Department of Medicine, Division of Rheumatology, Hospital for Special Surgery, New York, New York, USA
Department of Medicine, Division of Rheumatology, Hospital for Special Surgery, New York, New York, USA.
Ann Rheum Dis. 2021 Feb;80(2):228-237. doi: 10.1136/annrheumdis-2020-217840. Epub 2020 Oct 7.
We sought to determine histologic and gene expression features of clinical improvement in early diffuse cutaneous systemic sclerosis (dcSSc; scleroderma).
Fifty-eight forearm biopsies were evaluated from 26 individuals with dcSSc in two clinical trials. Histologic/immunophenotypic assessments of global severity, alpha-smooth muscle actin (aSMA), CD34, collagen, inflammatory infiltrate, follicles and thickness were compared with gene expression and clinical data. Support vector machine learning was performed using scleroderma gene expression subset (normal-like, fibroproliferative, inflammatory) as classifiers and histology scores as inputs. Comparison of w-vector mean absolute weights was used to identify histologic features most predictive of gene expression subset. We then tested for differential gene expression according to histologic severity and compared those with clinical improvement (according to the Combined Response Index in Systemic Sclerosis).
aSMA was highest and CD34 lowest in samples with highest local Modified Rodnan Skin Score. CD34 and aSMA changed significantly from baseline to 52 weeks in clinical improvers. CD34 and aSMA were the strongest predictors of gene expression subset, with highest CD34 staining in the normal-like subset (p<0.001) and highest aSMA staining in the inflammatory subset (p=0.016). Analysis of gene expression according to CD34 and aSMA binarised scores identified a 47-gene fibroblast polarisation signature that decreases over time only in improvers (vs non-improvers). Pathway analysis of these genes identified gene expression signatures of inflammatory fibroblasts.
CD34 and aSMA stains describe distinct fibroblast polarisation states, are associated with gene expression subsets and clinical assessments, and may be useful biomarkers of clinical severity and improvement in dcSSc.
我们旨在确定早期弥漫性皮肤系统性硬化症（dcSSc；硬皮病）临床改善的组织学和基因表达特征。
对两项临床试验中 26 名 dcSSc 患者的 58 份前臂活检标本进行评估。对整体严重程度、α-平滑肌肌动蛋白（aSMA）、CD34、胶原蛋白、炎症浸润、滤泡和厚度的组织学/免疫表型评估与基因表达和临床数据进行比较。使用 Scleroderma 基因表达亚组（正常样、纤维增生性、炎症性）作为分类器，组织学评分作为输入，进行支持向量机学习。比较 w-向量的平均绝对权重，以确定最能预测基因表达亚组的组织学特征。然后根据组织学严重程度检测差异基因表达，并将其与临床改善（根据系统性硬化症的综合反应指数）进行比较。
在局部改良 Rodnan 皮肤评分最高的样本中，aSMA 最高，CD34 最低。在临床改善者中，CD34 和 aSMA 从基线到 52 周显著变化。CD34 和 aSMA 是基因表达亚组的最强预测因子，正常样亚组的 CD34 染色最高（p<0.001），炎症亚组的 aSMA 染色最高（p=0.016）。根据 CD34 和 aSMA 二值评分分析基因表达，确定了一个仅在改善者中随时间减少的 47 个基因成纤维细胞极化特征。对这些基因的通路分析确定了炎症成纤维细胞的基因表达特征。
CD34 和 aSMA 染色描述了不同的成纤维细胞极化状态，与基因表达亚组和临床评估相关，可能是 dcSSc 临床严重程度和改善的有用生物标志物。
