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Response to: ‘Correspondence on ‘Machine learning algorithms reveal unique gene expression profiles in muscle biopsies from patients with different types of myositis’’ by Takanashi et al
  1. Iago Pinal-Fernandez1,2,3,
  2. Maria Casal-Dominguez1,2,
  3. Jose Cesar Milisenda4,
  4. Andrew Lee Mammen1,2
  1. 1 Muscle Disease Unit, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, Maryland, USA
  2. 2 Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
  3. 3 Faculty of Health Sciences and Faculty of Computer Science, Multimedia and Telecommunications, Universitat Oberta de Catalunya, Barcelona, Barcelona, Spain
  4. 4 Internal Medicine, Hospital Clinic de Barcelona, Barcelona, Catalunya, Spain
  1. Correspondence to Dr Iago Pinal-Fernandez, Muscle Disease Unit, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, Maryland, USA; iago.pinalfernandez{at}; Dr Andrew Lee Mammen, Muscle Disease Unit, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, Marylan, USA; andrew.mammen{at}

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Thank you for your constructive comments.1 We agree that transcriptomic data from affected muscle tissue have the potential to improve the diagnosis and treatment of inflammatory myopathies.2 First, transcriptomic data may allow us to identify the most relevant inflammatory pathways in a particular patient and thereby individualise therapy. For example, patients with marked upregulation of interferon-induced genes may benefit most from treatment with Janus kinase inhibitors. Second, transcriptomic analysis requires very little muscle tissue while providing a large amount of biological information. Thus, transcriptomic analysis using needle muscle biopsies may be as diagnostically useful as conventional surgical muscle biopsies. Finally, visual interpretation of muscle biopsies is a complicated task that, even when performed by experts, has relatively poor interrater reliability.3 In contrast, the analysis of transcriptomic data is objective and can be automated.

In our study, the presence of interstitial lung disease was almost always present in certain myositis subgroups (anti-MDA54 and anti-Jo15) and almost completely absent in others (anti-Mi2,6 anti-NXP2,7 anti-TIF1g,8 anti-SRP,9 anti-HMGCR,10 IBM11).Thus, the sample size of patients with and without interstitial lung disease within each subgroup was not sufficient to make robust comparisons within subgroups.

We agree that biopsies obtained from different muscles may not have identical histological or transcriptomic features. Indeed, we previously showed that deltoid muscle biopsies tend to have more severe histological abnormalities than muscle biopsies from other locations.12 In the current study, we used muscle biopsies that were obtained for diagnostic purposes by numerous clinicians at several different hospitals. Thus, individual clinicians made decisions about which muscles to biopsy based on factors including the degree of weakness, electromyographic findings and/or imaging features. Since clinicians at these centres do not perform muscle biopsies in completely amyopathic forms of myositis, the muscle biopsies from anti-MDA5 and anti-TIF1γ patients included in our study all came from patients who had muscle weakness.

Although anti-MDA5 and anti-TIF1γ patients are the patients with least myopathic dermatomyositis, 79% of anti-MDA5 patients and 81% of anti-TIF1γ patients develop clinically detectable weakness during follow-up. Furthermore, we have shown that the level of expression of interferon-inducible genes in anti-MDA5 and anti-TIF1γ was equivalent to that in the more myopathic forms of dermatomyositis (ie, those with anti-Mi2 and anti-NXP2 autoantibodies).13 Also, we have verified that there is a positive correlation between the expression of the interferon-inducible genes and the level of muscle weakness.13 Thus, it will be interesting to know if these inflammatory patterns are still detectable in completely amyopathic patients.

In our recent study, about half of muscle biopsies were from the quadriceps, 1/6 from the biceps and 1/3 from the deltoid. Nonetheless, independent of what muscle was biopsied, the key genes used by the machine learning algorithm to classify the muscle biopsies had similar magnitudes of expression (online supplemental figure 1). While further studies will be needed, this observation suggests that transcriptomic data from any of these affected muscles may be adequate for diagnostic purposes and identifying pathologically relevant pathways. Notwithstanding this, there were striking transcriptomic differences between biopsies obtained from different muscles independent of the myositis subgroup (table 1). Perhaps not surprisingly, the biggest differences were founded in homeobox genes that control morphogenesis. In fact, using the differentially expressed genes between muscle biopsy locations, a linear support vector machine model was able to predict the location of the biopsy with an accuracy of 95% (95% CI 87% to 100%). We hypothesise that the transcriptional differences between muscles may be related to the characteristic patterns of weakness observed in the different types of myositis (eg, inclusion body myositis,11 anti-NXP27 and anti-Pm/Scl14).

Table 1

Top 10 differentially expressed genes according to the location of the muscle biopsy independent of the type of myositis

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Ethics approval

This study was approved by the Institutional Review Boards at participating institutions and written informed consent was obtained from each participant. Muscle biopsies obtained from subjects enrolled in IRB-approved longitudinal cohorts from the NIH (IRB number 91-AR-0196), the Johns Hopkins Myositis CenterCentre (IRB number NA_00007454), the Clinic Hospital (Barcelona; IRB number HCB/2015/0479), and the Vall d’Hebron Hospital (Barcelona; IRB number PR (AG) 68/2008).


The authors thank Dr Gustavo Gutierrez-Cruz, Dr Stefania Dell’Orso and Faiza Naz from the NIAMS sequencing facility for all their technical collaboration in making the RNAseq libraries and sequencing them, and the University of Kentucky Centre for Muscle Biology for providing normal human muscle samples for the study.


Supplementary materials

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  • Contributors All the authors contributed to the design, data acquisition and drafting of this study. All the authors reviewed the final manuscript.

  • Funding This research was supported in part by the Intramural Research Programme of the National Institute of Arthritis and Musculoskeletal and Skin Diseases and the National Institute of Environmental Health Sciences of the National Institutes of Health. The Myositis Research Database and Dr. LC-S are supported by the Huayi and Siuling Zhang Discovery Fund. IPFs research was supported by a Fellowship from the Myositis Association. The authors also thank Dr. Peter Buck for support.

  • Competing interests None declared.

  • Provenance and peer review Commissioned; internally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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