Abstract
Gene expression-based signatures help identify pathways relevant to diseases and treatments, but are challenging to construct when there is a diversity of disease mechanisms and treatments in patients with complex diseases. To overcome this challenge, we present a new application of an in silico gene expression deconvolution method, ISOpure-S1, and apply it to identify a common gene expression signature corresponding to response to treatment in 33 juvenile idiopathic arthritis (JIA) patients. Using pre- and post-treatment gene expression profiles only, we found a gene expression signature that significantly correlated with a reduction in the number of joints with active arthritis, a measure of clinical outcome (Spearman rho = 0.44, p = 0.040, Bonferroni correction). This signature may be associated with a decrease in T-cells, monocytes, neutrophils and platelets. The products of most differentially expressed genes include known biomarkers for JIA such as major histocompatibility complexes and interleukins, as well as novel biomarkers including α-defensins. This method is readily applicable to expression datasets of other complex diseases to uncover shared mechanistic patterns in heterogeneous samples.
| Original language | English (US) |
|---|---|
| Article number | e0156055 |
| Journal | PLoS One |
| Volume | 11 |
| Issue number | 5 |
| DOIs | |
| State | Published - May 1 2016 |
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ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)
- Agricultural and Biological Sciences(all)
Cite this
Gene expression deconvolution for uncovering molecular signatures in response to therapy in juvenile idiopathic arthritis. / BBOP Study Consortium.
In: PLoS One, Vol. 11, No. 5, e0156055, 01.05.2016.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Gene expression deconvolution for uncovering molecular signatures in response to therapy in juvenile idiopathic arthritis
AU - BBOP Study Consortium
AU - Cui, Ang
AU - Quon, Gerald
AU - Rosenberg, Alan M.
AU - Yeung, Rae S.M.
AU - Morris, Quaid
AU - Dancey, Paul
AU - Huber, Adam
AU - Lang, Bianca
AU - Ramsey, Suzanne
AU - Stringer, Elizabeth
AU - Chetaille, Anne Laure
AU - Boire, Gilles
AU - Campillo, Sarah
AU - Chédeville, Gaëlle
AU - Scuccimarri, Rosie
AU - Duffy, Ciarán
AU - Duffy, Karen Watanabe
AU - Gibbon, Michele
AU - Jurencak, Roman
AU - Roth, Johannes
AU - Benseler, Susanne
AU - Cameron, Bonnie
AU - Feldman, Brian
AU - Laxer, Ronald
AU - Schneider, Rayfel
AU - Spiegel, Lynn
AU - Tse, Shirley
AU - Oen, Kiem
AU - Rezaei, Elham
AU - Matheson, Loren
AU - Ellsworth, Janet
AU - Cabral, David A.
AU - Guzman, Jaime
AU - Houghton, Kristin
AU - Petty, Ross
AU - Tucker, Lori B.
AU - Turvey, Stuart
PY - 2016/5/1
Y1 - 2016/5/1
N2 - Gene expression-based signatures help identify pathways relevant to diseases and treatments, but are challenging to construct when there is a diversity of disease mechanisms and treatments in patients with complex diseases. To overcome this challenge, we present a new application of an in silico gene expression deconvolution method, ISOpure-S1, and apply it to identify a common gene expression signature corresponding to response to treatment in 33 juvenile idiopathic arthritis (JIA) patients. Using pre- and post-treatment gene expression profiles only, we found a gene expression signature that significantly correlated with a reduction in the number of joints with active arthritis, a measure of clinical outcome (Spearman rho = 0.44, p = 0.040, Bonferroni correction). This signature may be associated with a decrease in T-cells, monocytes, neutrophils and platelets. The products of most differentially expressed genes include known biomarkers for JIA such as major histocompatibility complexes and interleukins, as well as novel biomarkers including α-defensins. This method is readily applicable to expression datasets of other complex diseases to uncover shared mechanistic patterns in heterogeneous samples.
AB - Gene expression-based signatures help identify pathways relevant to diseases and treatments, but are challenging to construct when there is a diversity of disease mechanisms and treatments in patients with complex diseases. To overcome this challenge, we present a new application of an in silico gene expression deconvolution method, ISOpure-S1, and apply it to identify a common gene expression signature corresponding to response to treatment in 33 juvenile idiopathic arthritis (JIA) patients. Using pre- and post-treatment gene expression profiles only, we found a gene expression signature that significantly correlated with a reduction in the number of joints with active arthritis, a measure of clinical outcome (Spearman rho = 0.44, p = 0.040, Bonferroni correction). This signature may be associated with a decrease in T-cells, monocytes, neutrophils and platelets. The products of most differentially expressed genes include known biomarkers for JIA such as major histocompatibility complexes and interleukins, as well as novel biomarkers including α-defensins. This method is readily applicable to expression datasets of other complex diseases to uncover shared mechanistic patterns in heterogeneous samples.
UR - http://www.scopus.com/inward/record.url?scp=84971636107&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84971636107&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0156055
DO - 10.1371/journal.pone.0156055
M3 - Article
C2 - 27244050
AN - SCOPUS:84971636107
VL - 11
JO - PLoS One
JF - PLoS One
SN - 1932-6203
IS - 5
M1 - e0156055
ER -