Abstract

Long-term outcome in patients with rheumatoid arthritis (RA) is highly dependent upon aggressive pharmacological control of inflammation early in the disease course. Despite the importance of selecting the optimal medication soon after disease onset, there is no clinical or biomarker predictor of drug treatment response. A genetic biomarker would be particularly useful for drugs that block the inflammatory cytokine TNF-alpha (TNF), as these drugs are first-line biological disease modifying anti-rheumatic drugs DMARDs, yet induce remission in only ~30% of patients. In this application, our central hypothesis is that common genetic variants of modest effect size predict response to anti-TNF therapy. To test this hypothesis, we propose to expand upon our established multi-center collaboration and available GWAS data to develop (i) new statistical methods for conducting GWAS (estimating variance explained by common single nucleotide polymorphisms, SNPs), (ii) new informatics methods for defining treatment response in the EMR (which will allow us to collect many more samples for GWAS), and (iii) a novel framework for testing mechanism directly in human immune cells.
Aim 1 : Analyze GWAS data on ~1,200 RA patients to search for common variants that predict response to anti-TNF therapy.
Aim 2 : Use electronic medical records (EMR) at Partners HealthCare, Vanderbilt and Northwestern to define treatment response, and conduct a GWAS on ~1,200 additional RA patients treated with anti-TNF therapy.
Aim 3 : Test mechanism of action of alleles that predict treatment response to anti-TNF therapy in human immune cells.

Public Health Relevance

A long-term goal of understanding the genetic basis treatment response in patients with rheumatoid arthritis (RA) is to improve care of patients with this common and debilitating disease. In theory, identifying specific pieces of DNA (alleles) that predict treatment response should aid in targeting therapy to the right individuals early in the course of disease before bone destruction occurs.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01GM092691-05
Application #
8691893
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Long, Rochelle M
Project Start
2010-07-01
Project End
2015-06-30
Budget Start
2014-07-01
Budget End
2015-06-30
Support Year
5
Fiscal Year
2014
Total Cost
Indirect Cost

  Institution

Name
Brigham and Women's Hospital
Department
Type
DUNS #
City
Boston
State
MA
Country
United States
Zip Code
02115

  Publications

Hinks, Anne; Marion, Miranda C; Cobb, Joanna et al. (2018) Brief Report: The Genetic Profile of Rheumatoid Factor-Positive Polyarticular Juvenile Idiopathic Arthritis Resembles That of Adult Rheumatoid Arthritis. Arthritis Rheumatol 70:957-962
Mizoguchi, Fumitaka; Slowikowski, Kamil; Wei, Kevin et al. (2018) Functionally distinct disease-associated fibroblast subsets in rheumatoid arthritis. Nat Commun 9:789
Davenport, Emma E; Amariuta, Tiffany; Gutierrez-Arcelus, Maria et al. (2018) Discovering in vivo cytokine-eQTL interactions from a lupus clinical trial. Genome Biol 19:168
Slowikowski, Kamil; Wei, Kevin; Brenner, Michael B et al. (2018) Functional genomics of stromal cells in chronic inflammatory diseases. Curr Opin Rheumatol 30:65-71
Budu-Aggrey, Ashley; Bowes, John; Stuart, Philip E et al. (2017) A rare coding allele in IFIH1 is protective for psoriatic arthritis. Ann Rheum Dis 76:1321-1324
Hinks, A; Bowes, J; Cobb, J et al. (2017) Fine-mapping the MHC locus in juvenile idiopathic arthritis (JIA) reveals genetic heterogeneity corresponding to distinct adult inflammatory arthritic diseases. Ann Rheum Dis 76:765-772
Cui, Jing; Diogo, Dorothee; Stahl, Eli A et al. (2017) Brief Report: The Role of Rare Protein-Coding Variants in Anti-Tumor Necrosis Factor Treatment Response in Rheumatoid Arthritis. Arthritis Rheumatol 69:735-741
Karnes, Jason H; Bastarache, Lisa; Shaffer, Christian M et al. (2017) Phenome-wide scanning identifies multiple diseases and disease severity phenotypes associated with HLA variants. Sci Transl Med 9:
Rao, Deepak A; Gurish, Michael F; Marshall, Jennifer L et al. (2017) Pathologically expanded peripheral T helper cell subset drives B cells in rheumatoid arthritis. Nature 542:110-114
Fonseka, Chamith Y; Rao, Deepak A; Raychaudhuri, Soumya (2017) Leveraging blood and tissue CD4+ T cell heterogeneity at the single cell level to identify mechanisms of disease in rheumatoid arthritis. Curr Opin Immunol 49:27-36

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