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| Status |
Public on Oct 15, 2018 |
| Title |
Longitudinal Stratification of Gene Expression Data Reveals Three SLE Groups of Disease Activity Progression. |
| Organism |
Homo sapiens |
| Experiment type |
Expression profiling by array
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| Summary |
The highly heterogeneous clinical presentation of lupus is characterized by the unpredictable appearance of flares of disease activity and important organ damage. Attempts to stratify lupus patients have been limited to clinical information, leading to unsuccessful clinical trials and controversial research results. Our aim was to develop and validate a robust method to stratify patients with lupus according to longitudinal disease activity and whole-genome gene expression data in order to establish subgroups of patients who share disease progression mechanisms.
We applied a clustering-based approach to stratify SLE patients based on the correlation between disease activity scores and longitudinal gene expression information. Clustering robustness was evaluated by bootstrapping and the clusters were characterized in terms of clinical and functional features.Using two independent sets of patients, one pediatric and another adult, our results show a clear partition into three different disease clusters not influenced by treatment, race or other source of bias. Two of the clusters differentiate into a neutrophil correlated disease group and a lymphocyte correlated disease group, while the third that correlated to a lesser extent with neutrophils, was functionally more heterogeneous. The neutrophil-driven clusters were associated with increased development towards proliferative nephritis.
We found three subgroups of patients that show different mechanisms of disease progression and are clinically differentiated. Our results have important implications for treatment options, the design of clinical trials, the etiology of the disease, and the prediction of severe glomerulonephritis.
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| Overall design |
We calculate a correlation matrix between each gene and the activity of the disease through the different visits for each patient. We select the best genes to stratify patients and perform clustering analysis. Finally, the clinical characterization of each cluster obtained was carried out.
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| Contributor(s) |
Toro-Dominguez D, Martorell-Marugán J, Goldman D, Petri M, Carmona-Sáez P, Alarcón-Riquelme ME |
| Citation(s) |
30626389, 29938934 |
| Submission date |
Oct 15, 2018 |
| Last update date |
Feb 15, 2019 |
| Contact name |
Daniel Toro |
| E-mail(s) |
danieltorodominguez@gmail.com
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| Organization name |
GENYO
|
| Department |
Bioinformatics
|
| Street address |
Avenida de la Ilustracion
|
| City |
GRANADA |
| State/province |
Spain |
| ZIP/Postal code |
18016 |
| Country |
Spain |
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| Platforms (1) |
| GPL13158 |
[HT_HG-U133_Plus_PM] Affymetrix HT HG-U133+ PM Array Plate |
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| Samples (312)
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| Relations |
| BioProject |
PRJNA496433 |
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