Response to: 'Correspondence on 'Machine learning algorithms reveal unique gene expression profiles in muscle biopsies from patients with different types of myositis' by Takanashi

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 …