AMPEL BioSolutions, LLC, 250 W. Main St. #300, Charlottesville, VA, 22902, USA.
RILITE Research Institute, Charlottesville, VA, 22902, USA.
Genome Med. 2023 Oct 16;15(1):84. doi: 10.1186/s13073-023-01237-9.
Systemic lupus erythematosus (SLE) is known to be clinically heterogeneous. Previous efforts to characterize subsets of SLE patients based on gene expression analysis have not been reproduced because of small sample sizes or technical problems. The aim of this study was to develop a robust patient stratification system using gene expression profiling to characterize individual lupus patients.
We employed gene set variation analysis (GSVA) of informative gene modules to identify molecular endotypes of SLE patients, machine learning (ML) to classify individual patients into molecular subsets, and logistic regression to develop a composite metric estimating the scope of immunologic perturbations. SHapley Additive ExPlanations (SHAP) revealed the impact of specific features on patient sub-setting.
Using five datasets comprising 2183 patients, eight SLE endotypes were identified. Expanded analysis of 3166 samples in 17 datasets revealed that each endotype had unique gene enrichment patterns, but not all endotypes were observed in all datasets. ML algorithms trained on 2183 patients and tested on 983 patients not used to develop the model demonstrated effective classification into one of eight endotypes. SHAP indicated a unique array of features influential in sorting individual samples into each of the endotypes. A composite molecular score was calculated for each patient and significantly correlated with standard laboratory measures. Significant differences in clinical characteristics were associated with different endotypes, with those with the least perturbed transcriptional profile manifesting lower disease severity. The more abnormal endotypes were significantly more likely to experience a severe flare over the subsequent 52 weeks while on standard-of-care medication and specific endotypes were more likely to be clinical responders to the investigational product tested in one clinical trial analyzed (tabalumab).
Transcriptomic profiling and ML reproducibly separated lupus patients into molecular endotypes with significant differences in clinical features, outcomes, and responsiveness to therapy. Our classification approach using a composite scoring system based on underlying molecular abnormalities has both staging and prognostic relevance.
系统性红斑狼疮（SLE）已知具有临床异质性。由于样本量小或技术问题，以前基于基因表达分析来描述 SLE 患者亚群的努力尚未得到重现。本研究旨在通过基因表达谱分析开发一种稳健的患者分层系统，以描述个体狼疮患者。
我们采用信息基因模块的基因集变异分析（GSVA）来识别 SLE 患者的分子内型，使用机器学习（ML）将个体患者分类为分子亚群，并使用逻辑回归开发一种估计免疫扰动范围的综合指标。Shapley 加法解释（SHAP）揭示了特定特征对患者亚群划分的影响。
使用包含 2183 名患者的五个数据集，确定了 8 个 SLE 内型。在 17 个数据集的 3166 个样本中进行的扩展分析表明，每个内型都有独特的基因富集模式，但并非所有内型都在所有数据集中观察到。在 2183 名患者上训练并在 983 名未用于开发模型的患者上测试的 ML 算法，有效地将其分类为 8 个内型之一。SHAP 表明，在将个体样本分类到每个内型中时，有一组独特的特征具有影响力。为每个患者计算了一个综合分子评分，与标准实验室测量显著相关。不同的临床特征与不同的内型相关，转录谱最正常的患者表现出较低的疾病严重程度。在标准治疗药物治疗期间，更异常的内型更有可能在随后的 52 周内经历严重的发作，并且在分析的一项临床试验中，特定的内型更有可能对研究产品有临床反应（tabalumab）。
转录组谱和 ML 可重复性地将狼疮患者分为具有显著临床特征、结局和对治疗反应不同的分子内型。我们使用基于潜在分子异常的综合评分系统的分类方法具有分期和预后相关性。
