Taroni Jaclyn N, Martyanov Viktor, Mahoney J Matthew, Whitfield Michael L
Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA.
Department of Neurological Sciences, Larner College of Medicine, University of Vermont, Burlington, Vermont, USA.
J Invest Dermatol. 2017 May;137(5):1033-1041. doi: 10.1016/j.jid.2016.12.007. Epub 2016 Dec 21.
Systemic sclerosis is an orphan, systemic autoimmune disease with no FDA-approved treatments. Its heterogeneity and rarity often result in underpowered clinical trials making the analysis and interpretation of associated molecular data challenging. We performed a meta-analysis of gene expression data from skin biopsies of patients with systemic sclerosis treated with five therapies: mycophenolate mofetil, rituximab, abatacept, nilotinib, and fresolimumab. A common clinical improvement criterion of -20% or -5 modified Rodnan skin score was applied to each study. We applied a machine learning approach that captured features beyond differential expression and was better at identifying targets of therapies than the differential expression alone. Regardless of treatment mechanism, abrogation of inflammatory pathways accompanied clinical improvement in multiple studies suggesting that high expression of immune-related genes indicates active and targetable disease. Our framework allowed us to compare different trials and ask if patients who failed one therapy would likely improve on a different therapy, based on changes in gene expression. Genes with high expression at baseline in fresolimumab nonimprovers were downregulated in mycophenolate mofetil improvers, suggesting that immunomodulatory or combination therapy may have benefitted these patients. This approach can be broadly applied to increase tissue specificity and sensitivity of differential expression results.
系统性硬化症是一种罕见的全身性自身免疫性疾病，尚无美国食品药品监督管理局（FDA）批准的治疗方法。其异质性和罕见性常常导致临床试验样本量不足，使得相关分子数据的分析和解读具有挑战性。我们对接受霉酚酸酯、利妥昔单抗、阿巴西普、尼洛替尼和氟司洛单抗五种疗法治疗的系统性硬化症患者皮肤活检的基因表达数据进行了荟萃分析。每项研究都采用了-20%或改良Rodnan皮肤评分降低5分这一常见的临床改善标准。我们应用了一种机器学习方法，该方法能够捕捉差异表达之外的特征，并且在识别治疗靶点方面比单纯的差异表达表现更好。无论治疗机制如何，多项研究表明，炎症途径的消除伴随着临床改善，这表明免疫相关基因的高表达表明疾病处于活跃且可靶向治疗的状态。我们的框架使我们能够比较不同的试验，并根据基因表达的变化询问，在一种疗法中治疗失败的患者是否可能在另一种疗法中得到改善。在接受氟司洛单抗治疗但无改善的患者中，基线时高表达的基因在接受霉酚酸酯治疗且有改善的患者中被下调，这表明免疫调节或联合治疗可能使这些患者受益。这种方法可以广泛应用，以提高差异表达结果的组织特异性和敏感性。
