus nephritis (LN) in the presence of ANAs or anti-dsDNA antibodies. SLE is also associated with a variety of extractable nuclear antibodies such as anti-SSA, anti-SSB, and anti-snRNP as well as anti-ribosomal P, anti-histone, anti-nucleosome, anti-PCNA and anti-C1q autoantibodies [3, 5, 6]. The diagnostic characteristics of these autoantibodies (especially the anti-dsDNA antibodies) have been reported to be variable, which may be attributable the diversity of analytical methods, target antigens, patient demographics and SLE clinical subsets investigated [3, 5, 6, 11].
In addition to specific autoantibody tests, the proposed SLICC criteria recommend testing for complements C3, C4 and CH50 as well as using the direct Coombs assay. In the past several years, serum C3, C4 and CH50 levels have traditionally been used to diagnose and monitor disease activity in SLE patients [reviewed in 1, 2, 4]. However, in vitro activation may compromise interpretation of results and serum complement levels do not differentiate between consumption and production, which may be important for diagnosis. There is some evidence that cell-bound complement activation products (CB-CAPs) may facilitate SLE diagnosis [4, 12, 13]. These include complement C4-derived ligand deposited on erythrocytes (EC4d), platelets (PC4d), B lymphocytes (BC4d) and reticulocytes as detected by flow cytometry. Compared to disease controls, there is a relative increase of cell-bound C4d (CB-C4d) in SLE. However, the actual relevance of a single CB-C4d assay to the diagnosis of SLE is thought to be unlikely, although a panel of EC4d and BC4d assays is proposed to be predictive of SLE [12]. Further studies in diverse SLE clinical subsets and populations are required to determine the optimal CB-C4d panels for diagnostic evaluation.
Biomarkers for assessing disease activity
There are currently no consensus measures or biomarkers to reliably evaluate disease activity, predict flares and their differentiation from permanent damage in SLE patients. Disease activity indices such as the SLE disease activity index 2000 (SLEDAI-2K), British Isles Lupus Assessment Group (BILAG 2004), and the Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index (SDI) are all complex and mostly used in academic centres and/or in clinical trials [reviewed in 14]. In addition to routine serologic markers for inflammation, anti-dsDNA antibodies, C3, and C4 have been traditionally used to assess disease activity and predict flares in SLE (Table 1). For patients with LN, urine analysis for protein, sediment, protein-creatinine ratio and albumin are used to evaluate activity, monitor treatment response and predict relapse. Several studies have examined the associations between traditional and non-traditional biomarkers to determine optimal panel of markers associated with disease activity or predicting severity [reviewed in 1–7]. The outcomes in these investigations have been inconsistent, probably due to variability in detection methods and heterogeneity in patient populations. These inconsistencies have hampered the adoption of an acceptable biomarker panel to evaluate and monitor disease activity.
A number of promising candidate biomarkers associated with disease activity in SLE have been identified and are reviewed in other publications [1, 2, 4–7]. These include biomarkers for serologic (autoantibodies, cytokines and cytokine receptors, markers of endothelial cell activation, and soluble cell surface molecules), cellular (cell-bound C4d, CD27high plasma cells and other lymphocyte subsets) and urine [neutrophil gelatinase-associated lipocalin (NGAL), sVCAM-1 (soluble vascular cell adhesion molecule 1), MCP-1 (monocyte chemotactic protein 1), and TWEAK (tumor necrosis factor-like weak inducer of apoptosis), immunoglobulin free light chain, von Willebrand factor, IL-6] analyses. Of the several autoantibodies described, anti-nucleosome and anti-C1q antibodies appear to significantly correlate with disease activity and/or predict flares [2, 4, 6]. Chromatin, the DNA-histone complex found in the nucleus is organized into a repeating series of nucleosomes. In SLE patients, anti-nucleosome antibodies are more likely to be detected in patients with LN and may serve as a useful biomarker in the diagnosis of active LN [reviewed in 2]. Anti-C1q IgG antibodies that target epitopes present within the collagen-like tail of C1q are also seen in varying prevalence in SLE patients. Increasing titres in anti-C1q antibodies have been suggested to predict renal flares [2, 4, 6]. Other serologic biomarkers such as cytokines (IFN-α/β, IFN-γ, IL-1, IL-6, IL-10, IL-12, IL-15, IL-17, IL-21, and TNF-α), IFN-inducible chemokines, a few cytokine receptors, complements, specific markers of endothelial cell activation, BLys and several others associated with SLE pathogenesis and disease activity have been described [1, 2, 4]. However, these have mostly been reported in research studies and are beyond the scope of this article.
Quite a number of studies have shown that elevated levels of complement activation cleavage products may reflect disease activity more accurately and are more likely than conventional measurements in the prediction disease flares. Of these, the best described is the Cd4 fragment, which is capable of binding several cell types. In one study, EC4d levels were observed to be higher in patients with ‘more active’ and ‘most active’ SLE compared with those with ‘less active’ disease [13]. EC4d measurements were also found to be associated with specific measures of disease activity even after adjusting for serum levels of C3, C4 and anti-dsDNA antibodies [13]. However, independent prospective investigations with appropriate controls are needed to validate these observations.
Biomarkers for renal involvement
Of the different clinical subsets of SLE, LN is one of the most common and associated with significant morbidity and mortality. In the US, approximately 35% of adults with SLE have clinical evidence of nephritis at the time of diagnosis, with an estimated 50–60% developing nephritis during the first 10 years of disease [reviewed in 15]. Among these patients, quite a few will progress to end-stage renal disease. Improved methods for detecting LN would allow earlier treatment preventing irreversible impairment of renal function and damage. In place of invasive, subjective and costly serial renal biopsies, tests such as creatinine clearance, levels of urine protein and sediment as well as serologic determinations of C3, C4, creatinine level and anti-dsDNA titres have for decades been used to follow the onset, course, and severity of LN. It is however, recognized that these analyses are inadequate as they are limited in responsiveness to change and therefore unsuitable for patient care [7, 15].
In an effort to reliably diagnose LN, several candidate biomarkers have identified [1, 2, 7]. Among autoantibodies, antichromatin/anti-nucleosome and anti-C1q antibodies have shown some promise as biomarkers of renal involvement as previously described in this article. Of the urine protein biomarkers NGAL, sVCAM-1, MCP-1 and TWEAK, amongst others, have received considerable attention (Table 1) [1, 2, 7]. Of these, NGAL has been much studied. It is a small protein expressed in the neutrophils and certain epithelial cells, including the renal tubules. Under normal physiologic conditions, NGAL expressions are low in urine and plasma, but quickly rise from basal concentrations in response to kidney injury to reach diagnostic thresholds within a very short period of time. This is in contrast to the routinely used kidney function tests such as creatinine, where increased concentrations may not be observed until 24 to 48 hours after injury and often lack sensitivity. Urine NGAL is, however, not specific for SLE and further studies are necessary to establish accurate reference ranges based on age, gender and ethnicity. Like NGAL, urine sVCAM-1, sICAM-1 (soluble intercellular CAM-1) and MCP-1 have been shown in human studies to be strongly correlated with LN activity and severity [reviewed in 7]. With the identification of these novel urinary biomarkers for diagnosing and differentiating active versus inactive LN, several studies have examined their relationship with histological features of LN. Brunner et al. 2012 examined a number of established markers (anti-dsDNA, serum C3, C4, creatinine, urinary protein : creatinine ratio, etc.) and a few candidate urinary biomarkers [MCP-1, NGAL, lipocalin-type prostaglandin D-synthetase (L-PGDS), α1-acid-blycoprotein (AAG/AGP), transferrin (TF), and ceruloplasmin (CP)] in urine samples from 76 SLE patients collected within 2 months of kidney biopsy [reviewed in 7]. These urinary biomarkers were compared with histopathologic features of the kidney biopsy such mesangial expansion, capillary proliferation, crescent formation, wire loops, or fibrosis. Overall, their results indicated that levels of specific urinary biomarkers were increased in active LN and appeared to correlate with distinctive histologic features in renal biopsies. Furthermore, based on the presence of defined urine proteins, the authors could predict specific LN signatures. LN activity signature was defined by a combination of urinary MCP-1, AAG, and CP levels and protein : creatinine ratio while LN chronicity was characterized by NGAL, MCP-1 and creatinine clearance. The combined tests of MCP-1, AAG, TF, creatinine clearance and serum C4 was indicative of a potential biomarker panel for membranous nephritis. However, like NGAL, increased expression of these urine biomarkers is not exclusive to LN. Future studies are likely to highlight the relevance of specific urine biomarker proteins in predicting renal involvement in SLE.
Conclusion
There is considerable evidence that no single biomarker will be sufficient to diagnose, monitor and stratify all patients with SLE. Ideally, new biomarkers should provide information not available from traditional tests. Recent efforts geared towards the discovery and validation of biomarker ‘panels’ or ‘signatures’ of SLE represent an informed approach. Validation studies with endpoints that ensure a true measure of the intended clinical process in diverse cohorts coupled with robust analytical assays and high statistical power to confirm these panels are needed.
References
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2. Liu CC, Kao AH, Manzi S, Ahearn JM. Biomarkers in systemic lupus erythematosus: challenges and prospects for the future. Ther Adv Musculoskelet Dis. 2013; 5: 210–233.
3. To CH, Petri M. Is antibody clustering predictive of clinical subsets and damage in systemic lupus erythematosus? Arthritis Rheum. 2005; 52: 4003–4010.
4. Leffler J, Bengtsson AA, Blom AM. The complement system in systemic lupus erythematosus: an update. Ann Rheum Dis. 2014; 73: 1601–1606.
5. Jeltsch-David H, Muller S. Neuropsychiatric systemic lupus erythematosus: pathogenesis and biomarkers. Nat Rev Neurol. 2014; 10(10): 579–596.
6. Cozzani E, Drosera M, Gasparini G, Parodi A. Serology of lupus erythematosus: correlation between immunopathological features and clinical aspects. Autoimmune Dis. 2014; 2014: 321359.
7. Bennett M, Brunner HI. Biomarkers and updates on pediatrics lupus nephritis. Rheum Dis Clin North Am. 2013; 39: 833–853.
8. Hochberg MC. Updating the American College of Rheumatology revised criteria for the classification of systemic lupus erythematosus. Arthritis Rheum. 1997; 40: 1725.
9. Petri M, Orbai AM, Alarcón GS, et al. Derivation and validation of the Systemic Lupus International Collaborating Clinics classification criteria for systemic lupus erythematosus. Arthritis Rheum. 2012; 64: 2677–2686.
10. Yu C, Gershwin ME, Chang C. Diagnostic criteria for systemic lupus erythematosus: a critical review. J Autoimmun. 2014; 48–49: 10–13.
11. Isenberg DA, Manson JJ, Ehrenstein MR, Rahman A. Fifty years of anti-ds DNA antibodies: are we approaching journey’s end? Rheumatology (Oxford) 2007; 46: 1052–1056.
12. Kalunian KC, Chatham WW, Massarotti EM, Reyes-Thomas J, Harris C, Furie RA, Chitkara P, Putterman C, Gross RL, Somers EC, Kirou KA, Ramsey-Goldman R, Hsieh C, Buyon JP, Dervieux T, Weinstein A. Measurement of cell-bound complement activation products enhances diagnostic performance in systemic lupus erythematosus. Arthritis Rheum. 2012; 64: 4040–4047.
13. Kao AH, Navratil JS, Ruffing MJ, Liu CC, Hawkins D, McKinnon KM, Danchenko N, Ahearn JM, Manzi S. Erythrocyte C3d and C4d for monitoring disease activity in systemic lupus erythematosus. Arthritis Rheum. 2010; 62: 837–844.
14. Romero-Diaz J, Isenberg D, Ramsey-Goldman R. Measures of adult systemic lupus erythematosus: updated version of British Isles Lupus Assessment Group (BILAG 2004), European Consensus Lupus Activity Measurements (ECLAM), Systemic Lupus Activity Measure, Revised (SLAM-R), Systemic Lupus Activity Questionnaire for Population Studies (SLAQ), Systemic Lupus Erythematosus Disease Activity Index 2000 (SLEDAI-2K), and Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index (SDI). Arthritis Care Res. (Hoboken). 2011; 63(Suppl 11): S37–46.
15. Hahn BH, McMahon MA. American College of Rheumatology guidelines for screening, treatment, and management of lupus nephritis. Arthritis Care Res. (Hoboken). 2012; 64: 797–808.
The author
Anne E. Tebo1,2 PhD
1Department of Pathology, University of Utah School of Medicine, Salt Lake City, UT 84132, USA
2ARUP Institute for Clinical and Experimental Pathology, Salt Lake City, UT 84108, USA
E-mail: anne.tebo@hsc.utah.edu;
anne.tebo@aruplab.com
CYSTANIN C reagent set
, /in Featured Articles /by 3wmediaTwo-in-one urinary antigen test
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, /in Featured Articles /by 3wmediaThe clinical lab and pharmacogenomics – bridging the last mile
, /in Featured Articles /by 3wmediaPharmacogenomics analyses the response of individual patients to a medicinal product, to optimize therapy, obtain maximum efficacy and minimize its side effects.
Pharmacogenomics is now increasingly accepted to encompass pharmacogenetics, which focuses only on heritable biomarkers. Unlike the latter, pharmacogenomics also includes the study of proteins and enzymes as biomarkers.
The proponents of pharmacogenomics believe it holds the key to personalized medicine, in which drugs are tailored to a patient’s unique genetic profile.
Roots of pharmacogenomics in Human Genome Project
Pharmacogenomics is among the first clinical applications of the ambitious Human Genome Project, which was completed in 2003. It has already begun making an impact on clinical medicine, and promises much more as new pharmacogenomic biomarkers are identified by increasingly versatile techniques such as single nucleotide polymorphisms (SNP), small nuclear (sn) RNA-mediation and others.
ADRs are research priority
Pharmacogenomic biomarkers are essentially DNA or RNA characteristics that measure normal biologic and pathogenic processes, as well as the pharmacologic response to drug intervention.
The highest priority of pharmacogenomic research is to identify biomarkers for adverse drug reactions (ADRs), which account for one-fifth of all readmissions to hospital and 4% of withdrawal of new medicines.
ADRs are among the leading causes of death, with as many as 100,000 deaths a year in the US.
Pharmacogenomic labelling of drugs
There already are over 120 drugs in the US which include pharmacogenomic biomarkers in their labels. In Europe, the number is smaller, about 35. One reason is that the European Medicines Agency, the pan-EU regulator, has limited authority in the area. This is because of the large number of drugs which have been approved by Member States (rather than the Agency), with updating of the drug label seen as their responsibility. However, “relabelling to include pharmacogenomic data does not seem to be a priority issue” for the regulatory agencies in individual EU Member States.
Pharmacogenomic labelling of drugs, nevertheless, has been standardized in both Europe and the US under three categories: ‘mandatory’ , ‘recommended’ and for ‘informative’ purposes. So far, mandatory pharmacogenomic labelling is required where clinical trials have established the basis for response. In the category of recommended use, there have been no clinical trials (so far).
Typically, biomarker labelling covers the following subjects: drug exposure and clinical response variability, risk of adverse events, genotype-specific dosing, polymorphic drug target and disposition genes.
Considerable attention has been given to biomarkers for a range of widely-used oncology products. Apart from trastuzumab, they include tamoxifen (for breast cancer therapy), irinotecan (metastatic colorectal cancer), panitumumab and cetuximab (colon cancer).
Pharmacogenomic research is also focused on a host of other drugs and drug classes: allopurinol (anti-inflammatories), flucloxacillin and amoxicillin clavulanate (anti-infectives), as well as statins and immunosuppressants.
Companion diagnostics: measuring response to therapy
Biomarkers have made it possible to sell so-called companion diagnostics alongside expensive drugs, so as to direct therapy to the most responsive patients. One of the most prominent examples is the HER-2 test, accompanying Herceptin (trastuzumab), used to fight metastatic gastric cancer. The drug costs €42,000 for a year’s treatment.
Companion diagnostics also enable identification of potential ADRs, for example tests for the HLA-B*5701 allele accompanying the anti-HIV drug abacavir and for HLA-B*1502 with the anti-epileptic carbamazepine. The latter poses a recently confirmed risk of Stevens-Johnson syndrome and toxic epidermal necrolysis (TEN) in Han Chinese and other Asians.
Drug development and relaunch
Pharmacogenomic biomarkers are becoming integrated tools in drug development, to assess pathways encoded by polymorphic genes and to identify the enzymes which lead to the formation of an active drug metabolite, before entering clinical trials.
One new application for pharmacogenomic data is the relaunch of drugs, which have been withdrawn because of adverse events. Novartis, for example, applied in 2009 to the European Medicines Agency to use Lumiracoxib in genetically selected populations. Lumiracoxib is a prostaglandin endoperoxide synthase 2 inhibitor. It was approved to treat osteoarthritis, but was withdrawn in 2005 because of cases of DILI (drug-induced liver injury). Although retrospective genetic analyses revealed that variants of the HLA-DQ allele could predict elevated transferase levels and identify patients susceptible to DILI, Novartis withdrew its application in 2011 due to its inability to provide additional data within the timeframe specified by the Agency.
Generic drugs and pharmacogenomics
Pharmacogenomics is proving to be a weapon against generic drug imports, especially from large, low-cost producers in countries like India.
In its first-ever Recommendation, the European Society of Pharmacogenomics and Theranostics (ESPT) has called for “a harmonized approach to an updatable drug labelling of generic versions for pharmacogenomic information, as is the case for the original drug.” The ESPT cites the case of Plavix (clopidogrel), used for dual antiplatelet therapy and once the world’s second bestselling drug. Pharmacogenomic information on Plavix, it states, “reveals that genetic polymorphisms of CYP enzymes … contribute to variation in the response of individual patients.” It concludes that pharmacogenomic labelling “should be extrapolated to all medications which are marketed as both branded and generic versions.”
Clinical labs have been late entrants
The role of the clinical laboratory in pharmacogenomics broadly encompasses the following components:
In spite of being a frontline player in the application of pharmacogenomics, the position of the clinical lab has been relatively muted and unrecognised.
In 2000, a feature article noted that the “clinical lab has rarely been discussed within the context of pharmacogenomics.” It however argued that, in the future, “clinical labs will be looked to for genetic test development and validation, and for high-throughput genotyping of patients in clinical trials and routine testing.” It urged “both the labs themselves and the industry as a whole” to take cognisance of the fact.
Different from classical genetic testing
Lab techniques for pharmacogenomics differ significantly from classical genetic testing through chromosome analysis. Although state-of-the-art microarrays can interrogate and evaluate vast masses of alleles, the interpretation of test results into clinically meaningful data is complex, sometimes bewilderingly so.
This is because a particular gene mutation does not always result in a predictable phenotypic effect. A host of non-genetic factors can also play an influential role. Included here are the age, gender and ethnicity of a patient; so too are interactions with other drugs he or she is taking, and above all, any impairment in areas such as liver or renal function.
Usable and actionable information on such diverse factors may only emerge after adequate throughput of clinical data and the establishment of correspondences between genotypic and phenotypic markers. The sole entity which can bring such a scale to being is the clinical laboratory.
Meanwhile, physicians too are overloaded by new diagnostic information which emerges by the day, and need guidance from laboratories on how best to interpret and use the information contained in tests.
Unadopted pharmacogenomic tests
One factor that could accelerate the need for more clinical lab involvement may be pharmacogenomic tests which have not been adopted, in spite of evidence that they work. The best examples here are the enzymes VKORC1 and CYP2C9 for the anti-coagulant warfarin, UGT1A1 for the anti-cancer drug Irinotecan. In such cases, there have been concerns that diagnostic test costs may overwhelm the healthcare system, without demonstrable benefit.
At the moment, it is principally academic groups which are addressing such challenges. The price paid here is an acceptance of the fact that pharmacogenomics will only be “adopted slowly as risk-benefit data demonstrate the value of testing.”
Lab tests and healthcare spending
There is a heated debate underway in the US about laboratory testing as a source of healthcare spending growth. In November 2013, researchers at Beth Israel Deaconess Medical Center (BIDMC) announced the results of a review of more than 1.6 million results from 46 of the 50 most common lab tests. They found that nearly one-third of all blood tests were unnecessary.”
Some experts believe that a solution to this problem might be to increase ‘useful’ tests by laboratories, above all those for pharmacogenomic biomarkers. Even if the growth of personalized medicine increases laboratory testing, they argue this will improve a physician’s ability to make highly targeted decisions about patient treatment. This, in turn, may well reduce overall healthcare spending.
Such perspectives were suggested by Ramy Arnaout, Assistant Professor of Pathology at Harvard Medical School, and lead author of the BIDMC study. He argues that “lab tests are inexpensive. Ordering one more test or one less test isn’t going to ‘bend the curve,’ even if we do it across the board. It’s everything that happens next – the downstream visits, the surgeries, the hospital stays – that matters to patients and to the economy and should matter to us.”
Pharmacogenomics and therapy for hepatitis C virus infection
, /in Featured Articles /by 3wmediaPharmacogenomic research has been active in the area of hepatitis C virus infection. Both viral and host polymorphisms are discussed in this review to describe how the genotype information helps predict response to conventional peginterferon alfa and ribavirin therapy as well as more recently recommended direct-acting antivirals, simeprevir and sofosbuvir.
by M. Kawaguchi-Suzuki and Dr R. F. Frye
Hepatitis C virus infection
Hepatitis C virus (HCV) chronically infects 170 million people worldwide [1]. After exposure to HCV, some patients may experience fever, fatigue, dark urine, clay-coloured stool, abdominal pain, loss of appetite, nausea, vomiting, joint pain or jaundice [2]. However, the majority of patients are asymptomatic and do not seek medical attention, leading to the chronic infection rate of 75–85% [2]. HCV infection is a significant burden to society, not only because of the prevalence but also because the infection can lead to severe complications and mortality. Patients infected with HCV can develop chronic liver disease, progressing to advanced fibrosis and cirrhosis and eventually to hepatocellular carcinoma [1]. Additionally, HCV infection is the primary indication for liver transplantation in developed countries [1].
HCV is a blood-borne 9.6 kb positive-sense, single-stranded RNA virus [1, 2]. Typically, the first screening method used to diagnose HCV is the detection of anti-HCV antibodies [3]. Definitive diagnosis of HCV infection is then made by measurements of HCV RNA or ‘viral load’, with a sensitive molecular method having a lower limit of detection <15 IU/mL [3, 4]. Once a decision to treat has been made, the therapeutic goal is to achieve virologic cure or sustained virologic response (SVR) defined as undetectable HCV RNA 12 weeks after the completion of therapy [4]. Recent advancements in HCV treatment now make HCV curable for more patients, which will reduce mortality and liver-related health adverse consequences. Therapy for HCV infection
Since the identification of HCV in 1989, various treatment regimens have been used to treat HCV infection [1]. Interferon therapy was the first treatment against HCV and then combination therapy with ribavirin (RBV) became available in 1998 [1]. After the introduction of the pegylated formulation of interferon or peginterferon alfa (PEG) in 2001, the dual therapy of PEG and RBV has been the standard care for a decade, until the recent approval of direct-acting antivirals (DAAs) [1]. The first-generation DAAs are the protease inhibitors boceprevir and telaprevir. Subsequently, another protease inhibitor, simeprevir, and a polymerase inhibitor, sofosbuvir, were introduced. The three protease inhibitors were approved in combination with PEG and RBV. However, PEG-free regimens became an option for select patients with the approval of sofosbuvir.
Although the likelihood of achieving SVR has improved with each advance in treatment, HCV infection is a therapeutic area in which large inter-patient variability in response has been observed. In order to predict treatment response and to tailor therapy for individual patients infected with HCV, the extent to which pharmacogenomic biomarkers explain this variability has been examined.
Pharmacogenomics: viral polymorphism
HCV is categorized into genotypes 1–7 and further classified into subtypes a, b, etc., based on sequence divergence [1]. The observed HCV genotype depends largely on the geographic location; genotype 1 accounts for 70% of cases in the Americas, 50–70% of cases in Europe, and 75% of cases in Japan, with genotypes 2 and 3 being the next most prevalent [1]. In contrast, genotypes 3 and 6 have been widely identified in South and Southeast Asia, while genotypes 4 and 5 are most commonly observed in Africa [1]. Genotype 7 was most recently discovered, but its clinical importance has not yet been determined [1]. Identification of the HCV genotype is important for HCV-infected patients because treatment choice, therapy duration, and treatment response depend on the viral genotype. The first-generation DAAs and simeprevir are only indicated for genotype 1 infection. However, sofosbuvir has pan-genotypic activity and is approved for the treatment of genotypes 1, 2, 3, and 4 [5].
Before the approval of the use of DAAs, HCV therapy targeted the host immune system with the use of PEG. However, since therapy now directly targets the virus itself, various viral polymorphisms that may confer treatment resistance have been reported. The most notable one is the Q80K polymorphism observed in HCV genotype 1a [6]. Findings from the phase 3 QUEST-1, QUEST-2, and PROMISE trials are shown in Figure 1 [6]. QUEST-1 and QUEST-2 were conducted among treatment-naïve patients, whereas PROMISE was a study in patients who relapsed after previous treatment with an interferon-based regimen.
In general, HCV genotype 1a was considered less susceptible to the treatment, but when the data were analysed based on Q80K polymorphism, the SVR rates in genotype 1a were similar to those in genotype 1b if the Q80K polymorphism was absent. However, the SVR rates turned out to be even lower when the polymorphism was present. Based on these data, both prescribing information and clinical guidelines suggest alternative therapy if the Q80K polymorphism is detected in HCV genotype 1a infection [6]. Consequently, Q80K polymorphism testing is recommended by the clinical guidelines in all patients before the initiation of simeprevir, PEG, and RBV triple regimen [4].
Pharmacogenomics: host polymorphism
Earlier, various genome-wide association studies and candidate gene studies found two single nucleotide polymorphisms (SNPs) in the host were associated with SVR in HCV genotype 1 infection after PEG and RBV therapy [7]. The two SNPs, rs12979860 and rs8099917, are located in IFNL3 gene (previously called IL28B) [7]. Rs12979860 CC and rs8099917 TT genotypes have been considered as favourable response genotypes, with SVR rates of around 80% achieved in genotype 1 infection, whereas rs12979860 minor T allele or rs8099917 minor G allele carriers had lower SVR rates of about 20% [8]. SVR rates tend to be higher in HCV genotype 2 and 3 infections with PEG and RBV therapy, compared to those in genotype 1 therapy, and the association of these two SNPs with SVR has not been as strong in genotype 2 and 3 infections [7]. However, similar association of the two IFNL3 SNPs with SVR to that in genotype 1 infection has been shown in genotype 4 infection [7]. Although data has been scarce in other rare genotype infections, the IFNL3 genotype has been one of the strongest predictors of SVR with PEG and RBV therapy [7, 8]. The exact mechanism by which the IFNL3 SNPs affect the phenotype has not been fully elucidated, but baseline differences in the expression level of interferon-stimulated genes have been proposed [9]. Data has been collected with both IFNL3 SNPs, but rs12979860 is more commonly used if a single SNP has to be chosen for research or clinical purpose.
The association of treatment response with the IFNL3 genotype has also been observed with interferon-based DAA therapies. Currently, treatment with boceprevir or telaprevir is not recommended by the guidelines, and most commonly used regiments include simeprevir and/or sofosbuvir [4]. Figure 2 describes SVR rates in treatment-naïve and treatment-experienced patients who were treated with simeprevir or sofosbuvir combined with PEG and RBV as indicated in the prescribing information [5, 6]. Higher SVR rates were consistently observed in patients with the rs12979860 CC genotype, compared to the T allele carriers. However, it should be noted that the difference in SVR rates between the IFNL3 genotypes was comparatively modest with the addition of a DAA to the conventional PEG and RBV therapy. In addition, no significant difference was observed in SVR rates based on the IFNL3 genotype in patients infected with HCV genotype 2 or 3 after an interferon-free regimen of sofosbuvir and RBV [10].
Summary and future directions
Large inter-patient variability exists in response to PEG and RBV therapy, and the IFNL3 genotype has been demonstrated as one of the strongest predictors for SVR, especially in HCV genotype 1 infection. This trend has also been observed in interferon-based DAA therapies. However, if an interferon-free regimen becomes more readily available in the future, with the potential for SVR rates to approach 100%, the IFNL3 genotype may no longer hold clinical utility. However, IFNL3 genotype may still need to be tested during drug development to ensure that investigational agents will have efficacy in patients carrying the variant allele previously associated with an unfavourable treatment response. Additionally, when PEG is eliminated from treatment regimens and only DAAs are combined, cross-resistance may become a concern in the future, especially in patients who failed a DAA regimen previously. Various viral polymorphisms have been detected in protease inhibitors, and cross-resistance can be an issue in this drug class [6, 11, 12]. Viral polymorphisms may play a bigger role and need to be monitored for the future.
References
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7. Kawaguchi-Suzuki M, Frye RF. The role of pharmacogenetics in the treatment of chronic hepatitis C infection. Pharmacotherapy. 2014; 34(2): 185–201.
8. Pacanowski M, et al. New genetic discoveries and treatment for hepatitis C. JAMA. 2012; 307(18): 1921–1922.
9. Cariani E, et al. Translating pharmacogenetics into clinical practice: interleukin (IL)28B and inosine triphosphatase (ITPA) polymophisms in hepatitis C virus (HCV) infection. Clin Chem Lab Med. 2011; 49(8): 1247–1256.
10. Jacobson IM, et al. Sofosbuvir for hepatitis C genotype 2 or 3 in patients without treatment options. N Engl J Med. 2013; 368(20): 1867–1877.
11. Victrelis (boceprevir) package insert. Merck & Co., Inc. 2013.
12. Incivek (telaprevir) package insert. Vertex Pharmaceuticals 2013.
The authors
Marina Kawaguchi-Suzuki PharmD, BCPS; Reginald F. Frye* PharmD, PhD, FCCP
Department of Pharmacotherapy and Translational Research,
University of Florida College of Pharmacy, Gainesville,
FL 32610-0486, USA
*Corresponding author
E-mail: frye@cop.ufl.edu
Laboratory biomarkers of rheumatoid arthritis
, /in Featured Articles /by 3wmediaRheumatoid arthritis is a systemic autoimmune disease that is an important socio-economic health problem. Recent evidence about the immunopathogenesis of this disorder might open new perspectives for a more appropriate laboratory approach. In this review, our attention is focused on the clinical relevance and appropriateness of laboratory biomarkers correlated with early diagnosis, prognosis, evolutive aspects of the disease and therapeutic efficacy.
by Prof. D. P. Foti, Dr E. Palella, F. Accattato, M. Greco, Prof. E. Gulletta
Introduction
Rheumatoid arthritis (RA) is a chronic inflammatory polyarthritis that can affect any synovial-lined diarthrodial joint, especially the wrist and the small joints of the hand. RA evolves as progressive articular damage leading to joint deformities and may be accompanied and complicated by several extra-articular manifestations [1]. The onset of RA clinical manifestations is often between the 4th and 5th decades of life, although a second peak of incidence is reported between 60 and 70 years of age. The prevalence is of 0.5–1% in industrialized countries, reaching the rate of 5% in women over 55 years. Following the 2010 RA classification criteria, the target population to be tested includes patients with at least one joint with a definite clinical synovitis that cannot be explained by another alternative disease. The classification criteria for RA is a score-based algorithm: a score of at least 6/10 obtained by the sum of scores for each category A–D (Joint involvement, Serology, Acute-phase reactants, Duration of symptoms) is needed to classify a patient as having definite RA [2].
RA is a multifactorial disease, in which environmental and genetic factors seem to play a role in the susceptibility and evolution of illness [3]. Several studies have shown a close genetic association with antigens of the MHC-II, in particular HLA-DRB1 [4], and PTPN22 that encodes the lymphoid tyrosine phosphatase (LYP), which is a critical negative regulator of signalling through the T cell receptor [5]. It is known that molecular targets of rheumatic autoimmune reaction are proteins that undergo post-translational modification typically associated with inflammation and apoptosis, such as citrullination and keratinization. Self-antigens (collagen, proteoglycans, rheumatoid factor and citrullinated proteins) probably play a role in the chronic evolution of the process, whereas super-antigens may be involved in the onset of illness. Pro-inflammatory substances released by cells from the immune system (GM-CSF, IL-1, IL-6, TNFα and its receptor, IL-17, IL-20, IL-21, IL-23) maintain the inflammatory process and contribute to the chronic damage [6].
Most recent data on the pathogenetic mechanisms have led to a new laboratory approach on the choice of proper biomarkers [7] useful for each phase of clinical decision making (prediction, diagnosis, prognosis, monitoring therapeutic efficacy or adverse effects), in order to improve the patient management (fig. 1).
Biohumoral markers
Rheumatoid factor
Rheumatoid factor (RF) is an antibody directed against the Fc portion of immunoglobulin G (IgG). The evaluation of its isotypes has been used to enhance the serological diagnosis of RA, although in seropositive RA patients, the levels of RF are not related to either bone damage or status of the disease. However, it is not specifically associated with RA as it may be present in patients affected by other autoimmune or infectious diseases, and even in healthy elderly subjects. For several years, RF has been proposed as a useful tool for classifying patients as positive or negative at onset of disease and monitoring biological therapies [8].
Matrix metalloproteinases
Several proteolytic enzymes, including matrix metalloproteinases (MMP-1, -2, -3 and -9), cysteine proteinases (cathepsin B, H, L), serine proteinase (elastase, PA, cathepsin G) and aspartic acid proteinase (cathepsin D), play a role in the pathogenesis of RA. Among these, the metalloproteinases represent a family of important factors which cause the destruction of articular tissue. MMP-3 (stromelysin-1), expressed by synovial and articular cells, fibroblasts, chondroblasts and osteoclasts, can be a very useful marker for prediction of joint destruction. It acts upon the extracellular components of cartilage, such as fibronectin, collagen IV and V, elastin, proteoglycans, or even together with other MMPs in the disruption of cartilage. It is present in synovial fluid during the active phase of disease and its levels correlate with serum concentrations, independently from the patient’s age and severity of the disease. MMP-3 levels are strongly associated with disease activity, inflammatory markers and cartilage breakdown, indicating that it represents a potential biomarker of severity and progression to disabling disease [9].
A Japanese study has demonstrated that serum MMP-3 levels can be considered a predictor of joint destruction in RA, and its assessment could be useful in routinely evaluated outcome in the follow-up of RA patients [10].
Autoantibodies
Currently, several papers emphasize the importance of identifying a complete profile of RA-associated antibodies to improve the early diagnosis of disease and provide prognostic and theragnostic indications. The immunological profile consists of the definition of haplotype, evaluation of the cytoplasmic pattern of antinuclear autoantibodies (ANA), anti-citrullinated peptides antibodies (ACPA), and measurement of plasma levels of Th1 and Th2 cytokine networks. ACPA (vimentin, type II collagen, alpha-enolase and fibrinogen) are specific for RA and are associated with typically distinct clinical behaviour and genetic background. These antibodies can be present in serum years before the appearance of clinical symptoms and are highly specific and extremely useful for diagnosing RA. In this regard, ACPA serological positivity could be considered the most specific biomarker for RA, although these antibodies are not appropriate for monitoring disease progression [11].
RA and non-RA patients could be discriminated by a cyclic citrullinated peptides (CCP) antibody evaluation. Anti-CCP2 antibodies (IgG and IgA isotypes), a subset of ACPA, foresee the onset and development of RA, with the highest predictive value seen for IgG anti-CCP2 autoantibodies. This analytical data can have a higher positive predictive value in an at-risk rather than in a general population, thus the evaluation of the haplotype profile can improve the early diagnostic outcome.
Anti-CCP2 antibodies demonstrate the best diagnostic performances for profiling, thus they must be used as a first-line screening for the identification of subgroups of patients. The use of multiplex assays may facilitate a wider implementation of profiling [12].
Cytokines
Cytokines regulate many biological processes, including inflammatory and immune responses. An imbalance between pro- and anti-inflammatory cytokines or their uncontrolled production by activated immune cells can play a crucial role in regulating inflammatory diseases, such as RA.
Patients affected by RA have increased serum levels of several cytokines and chemokines years before the onset of symptoms of joint disease. Cytokine measurement by microarray is useful to evaluate the profile of pro- or anti-inflammatory molecules (IL-1β, IL-6, TNF-α, IL-10, VEGF, MCP-1, IL-17). In RA, Th-17 cells have been shown to play a central role by secreting IL-17, which activates a number of cell types involved in the pathogenesis of RA, including synovial fibroblasts, monocytes, macrophages, chondrocytes and osteoblasts [13]. The immune response during RA can also be modulated by Treg lymphocytes. These cells can be well characterized by cytofluorimetric assay by targeting specific markers (CD4+CD25high FoxP3+). The balance between Th-17 and Treg cells is a key point in autoimmune response. In general, Th-17 cells promote autoimmunity, whereas Treg cells protect against the occurrence of autoimmune diseases. Recent data have shown that IL-6 and TNF-α, by triggering Th17-cells, may alter the Th17/Treg balance, thereby promoting the autoimmune response. In this context, innovative therapies using anti-TNF-α and anti-IL-6 biological drugs, by decreasing the Th17/Treg ratio, have been shown to cause a clinical improvement in RA patients [14].
Multiparametric approaches in RA diagnosis and management
Pathogenic and clinical evidence suggest a new approach for laboratory medicine to evaluate patients in all different phases of RA progression. The American Rheumatism Association guidelines recommend that baseline laboratory evaluation include a complete blood cell count with differential, RF, erythrocyte sedimentation rate (ESR) and/or C-reactive protein (hsCRP), renal and hepatic function assessment. These laboratory findings may also be used to monitor the disease course in association with ANA and Anti-CCP antibodies [15]. In order to completely and correctly evaluate RA patients, several studies suggest combining the cytokine profile and MMP-3 measurements with conventional tests. The measurement of cytokines by multiparametric microarray is needed to completely evaluate the immunological response, the activation of Th1 or Th2 cells, the cytokine network and the stimulation of Th17 cells. MMP-3 can be considered an effective biomarker of disease aggressiveness and progression. Recently, by using a Venn diagram to predict potentially useful laboratory analytes, Curtis et al. have validated an algorithm with 12 biomarkers to obtain a multi-biomarker disease activity (MBDA) score for RA patients, with no effects from common comorbidities [16]. This complete laboratory profiling may allow a correct and personalized therapeutic treatment and a prognostic evaluation. In the future, the application of genomics and proteomics arrays will provide significant improvements in the characterization of the individual patient’s status at diagnosis and the response to therapeutic treatments.
References
1. Cavagna L, Boffini N, Cagnotto G, et al. Atherosclerosis and rheumatoid arthritis: more than a simple association. Mediators Inflamm. 2012; 2012: 147354.
2. Aletaha D, Neogi T, Silman AJ, et al. 2010 Rheumatoid arthritis classification criteria: an American College of Rheumatology/European League Against Rheumatism collaborative initiative. Arthritis Rheum. 2010; 62: 2569–2581.
3. Pincus T, Kavanaugh A, Sokka T. Benefit/risk of therapies for rheumatoid arthritis: underestimation of the “side effects” or risks of RA leads to underestimation of the benefit/risk of therapies. Clin Exp Rheumatol. 2004; 22(5 Suppl 35): S2–11.
4. Deane KD, El-Gabalawy H. Pathogenesis and prevention of rheumatoid disease: focus on preclinical RA and SLE. Nat Rev Rheumatol. 2014; 10(4): 212–228.
5. Fiorillo E, Orrú V, Stanford SM, et al. Autoimmune-associated PTPN22 R620W variation reduces phosphorylation of lymphoid phosphatase on an inhibitory tyrosine residue. J Biol Chem. 2010; 20: 26506–26518.
6. Burmester GR, Feist E, Dörner T. Emerging cell and cytokine targets in rheumatoid arthritis. Nat Rev Rheumatol. 2014; 10: 77–88.
7. Smolen JS, Alehata D, Redlich K. The pathogenesis of rheumatoid arthritis: new insights from old clinical data? Nat Rev Rheumatol. 2012; 8(4): 235–243.
8. Can M, Najip A, Yılmaz N, et al. Immunoglobulin subtypes predict therapy response to the biologics in patients with rheumatoid arthritis. Rheumatol Int. 2013; 33(6): 1455–1460.
9. Mamehara A, Sugimoto T, Sugiyama D, et al. Serum MMP-3 as predictor of joint desctruction in RA, treated with non-biological diseases modifying anti-rheumatic drugs. Kobe J Med Sci. 2010; 56(3): E98–107.
10. Shinozaki M, Inoue E, Nakajima A, et al. Elevation of serum matrix metalloproteinase-3 as a predictive marker for the long-term disability of rheumatoid arthritis patients in a prospective observational cohort IORRA. Mod Rheumatol. 2007; 17(5): 403–408.
11. Jaskowski TD, Hill HR, Russo KL, et al. Relationship between rheumatoid factor isotypes and IgG anti-cyclic citrullinated peptide antibodies. J Rheumatol 2010; 37(8):1582–1588.
12. Conrad K, Roggenbuck D, Reinhold D, Dörner T. Profiling of rheumatoid arthritis associated autoantibodies. Autoimm Rev 2010; 9(6): 431–435.
13. Samson M, Audia S, Janikashvili N, et al. Brief report: inhibition of IL-6 function corrects Th17/Treg imbalance in rheumatoid arthritis patients. Arthritis Rheum. 2012; 64(8): 2499–2503.
14. Miossec P. Interleukin-17 in rheumatoid arthritis: if T cells were to contribute to inflammation and destruction through synergy. Arthritis Rheum. 2003; 48: 594–601.
15. Singh A. 2012 Update of the 2008 American College of Rheumatology recommendations for the use of disease-modifying antirheumatic drugs and biologic agents in the treatment of rheumatoid arthritis. Arthritis Care Res. (Hoboken) 2012; 64: 625–639.
16. Curtis JR, van der Helm-van Mil AH, Knevel R, et al. Validation of a novel multibiomarker test to assess rheumatoid arthritis disease activity. Arthritis Care Res. (Hoboken) 2012; 64(12): 1794–1803.
17. Imboden JB. The immunopathogenesis of rheumatoid arthritis. Annu Rev Pathol. 2009; 4: 417–434.
The authors
Daniela P. Foti MD, PhD; Eleonora Palella MD; Francesca Accattato Bs Sci; Marta Greco Bs Sci; Elio Gulletta* MD
Dept. of Health Sciences, University Magna Grecia, Catanzaro, Italy
*Corresponding author
E-mail: gulletta@unicz.it
Biomarker diversity in lupus: challenges and opportunities
, /in Featured Articles /by 3wmediaSystemic lupus erythematosus (SLE) is a chronic autoimmune disease associated with diverse clinical manifestations. Accurate diagnosis, prediction of disease activity, organ involvement and management remains problematic owing to a lack of reliable biomarkers. This article reviews traditional and a few promising candidate biomarkers in SLE with specific clinical implications.
by Dr Anne E. Tebo
Background
Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by heterogeneity in disease manifestations as well as diversity in immunologic and therapeutic responses. Despite longstanding research efforts, precise diagnosis and prediction of response to treatment remain problematic owing to variable disease presentation and course as well as a lack of sensitive and specific biomarkers [1, 2]. Several factors contribute to the immune abnormality and clinical heterogeneity that occurs in SLE; these include genetic, epigenetic, environmental, and hormonal influences. The interplay between these elements drives the production of a variety of autoantibodies, complement products, inflammatory markers and other mediators identified as biomarkers to diagnose, monitor, stratify and/or predict disease risk, course or response to treatment [1–7]. These biomarkers – genetic, biologic, biochemical or molecular – may correlate with disease pathogenesis or specific clinical manifestations and can be evaluated qualitatively or quantitatively in laboratories. Notable amongst these are diverse autoantibodies and complement products which (in addition to clinical manifestations such as skin lesions, arthritis, renal disorder, hematologic changes, neurologic disorder amongst others) are traditionally considered hallmarks of disease [8, 9]. This review article highlights traditional and a few promising candidate serologic, cellular and urine biomarkers for diagnosing and predicting disease activity as well as renal involvement in SLE.
Biomarkers for the diagnosis of SLE
The updated American College of Rheumatology (ACR) revised criteria for the classification of SLE [8] is largely used in clinical practice to diagnose patients. In addition to specific clinical manifestations, the guidelines recommend testing for antinuclear antibodies (ANA), anti-double stranded deoxyribonucleic acid (anti-dsDNA), anti-Smith (anti-Sm) and antiphospholipid antibodies (lupus anticoagulant, IgG and IgM antibodies to cardiolipin (aCL) and beta2 glycoprotein I (anti-β2GPI). Recently, the Systemic Lupus Collaborating Clinics proposed the SLICC criteria for SLE in view of recent knowledge of the immunology of SLE [9]. Based on the SLICC rule for the classification of SLE, a patient must satisfy at least four criteria, including at least one clinical criterion and one immunologic criterion or must have biopsy-proven lupus nephritis (LN) in the presence of ANAs or anti-dsDNA antibodies. SLE is also associated with a variety of extractable nuclear antibodies such as anti-SSA, anti-SSB, and anti-snRNP as well as anti-ribosomal P, anti-histone, anti-nucleosome, anti-PCNA and anti-C1q autoantibodies [3, 5, 6]. The diagnostic characteristics of these autoantibodies (especially the anti-dsDNA antibodies) have been reported to be variable, which may be attributable the diversity of analytical methods, target antigens, patient demographics and SLE clinical subsets investigated [3, 5, 6, 11].
In addition to specific autoantibody tests, the proposed SLICC criteria recommend testing for complements C3, C4 and CH50 as well as using the direct Coombs assay. In the past several years, serum C3, C4 and CH50 levels have traditionally been used to diagnose and monitor disease activity in SLE patients [reviewed in 1, 2, 4]. However, in vitro activation may compromise interpretation of results and serum complement levels do not differentiate between consumption and production, which may be important for diagnosis. There is some evidence that cell-bound complement activation products (CB-CAPs) may facilitate SLE diagnosis [4, 12, 13]. These include complement C4-derived ligand deposited on erythrocytes (EC4d), platelets (PC4d), B lymphocytes (BC4d) and reticulocytes as detected by flow cytometry. Compared to disease controls, there is a relative increase of cell-bound C4d (CB-C4d) in SLE. However, the actual relevance of a single CB-C4d assay to the diagnosis of SLE is thought to be unlikely, although a panel of EC4d and BC4d assays is proposed to be predictive of SLE [12]. Further studies in diverse SLE clinical subsets and populations are required to determine the optimal CB-C4d panels for diagnostic evaluation.
Biomarkers for assessing disease activity
There are currently no consensus measures or biomarkers to reliably evaluate disease activity, predict flares and their differentiation from permanent damage in SLE patients. Disease activity indices such as the SLE disease activity index 2000 (SLEDAI-2K), British Isles Lupus Assessment Group (BILAG 2004), and the Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index (SDI) are all complex and mostly used in academic centres and/or in clinical trials [reviewed in 14]. In addition to routine serologic markers for inflammation, anti-dsDNA antibodies, C3, and C4 have been traditionally used to assess disease activity and predict flares in SLE (Table 1). For patients with LN, urine analysis for protein, sediment, protein-creatinine ratio and albumin are used to evaluate activity, monitor treatment response and predict relapse. Several studies have examined the associations between traditional and non-traditional biomarkers to determine optimal panel of markers associated with disease activity or predicting severity [reviewed in 1–7]. The outcomes in these investigations have been inconsistent, probably due to variability in detection methods and heterogeneity in patient populations. These inconsistencies have hampered the adoption of an acceptable biomarker panel to evaluate and monitor disease activity.
A number of promising candidate biomarkers associated with disease activity in SLE have been identified and are reviewed in other publications [1, 2, 4–7]. These include biomarkers for serologic (autoantibodies, cytokines and cytokine receptors, markers of endothelial cell activation, and soluble cell surface molecules), cellular (cell-bound C4d, CD27high plasma cells and other lymphocyte subsets) and urine [neutrophil gelatinase-associated lipocalin (NGAL), sVCAM-1 (soluble vascular cell adhesion molecule 1), MCP-1 (monocyte chemotactic protein 1), and TWEAK (tumor necrosis factor-like weak inducer of apoptosis), immunoglobulin free light chain, von Willebrand factor, IL-6] analyses. Of the several autoantibodies described, anti-nucleosome and anti-C1q antibodies appear to significantly correlate with disease activity and/or predict flares [2, 4, 6]. Chromatin, the DNA-histone complex found in the nucleus is organized into a repeating series of nucleosomes. In SLE patients, anti-nucleosome antibodies are more likely to be detected in patients with LN and may serve as a useful biomarker in the diagnosis of active LN [reviewed in 2]. Anti-C1q IgG antibodies that target epitopes present within the collagen-like tail of C1q are also seen in varying prevalence in SLE patients. Increasing titres in anti-C1q antibodies have been suggested to predict renal flares [2, 4, 6]. Other serologic biomarkers such as cytokines (IFN-α/β, IFN-γ, IL-1, IL-6, IL-10, IL-12, IL-15, IL-17, IL-21, and TNF-α), IFN-inducible chemokines, a few cytokine receptors, complements, specific markers of endothelial cell activation, BLys and several others associated with SLE pathogenesis and disease activity have been described [1, 2, 4]. However, these have mostly been reported in research studies and are beyond the scope of this article.
Quite a number of studies have shown that elevated levels of complement activation cleavage products may reflect disease activity more accurately and are more likely than conventional measurements in the prediction disease flares. Of these, the best described is the Cd4 fragment, which is capable of binding several cell types. In one study, EC4d levels were observed to be higher in patients with ‘more active’ and ‘most active’ SLE compared with those with ‘less active’ disease [13]. EC4d measurements were also found to be associated with specific measures of disease activity even after adjusting for serum levels of C3, C4 and anti-dsDNA antibodies [13]. However, independent prospective investigations with appropriate controls are needed to validate these observations.
Biomarkers for renal involvement
Of the different clinical subsets of SLE, LN is one of the most common and associated with significant morbidity and mortality. In the US, approximately 35% of adults with SLE have clinical evidence of nephritis at the time of diagnosis, with an estimated 50–60% developing nephritis during the first 10 years of disease [reviewed in 15]. Among these patients, quite a few will progress to end-stage renal disease. Improved methods for detecting LN would allow earlier treatment preventing irreversible impairment of renal function and damage. In place of invasive, subjective and costly serial renal biopsies, tests such as creatinine clearance, levels of urine protein and sediment as well as serologic determinations of C3, C4, creatinine level and anti-dsDNA titres have for decades been used to follow the onset, course, and severity of LN. It is however, recognized that these analyses are inadequate as they are limited in responsiveness to change and therefore unsuitable for patient care [7, 15].
In an effort to reliably diagnose LN, several candidate biomarkers have identified [1, 2, 7]. Among autoantibodies, antichromatin/anti-nucleosome and anti-C1q antibodies have shown some promise as biomarkers of renal involvement as previously described in this article. Of the urine protein biomarkers NGAL, sVCAM-1, MCP-1 and TWEAK, amongst others, have received considerable attention (Table 1) [1, 2, 7]. Of these, NGAL has been much studied. It is a small protein expressed in the neutrophils and certain epithelial cells, including the renal tubules. Under normal physiologic conditions, NGAL expressions are low in urine and plasma, but quickly rise from basal concentrations in response to kidney injury to reach diagnostic thresholds within a very short period of time. This is in contrast to the routinely used kidney function tests such as creatinine, where increased concentrations may not be observed until 24 to 48 hours after injury and often lack sensitivity. Urine NGAL is, however, not specific for SLE and further studies are necessary to establish accurate reference ranges based on age, gender and ethnicity. Like NGAL, urine sVCAM-1, sICAM-1 (soluble intercellular CAM-1) and MCP-1 have been shown in human studies to be strongly correlated with LN activity and severity [reviewed in 7]. With the identification of these novel urinary biomarkers for diagnosing and differentiating active versus inactive LN, several studies have examined their relationship with histological features of LN. Brunner et al. 2012 examined a number of established markers (anti-dsDNA, serum C3, C4, creatinine, urinary protein : creatinine ratio, etc.) and a few candidate urinary biomarkers [MCP-1, NGAL, lipocalin-type prostaglandin D-synthetase (L-PGDS), α1-acid-blycoprotein (AAG/AGP), transferrin (TF), and ceruloplasmin (CP)] in urine samples from 76 SLE patients collected within 2 months of kidney biopsy [reviewed in 7]. These urinary biomarkers were compared with histopathologic features of the kidney biopsy such mesangial expansion, capillary proliferation, crescent formation, wire loops, or fibrosis. Overall, their results indicated that levels of specific urinary biomarkers were increased in active LN and appeared to correlate with distinctive histologic features in renal biopsies. Furthermore, based on the presence of defined urine proteins, the authors could predict specific LN signatures. LN activity signature was defined by a combination of urinary MCP-1, AAG, and CP levels and protein : creatinine ratio while LN chronicity was characterized by NGAL, MCP-1 and creatinine clearance. The combined tests of MCP-1, AAG, TF, creatinine clearance and serum C4 was indicative of a potential biomarker panel for membranous nephritis. However, like NGAL, increased expression of these urine biomarkers is not exclusive to LN. Future studies are likely to highlight the relevance of specific urine biomarker proteins in predicting renal involvement in SLE.
Conclusion
There is considerable evidence that no single biomarker will be sufficient to diagnose, monitor and stratify all patients with SLE. Ideally, new biomarkers should provide information not available from traditional tests. Recent efforts geared towards the discovery and validation of biomarker ‘panels’ or ‘signatures’ of SLE represent an informed approach. Validation studies with endpoints that ensure a true measure of the intended clinical process in diverse cohorts coupled with robust analytical assays and high statistical power to confirm these panels are needed.
References
1. Illei GG, Tackey E, Lapteva L, Lipsky PE. Biomarkers in systemic lupus erythematosus. I. General overview of biomarkers and their applicability. Arthritis Rheum. 2004; 50: 1709–1720.
2. Liu CC, Kao AH, Manzi S, Ahearn JM. Biomarkers in systemic lupus erythematosus: challenges and prospects for the future. Ther Adv Musculoskelet Dis. 2013; 5: 210–233.
3. To CH, Petri M. Is antibody clustering predictive of clinical subsets and damage in systemic lupus erythematosus? Arthritis Rheum. 2005; 52: 4003–4010.
4. Leffler J, Bengtsson AA, Blom AM. The complement system in systemic lupus erythematosus: an update. Ann Rheum Dis. 2014; 73: 1601–1606.
5. Jeltsch-David H, Muller S. Neuropsychiatric systemic lupus erythematosus: pathogenesis and biomarkers. Nat Rev Neurol. 2014; 10(10): 579–596.
6. Cozzani E, Drosera M, Gasparini G, Parodi A. Serology of lupus erythematosus: correlation between immunopathological features and clinical aspects. Autoimmune Dis. 2014; 2014: 321359.
7. Bennett M, Brunner HI. Biomarkers and updates on pediatrics lupus nephritis. Rheum Dis Clin North Am. 2013; 39: 833–853.
8. Hochberg MC. Updating the American College of Rheumatology revised criteria for the classification of systemic lupus erythematosus. Arthritis Rheum. 1997; 40: 1725.
9. Petri M, Orbai AM, Alarcón GS, et al. Derivation and validation of the Systemic Lupus International Collaborating Clinics classification criteria for systemic lupus erythematosus. Arthritis Rheum. 2012; 64: 2677–2686.
10. Yu C, Gershwin ME, Chang C. Diagnostic criteria for systemic lupus erythematosus: a critical review. J Autoimmun. 2014; 48–49: 10–13.
11. Isenberg DA, Manson JJ, Ehrenstein MR, Rahman A. Fifty years of anti-ds DNA antibodies: are we approaching journey’s end? Rheumatology (Oxford) 2007; 46: 1052–1056.
12. Kalunian KC, Chatham WW, Massarotti EM, Reyes-Thomas J, Harris C, Furie RA, Chitkara P, Putterman C, Gross RL, Somers EC, Kirou KA, Ramsey-Goldman R, Hsieh C, Buyon JP, Dervieux T, Weinstein A. Measurement of cell-bound complement activation products enhances diagnostic performance in systemic lupus erythematosus. Arthritis Rheum. 2012; 64: 4040–4047.
13. Kao AH, Navratil JS, Ruffing MJ, Liu CC, Hawkins D, McKinnon KM, Danchenko N, Ahearn JM, Manzi S. Erythrocyte C3d and C4d for monitoring disease activity in systemic lupus erythematosus. Arthritis Rheum. 2010; 62: 837–844.
14. Romero-Diaz J, Isenberg D, Ramsey-Goldman R. Measures of adult systemic lupus erythematosus: updated version of British Isles Lupus Assessment Group (BILAG 2004), European Consensus Lupus Activity Measurements (ECLAM), Systemic Lupus Activity Measure, Revised (SLAM-R), Systemic Lupus Activity Questionnaire for Population Studies (SLAQ), Systemic Lupus Erythematosus Disease Activity Index 2000 (SLEDAI-2K), and Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index (SDI). Arthritis Care Res. (Hoboken). 2011; 63(Suppl 11): S37–46.
15. Hahn BH, McMahon MA. American College of Rheumatology guidelines for screening, treatment, and management of lupus nephritis. Arthritis Care Res. (Hoboken). 2012; 64: 797–808.
The author
Anne E. Tebo1,2 PhD
1Department of Pathology, University of Utah School of Medicine, Salt Lake City, UT 84132, USA
2ARUP Institute for Clinical and Experimental Pathology, Salt Lake City, UT 84108, USA
E-mail: anne.tebo@hsc.utah.edu;
anne.tebo@aruplab.com
Multiplex autoantibody screening in neurological diseases
, /in Autoimmunity & Allergy, Featured Articles /by 3wmediaThe spectrum of autoimmune neurological syndromes has expanded substantially in the last fifteen years because of the discovery of novel anti-neuronal autoantibodies. Today’s autoantibody test portfolio includes over 32 different specificities associated with neurological diseases.
Scientists discover how to ‘switch off’ autoimmune diseases
, /in Featured Articles /by 3wmediaApitope, the drug discovery and development company focused on disease-modifying treatments for patients with autoimmune and allergic diseases, announced today that Bristol University research led by Apitope Founder and CSO, Prof David Wraith, on its treatment approach to autoimmune diseases, such as Multiple Sclerosis (MS), has been published in Nature Communications.
www.apitope.comThe researchers at the University of Bristol reported an important breakthrough in the fight against debilitating autoimmune diseases such as Multiple Sclerosis. Rather than the body’s immune system destroying its own tissue by mistake, researchers have discovered how cells convert from being aggressive to actually protecting against disease. It is hoped this latest insight will lead to the widespread use of antigen-specific immunotherapy as a treatment for many autoimmune disorders, including Multiple Sclerosis (MS), Factor VIII intolerance in hemophiliacs, Graves’ disease (hyperthyroidism) and uveitis, conditions for which Apitope is developing important new therapies.
Commenting on the research, Dr. Keith Martin, CEO said: “Multiple Sclerosis affects around 100,000 people in the UK and 2.5 million people worldwide. This is an important breakthrough in our fight against debilitating autoimmune diseases by providing further important information on how to stop cells attacking healthy body tissue. This research further reinforces Apitope’s treatment approach, which has already successfully completed two clinical trials in MS patients with MRI data showing a significant decrease in new lesions, and has the potential to improve the lives of millions of people worldwide. Importantly, we are now taking this approach into other serious autoimmune conditions as well as MS.”
The reported study, funded by the Wellcome Trust, is published in Nature Communications. The article entitled “Sequential transcriptional changes dictate safe and effective antigen-specific immunotherapy” that describes how researchers have managed to “switch off” autoimmune disease as a breakthrough for Multiple Sclerosis (MS) treatment, can be viewed on the following link: http://tinyurl.com/nupw4lj Apitope International NV, based in Belgium and the UK, is a drug developer of immunotherapies for the treatment of autoimmune and allergic diseases, including multiple sclerosis, factor VIII intolerance, uveitis and Graves’ disease. The company has a patented discovery platform which enables selection of potential disease-modifying peptide therapies for the autoimmune/allergic disease of interest; and has already generated a pipeline of 7 programmes in clinical and preclinical development, of which the lead programme in multiple sclerosis is partnered with Merck Serono.
LKM Antibodies: skilled pattern recognition
, /in Autoimmunity & Allergy, Featured Articles /by 3wmediaThe detection of liver/kidney/microsomal antibodies (LKM) is one of the most common analytical procedures performed in the autoimmunity laboratory. Several techniques can be currently adopted that allow the detection of LKM in patient serum, such as ELISA, line immunoassay or indirect immunofluorescence. Far from being outdated, indirect immunofluorescence is the major method used for the […]