scholarly journals Machine learning applied to whole‐blood RNA‐sequencing data uncovers distinct subsets of patients with systemic lupus erythematosus

2019 ◽  
Vol 8 (12) ◽  
Author(s):  
William A Figgett ◽  
Katherine Monaghan ◽  
Milica Ng ◽  
Monther Alhamdoosh ◽  
Eugene Maraskovsky ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systemic Lupus Erythematosus ◽  
Rna Sequencing ◽  
Lupus Erythematosus ◽  
Whole Blood ◽  
Sequencing Data ◽  
Systemic Lupus

scholarly journals Machine learning applied to whole-blood RNA-sequencing data uncovers distinct subsets of patients with systemic lupus erythematosus

2019 ◽  
Author(s):  
William A Figgett ◽  
Katherine Monaghan ◽  
Milica Ng ◽  
Monther Alhamdoosh ◽  
Eugene Maraskovsky ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Systemic Lupus Erythematosus ◽  
Clinical Trials ◽  
Lupus Erythematosus ◽  
Whole Blood ◽  
Rna Seq ◽  
Systemic Lupus ◽  
Disease Heterogeneity ◽  
Healthy Donors

ABSTRACTObjectiveSystemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease that is difficult to treat. There is currently no optimal stratification of patients with SLE, and thus responses to available treatments are unpredictable. Here, we developed a new stratification scheme for patients with SLE, based on the whole-blood transcriptomes of patients with SLE.MethodsWe applied machine learning approaches to RNA-sequencing (RNA-seq) datasets to stratify patients with SLE into four distinct clusters based on their gene expression profiles. A meta-analysis on two recently published whole-blood RNA-seq datasets was carried out and an additional similar dataset of 30 patients with SLE and 29 healthy donors was contributed in this research; 141 patients with SLE and 51 healthy donors were analysed in total.ResultsExamination of SLE clusters, as opposed to unstratified SLE patients, revealed underappreciated differences in the pattern of expression of disease-related genes relative to clinical presentation. Moreover, gene signatures correlated to flare activity were successfully identified.ConclusionGiven that disease heterogeneity has confounded research studies and clinical trials, our approach addresses current unmet medical needs and provides a greater understanding of SLE heterogeneity in humans. Stratification of patients based on gene expression signatures may be a valuable strategy to harness disease heterogeneity and identify patient populations that may be at an increased risk of disease symptoms. Further, this approach can be used to understand the variability in responsiveness to therapeutics, thereby improving the design of clinical trials and advancing personalised therapy.

scholarly journals OP0277 RNA SEQUENCING AND MACHINE LEARNING TECHNIQUES PREDICT MAJOR ORGAN INVOLVEMENT IN PATIENTS WITH SYSTEMIC LUPUS ERYTHEMATOSUS

2019 ◽  
Author(s):  
Anastasia Filia ◽  
George Bertsias ◽  
Nikolaos Panousis ◽  
Dionisis Nikolopoulos ◽  
Emmanouil Dermitzakis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systemic Lupus Erythematosus ◽  
Rna Sequencing ◽  
Lupus Erythematosus ◽  
Machine Learning Techniques ◽  
Organ Involvement ◽  
Systemic Lupus ◽  
Learning Techniques ◽  
Major Organ

OP0318 Use of an in Vitro Whole Blood Depletion Assay to Compare the Efficacy of B Cell Depleting Agents in Patients with Systemic Lupus Erythematosus

2013 ◽  
Vol 72 (Suppl 3) ◽  
pp. A162.1-A162 ◽  
Author(s):  
V. Reddy ◽  
G. Cambridge ◽  
D. Isenberg ◽  
M. Cragg ◽  
M. Leandro
Keyword(s):  
Systemic Lupus Erythematosus ◽  
B Cell ◽  
Lupus Erythematosus ◽  
Whole Blood ◽  
Systemic Lupus

scholarly journals Prediction of chronic damage in systemic lupus erythematosus by using machine-learning models

PLoS ONE ◽  
2017 ◽  
Vol 12 (3) ◽  
pp. e0174200 ◽  
Author(s):  
Fulvia Ceccarelli ◽  
Marco Sciandrone ◽  
Carlo Perricone ◽  
Giulio Galvan ◽  
Francesco Morelli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systemic Lupus Erythematosus ◽  
Lupus Erythematosus ◽  
Learning Models ◽  
Systemic Lupus ◽  
Machine Learning Models

scholarly journals Whole blood viscosity and arterial thrombotic events in patients with systemic lupus erythematosus

2007 ◽  
Vol 57 (5) ◽  
pp. 845-850 ◽  
Author(s):  
Stephanie Booth ◽  
Saima Chohan ◽  
James C. Curran ◽  
Theodore Karrison ◽  
Amanda Schmitz ◽  
...  
Keyword(s):  
Systemic Lupus Erythematosus ◽  
Lupus Erythematosus ◽  
Blood Viscosity ◽  
Whole Blood ◽  
Systemic Lupus ◽  
Whole Blood Viscosity

scholarly journals Genomic dissection of Systemic Lupus Erythematosus: Distinct Susceptibility, Activity and Severity Signatures

2018 ◽  
Author(s):  
Nikolaos I. Panousis ◽  
George Bertsias ◽  
Halit Ongen ◽  
Irini Gergianaki ◽  
Maria Tektonidou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Systemic Lupus Erythematosus ◽  
Disease Activity ◽  
Lupus Erythematosus ◽  
Whole Blood ◽  
Personalized Therapy ◽  
Flare Activity ◽  
Rna Seq ◽  
Healthy Individuals ◽  
Systemic Lupus

AbstractRecent genetic and genomics approaches have yielded novel insights in the pathogenesis of Systemic Lupus Erythematosus (SLE) but the diagnosis, monitoring and treatment still remain largely empirical1,2. We reasoned that molecular characterization of SLE by whole blood transcriptomics may facilitate early diagnosis and personalized therapy. To this end, we analyzed genotypes and RNA-seq in 142 patients and 58 matched healthy individuals to define the global transcriptional signature of SLE. By controlling for the estimated proportions of circulating immune cell types, we show that the Interferon (IFN) and p53 pathways are robustly expressed. We also report cell-specific, disease-dependent regulation of gene expression and define a core/susceptibility and a flare/activity disease expression signature, with oxidative phosphorylation, ribosome regulation and cell cycle pathways being enriched in lupus flares. Using these data, we define a novel index of disease activity/severity by combining the validated Systemic Lupus Erythematosus Disease Activity Index (SLEDAI)1 with a new variable derived from principal component analysis (PCA) of RNA-seq data. We also delineate unique signatures across disease endo-phenotypes whereby active nephritis exhibits the most extensive changes in transcriptome, including prominent drugable signatures such as granulocyte and plasmablast/plasma cell activation. The substantial differences in gene expression between SLE and healthy individuals enables the classification of disease versus healthy status with median sensitivity and specificity of 83% and 100%, respectively. We explored the genetic regulation of blood transcriptome in SLE and found 3142 cis-expression quantitative trait loci (eQTLs). By integration of SLE genome-wide association study (GWAS) signals and eQTLs from 44 tissues from the Genotype-Tissue Expression (GTEx) consortium, we demonstrate that the genetic causality of SLE arises from multiple tissues with the top causal tissue being the liver, followed by brain basal ganglia, adrenal gland and whole blood. Collectively, our study defines distinct susceptibility and activity/severity signatures in SLE that may facilitate diagnosis, monitoring, and personalized therapy.

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