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Standardisation of myositis-specific antibodies: where are we today?
  1. Michael Mahler1,
  2. Jean-Baptiste Vulsteke2,3,
  3. Xavier Bossuyt4,5,
  4. Ellen De Langhe2,3,
  5. Minoru Satoh6
  1. 1 Research and Development, Inova Diagnostics, San Diego, California, USA
  2. 2 Laboratory of Tissue Homeostasis and Disease, Skeletal Biology and Engineering Research Center, Department of Development and Regeneration, KU Leuven, Leuven, Belgium
  3. 3 Division of Rheumatology, University Hospitals Leuven, Leuven, Belgium
  4. 4 Clinical and Diagnostic Immunology, Department of Microbiology, Immunology and Transplantation, KU Leuven, Leuven, Belgium
  5. 5 Department of Laboratory Medicine, University Hospitals Leuven, Leuven, Belgium
  6. 6 Department of Clinical Nursing, University of Occupational and Environmental Health, Kitakyushu, Japan
  1. Correspondence to Dr Michael Mahler, Research, INOVA Diagnostics, San Diego, CA 92131, USA; mmahler{at}

Statistics from

With interest we read the recent article by Espinosa-Ortega et al 1 comparing a line immunoassay (LIA) and immunoprecipitation (IP) for the detection of autoantibodies associated with idiopathic inflammatory myopathies (IIM). Historically, most of the clinical associations of myositis specific antibodies (MSAs) and myositis associated antibodies (MAAs) have been established using IP. Consequently, it is important to compare newer technologies such as LIA and dot blots (DB) to IP, which does not imply that IP is correct in all instances or that IP should be regarded as the ‘gold standard’.

In a recent study that compared LIA and IP, poor agreement was found for several MSAs.2 In the letter by Espinosa-Ortega et al, the authors made a similar analysis, comparing IP and LIA in a cohort of 110 patients with IIM. For the LIA, the study also included controls (n=60). A strength of the study is the use of both protein and RNA IP performed in a reference centre for IP. In the interest of assay evaluation and standardisation, it is valuable to provide data showing a more statistics-based approach for method comparison. Due to the rarity of several of the MSAs, large cohorts with thousands of patients are needed to carefully validate the positive agreement between methods. The small number of positive samples results in large 95% CIs (table 1), which makes it difficult to draw true conclusions from the results. For example, in the article of Espinosa-Ortega et al, the 95% CI derived from Kappa statistics for anti-TIF1γ ranged from 0.29 (poor) to 0.83 (excellent). Table 1 summarises the results obtained by Espinosa-Ortega et al 1, Cavazzana et al.2 and Mahler et al. 4

Table 1

Agreement between LIA and IP derived from three studies

In addition, several interesting observations merit further discussion. First, the observation that anti-Mi-2 antibodies do not reflect the known clinical phenotype with skin involvement is interesting and might be related to the assay used. In two recent studies it has been demonstrated that the prevalence of anti-Mi-2 antibodies in polymyositis might be assay dependent.3 4 Second, for TIF1γ it is important to specify how positivity by IP is defined. The original description of anti-TIF1γ with IP reported a double band (p140/p155), corresponding to TIF1α (140 kDa) and TIF1γ (155 kDa). Some laboratories might report both, as based on IP, or only anti-TIF1γ, as based on either the 155 kDa IP band or a secondary test. Most commercial in-vitro diagnostic assays, including LIAs, detect only anti-TIF1γ, which might result in different results. While anti-TIF1γ is associated with cancer, anti-TIF1α co-occurs with anti-TIF1γ and anti-Mi-2 autoantibodies.5 As a matter of fact, about 80% of patients with anti-Mi-2 antibodies also express anti-TIF1α autoantibodies.5 In these patients the clinical phenotype matches the co-occurring MSA, rather than having a separate phenotype. In this context, it should be noted that there is a large variation in anti-TIF1γ reactivity between different LIA methods, with, for example, the Euroimmun LIA detecting more anti-TIF1γ reactivity than the Alphadia DB.6

Espinosa-Ortega et al emphasise the importance of anti-TIF1γ due to its association with cancer; however, other markers such as anti-MDA5 autoantibodies are equally important due to the association with rapidly progressive interstitial lung disease. In addition, the consequences for false positive or false negative results can be different depending on the autoantibody (see table 2).

Similar to a previous study reporting variability between blot assays for anti-Jo-1 antibodies,6 the current letter also reports higher than expected variability for anti-Jo-1 antibodies which is related to the known limitation of IP for the detection of anti-Jo-1 antibodies, due to the relatively thin uncharacteristic band in IP (in contrast to other aminoacyl-tRNA synthetases) and to the co-migration with the IgG heavy chain.7

IP analysis of RNA components to confirm the presence of tRNA is helpful; however, it does not reveal the tRNA identity; it only indicates IP of ‘some’ tRNA and cannot distinguish between histidyl-tRNA and other tRNAs. Practically, if a band corresponding to a~50 kD protein is observed on IP, and tRNA by RNA analysis is present, it is reasonable to report anti-Jo-1 antibody positivity.7 Seven out of 18 anti-Jo-1 positive samples, as detected by LIA were negative by IP. Three of them tested positive by a fluorescence enzyme immunoassay (FEIA), one borderline and one negative. It appears that IP misses true positive anti-Jo-1 samples that are detected by LIA and FEIA. This last observation is in line with the concept that IP should not be regarded as a gold standard in all instances. Although known and used for decades, IP is by no means standardised across different laboratories which can lead to significant variability. Consequently, site to site comparison studies for IP are needed, followed by a standardisation approach.

As pointed out above, the rarity of several of the MSAs requires large cohorts with thousands of patients in order to carefully validate agreement (especially positive per cent agreement) between methods. A viable and more realistic approach is to select positive samples from clinically well-characterised patients and complement those with also well characterised controls and test this cohort with various methods and platforms, including IP and LIA. Although this introduces a biassed inclusion criterion, it might provide more meaningful data. Along those lines, a close collaboration between clinicians, patient groups, research networks and kit manufacturers are mandatory to make serum samples available for validation, calibration and quality control. An alternative approach for calibration and quality control is the generation of human or humanised monoclonal antibodies. Standardisation is imperative for the consideration of additional MSAs (in addition to anti-Jo-1) as part of future classification criteria for IIM.8–10 To conclude, we thank the authors for conducting this study and encourage future studies with larger patient cohorts (such as MyoNet/EuroMyositis), ideally including different novel methods for the detection of MSA next to IP.11

Table 2

Overview of myositis specific antibodies and contribution to clinical decisions.

Ethics statements



  • Contributors All authors contributed to the correspondence.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests MM is employee of Inova Diagnostics. However, no product of the company is mentioned in the correspondence.

  • Provenance and peer review Not commissioned; internally peer reviewed.

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