Hubbard Erika, Bachali Prathyusha, Grammer Amrie C, Lipsky Peter E
Bioinformatics and Biostatistics, The George Washington University, Washington, District of Columbia, USA
AMPEL BioSolutions LLC, Charlottesville, Virginia, USA.
Lupus Sci Med. 2025 May 13;12(1):e001526. doi: 10.1136/lupus-2025-001526.
We previously described a classification system of persons with SLE based on whole blood RNA profiles and a random forest (RF) algorithm to predict individual patient endotypes. Here, we apply this algorithm prospectively in an independent set of patients to validate its use as a staging biomarker.
Whole blood from 101 patients participating in three clinical trials (NCT03626311, NCT03180021 and NCT05845593) meeting American College of Rheumatology (ACR) or Systemic Lupus Collaborating Clinics (SLICC) criteria for SLE classification was obtained at baseline, and RNA isolated and sequenced. Gene expression values were used as input to gene set variation analysis (GSVA), and the RF algorithm was applied using GSVA enrichment scores of 32 informative gene sets as input. Composite scores summarising gene expression perturbations were assigned to each patient using a ridge logistic regression algorithm.
Patients with SLE were subset into eight endotypes identified by the algorithm. Patterns of gene enrichment in the identified endotypes mirrored those found in the previously reported endotypes. Differences in clinical characteristics, including serum complement levels, autoantibody positivity and the presence of nephritis, were observed between patients in various endotypes. Patients with active, concurrent nephritis were disproportionately assigned to the more molecularly perturbed endotypes. Composite scores were significantly, but modestly, inversely correlated with complement but not SLE Disease Activity Index (SLEDAI) or anti-double-stranded DNA antibody (anti-dsDNA) titre.
The identification of eight molecular endotypes of lupus based on whole blood gene expression was validated in an independent data set of diverse patients. Endotyping patients with SLE based on transcriptional profiles can provide important status (presence of nephritis) information and provide novel molecular insights in support of personalised management.
我们之前描述了一种基于全血RNA谱和随机森林（RF）算法的系统性红斑狼疮（SLE）患者分类系统，以预测个体患者的内型。在此，我们前瞻性地将该算法应用于一组独立的患者，以验证其作为分期生物标志物的用途。
从101名参与三项临床试验（NCT03626311、NCT03180021和NCT05845593）且符合美国风湿病学会（ACR）或系统性红斑狼疮协作诊所（SLICC）SLE分类标准的患者中获取基线全血，分离并测序RNA。基因表达值用作基因集变异分析（GSVA）的输入，使用32个信息性基因集的GSVA富集分数作为输入应用RF算法。使用岭逻辑回归算法为每位患者分配总结基因表达扰动的综合分数。
SLE患者被该算法分为八种内型。所确定内型中的基因富集模式与先前报道的内型中的模式相似。在不同内型的患者之间观察到临床特征的差异，包括血清补体水平、自身抗体阳性和肾炎的存在。患有活动性并发肾炎的患者不成比例地被分配到分子扰动更大的内型。综合分数与补体显著但适度负相关，与SLE疾病活动指数（SLEDAI）或抗双链DNA抗体（抗dsDNA）滴度无关。
基于全血基因表达对狼疮的八种分子内型的识别在一组独立的不同患者数据集中得到了验证。基于转录谱对SLE患者进行内型分类可以提供重要的状态（肾炎的存在）信息，并为个性化管理提供新的分子见解。
