Eng Simon W M, Olazagasti Jeannette M, Goldenberg Anna, Crowson Cynthia S, Oddis Chester V, Niewold Timothy B, Yeung Rae S M, Reed Ann M
Hospital for Sick Children (SickKids), University of Toronto, Toronto, Ontario, Canada.
Mayo Clinic, Rochester, Minnesota.
ACR Open Rheumatol. 2020 Mar;2(3):158-166. doi: 10.1002/acr2.11115. Epub 2020 Feb 10.
Published predictive models of disease outcomes in idiopathic inflammatory myopathies (IIMs) are sparse and of limited accuracy due to disease heterogeneity. Computational methods may address this heterogeneity by partitioning patients based on clinical and biological phenotype.
To identify new patient groups, we applied similarity network fusion (SNF) to clinical and biological data from 168 patients with myositis (64 adult polymyositis [PM], 65 adult dermatomyositis [DM], and 39 juvenile DM [JDM]) in the Rituximab in Myositis trial. We generated a sparse proof-of-concept bedside classifier using multinomial regression and identified characteristics that distinguished these groups. We conducted χ tests to link new patient groups with the myositis subtypes.
SNF identified five patient groups in the discovery cohort that subdivided the myositis subtypes. The sparse multinomial regressor to predict patient group assignments (areas under the receiver operating characteristic curve = [0.78, 0.97]; areas under the precision-recall curve = [0.55, 0.96]) found that autoantibody enrichment defined four of these groups: anti-Mi-2, anti-signal recognition peptide (SRP), anti-nuclear matrix protein 2 (NXP2), and anti-synthetase (Syn). Depletion of immunoglobulin M (IgM) defined the fifth group. Each group was associated with one subtype, with adult DM being associated with anti-Mi-2 and anti-Syn autoantibodies, JDM being associated with anti-NXP2 autoantibodies, and adult PM being associated with IgM depletion and anti-SRP autoantibodies. These associations enabled us to further resolve the current myositis subtypes.
Using unsupervised machine learning, we identified clinically and biologically homogeneous groups of patients with IIMs, forming the basis of an integrated disease classification based on both clinical and biological phenotype, thus validating other approaches and what has been previously described.
由于特发性炎性肌病（IIM）的疾病异质性，已发表的该疾病预后预测模型稀少且准确性有限。计算方法可通过根据临床和生物学表型对患者进行分组来解决这种异质性问题。
为了识别新的患者群体，我们将相似性网络融合（SNF）应用于肌炎利妥昔单抗试验中168例肌炎患者（64例成人多发性肌炎[PM]、65例成人皮肌炎[DM]和39例青少年皮肌炎[JDM]）的临床和生物学数据。我们使用多项回归生成了一个稀疏的概念验证床边分类器，并确定了区分这些群体所需的特征。我们进行了χ检验，以将新的患者群体与肌炎亚型联系起来。
SNF在发现队列中识别出五个患者群体，这些群体细分了肌炎亚型。用于预测患者群体归属的稀疏多项回归器（受试者操作特征曲线下面积 = [0.78, 0.97]；精确召回率曲线下面积 = [0.55, 0.96]）发现自身抗体富集定义了其中四个群体：抗Mi-2、抗信号识别颗粒（SRP）、抗核基质蛋白2（NXP2）和抗合成酶（Syn）。免疫球蛋白M（IgM）的耗竭定义了第五个群体。每个群体都与一种亚型相关，成人DM与抗Mi-2和抗Syn自身抗体相关，JDM与抗NXP2自身抗体相关，成人PM与IgM耗竭和抗SRP自身抗体相关。这些关联使我们能够进一步细化当前的肌炎亚型。
通过无监督机器学习，我们识别出了临床上和生物学上均一的IIM患者群体，形成了基于临床和生物学表型的综合疾病分类基础，从而验证了其他方法以及先前描述的内容。
