Gene expression deconvolution for uncovering molecular signatures in response to therapy in juvenile idiopathic arthritis

BBOP Study Consortium

doi:10.1371/journal.pone.0156055

Gene expression deconvolution for uncovering molecular signatures in response to therapy in juvenile idiopathic arthritis

BBOP Study Consortium

Research output: Contribution to journal › Article › peer-review

8 Citations (Scopus)

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
Article number	e0156055
Journal	PLoS One
Volume	11
Issue number	5
DOIs	https://doi.org/10.1371/journal.pone.0156055
Publication status	Published - May 2016
Externally published	Yes

Bibliographical note

Funding Information:
This study was funded by a Natural Sciences and Engineering Research Council (NSERC) operating grant and an Early Researcher Award from the Ontario Research Fund to QM, team grants from the Canadian Institutes of Health Research (CIHR grant numbers 82517 and QNT-69301), Canadian Arthritis Network (SRI-IJD-01), and The Arthritis Society. Additional funding was provided by the University of Saskatchewan, Manitoba Institute of Child Health, McGill University, Memorial University, University of British Columbia, and University of Sherbrooke.

Publisher Copyright:
© 2016 Cui et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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10.1371/journal.pone.0156055

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@article{3fb1c1980bed46efb840ae276c13fc9f,

title = "Gene expression deconvolution for uncovering molecular signatures in response to therapy in juvenile idiopathic arthritis",

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.",

author = "{BBOP Study Consortium} and Ang Cui and Gerald Quon and Rosenberg, {Alan M.} and Yeung, {Rae S.M.} and Quaid Morris and Paul Dancey and Adam Huber and Bianca Lang and Suzanne Ramsey and Elizabeth Stringer and Chetaille, {Anne Laure} and Gilles Boire and Sarah Campillo and Ga{\"e}lle Ch{\'e}deville and Rosie Scuccimarri and Ciar{\'a}n Duffy and Duffy, {Karen Watanabe} and Michele Gibbon and Roman Jurencak and Johannes Roth and Susanne Benseler and Bonnie Cameron and Brian Feldman and Ronald Laxer and Rayfel Schneider and Lynn Spiegel and Shirley Tse and Kiem Oen and Elham Rezaei and Loren Matheson and Janet Ellsworth and Cabral, {David A.} and Jaime Guzman and Kristin Houghton and Ross Petty and Tucker, {Lori B.} and Stuart Turvey",

note = "Funding Information: This study was funded by a Natural Sciences and Engineering Research Council (NSERC) operating grant and an Early Researcher Award from the Ontario Research Fund to QM, team grants from the Canadian Institutes of Health Research (CIHR grant numbers 82517 and QNT-69301), Canadian Arthritis Network (SRI-IJD-01), and The Arthritis Society. Additional funding was provided by the University of Saskatchewan, Manitoba Institute of Child Health, McGill University, Memorial University, University of British Columbia, and University of Sherbrooke. Publisher Copyright: {\textcopyright} 2016 Cui et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.",

year = "2016",

month = may,

doi = "10.1371/journal.pone.0156055",

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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

N1 - Funding Information: This study was funded by a Natural Sciences and Engineering Research Council (NSERC) operating grant and an Early Researcher Award from the Ontario Research Fund to QM, team grants from the Canadian Institutes of Health Research (CIHR grant numbers 82517 and QNT-69301), Canadian Arthritis Network (SRI-IJD-01), and The Arthritis Society. Additional funding was provided by the University of Saskatchewan, Manitoba Institute of Child Health, McGill University, Memorial University, University of British Columbia, and University of Sherbrooke. Publisher Copyright: © 2016 Cui et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

PY - 2016/5

Y1 - 2016/5

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.

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Gene expression deconvolution for uncovering molecular signatures in response to therapy in juvenile idiopathic arthritis

Abstract

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