Download RESEARCH GROUP: Inflammatory Diseases Group, Northern

RESEARCH GROUP: Inflammatory Diseases Group, Northern Ireland centre for Stratified Medicine
Project Title: Design & testing of a pharmacogenomic model to inform treatment decisions for
Rheumatoid Arthritis patients
Supervisor(s): David Gibson, Tony Bjourson, Priyank Shukla
Contact Details: [email protected] ; [email protected]; [email protected]
Level: PhD
Background to the project :
Rheumatoid arthritis (RA) is a chronic inflammatory disease which affects up to 1 % of the population
world-wide [1]. The current treatment paradigm for RA involves the sequential use of disease modifying
anti-rheumatic drugs (DMARDS). The efficacy and patient tolerability of each therapy is currently
determined using a trial and error approach. Many patients therefore derive no clinical benefit, yet are
exposed to a spectrum of side effects. Time to effective treatment is known to influence the overall disease
outcome for the patient in terms of disease progression, joint destruction and subsequent disability.
There are currently several key single nucleotide polymorphisms (SNPs) which have been associated with
treatment response outcomes relating to commonly used DMARDs in rheumatology. These include SNPs
within the folate pathway for methotrexate [2-6], within cytokines for hydroxychloroquine [7], effecting
acetylators for sulpsalazine [8, 9], and influencing cytochrome enzyme activity for leflunomide [10].
However, there are no clinically validated genetic tests to direct treatment choice, reduce drug toxicity or
predict response to treatment.
The aim of this research is to design and test the ability of a pharmacogenomic model to stratify RA
patients into treatment groups which maximise drug efficacy and minimise toxicity.
Objectives of the research project :
Hypothesis: A pharmacogenomics driven machine learning model can accurately predict DMARD
therapy response for rheumatoid arthritis patients.
1. To develop a comprehensive database of treatment and outcome data features for rheumatoid arthritis
patients.
2. To define a discrete multiplex panel of single nucleotide polymorphisms (SNPs) and methtlyation status
of specific genes in FACS sorted immune and other cell populations which are known to impact upon
DMARD metabolism and/ or associated with DMARD response, toxicity or adverse events.
3. To develop and test the accuracy of a predictive model, from whole genome sequence data, to aid
DMARD treatment selection, on a response blinded cohort of rheumatoid arthritis patients.
.
Methods to be used :
1. Detailed prescribing, drug efficacy and adverse event information will be extracted from patient medical
notes for rheumatoid arthritis patients currently recruited to the BioRA study of n=391 patients already
recruited (of 696 total planned) and EarlyRA study (306 currently under recruitment).
2. We have already identified a panel of candidate SNPs for the drugs of interest and additional candidates
will be selected using online databases to compliment this. In brief, PharmGKB (Pharmacogenomics
Knowledge Base, www.pharmgkb.org) will be used to identify genes associated with the metabolism of the
relevant DMARDS, Methotrexate, Hydroxychloroquine, Sulphasalazine and Lefluonamide. Furthermore,
information related to SNPS within these genes will be collected and cross referenced from all available
sources (e.g. Ensemble, DbSNP) along with regulatory regions known to be subject to
hyo/hypermethylation. This process will build up a comprehensive table of candidate SNPs of interest
which are prioritised based on their available clinical data, SIFT and PolyPhen SNP consequence
prediction tools, used to refine SNP/methylation targets for each drug. For example preliminary research
for the drug Sulphasalizine, indicates that up to 25 variant annotations impact upon its metabolism, with
allele frequencies ranging from 0.18-0.8 in mixed race rheumatoid arthritis populations. In addition,
hpermethylation of a key
3. Data Analysis
a. Whole genome sequence data generated by third party provider and examined from these and
previously biobanked patient DNA with enriched DMARD response data (n=152*) for the selected panel of
pharmacogenetic features (SNPs, methylation and other data).
b. Data analysis will examine patient group stratification for each drug, and also individual patient case
studies following their treatment pathway and outcomes relative to their pharmacogenetic profile. This data
will be used to develop a predictive pharmacogenomic model for DMARD selection by machine learning
methods.
The analyses will include various Machine Learning algorithms such as Artificial Neural
Networks (ANN), Support Vector Machines (SVM), Hidden Markov Models (HMM), Random Forests (RF),
Decision Trees and their ensemble will be used to develop a computational model for predicting patient
outcomes.
c. An independent treatment and response blinded cohort of rheumatoid arthritis patients, will have DNA
(n=152*) genotyped by targeted sequencing/methylation assay for the presence/absence/status of each
feature. This will result in a prototype targeted assay which can be run in a multiplex fashion on 5-10 DNA
samples on a single chip (assuming 100s of feature targets per sample). The sensitivity and specificity of
the model to accurately predict drug efficacy and tolerability will be assessed.
* Using the Genetic Power Calculator (http://pngu.mgh.harvard.edu/~purcell/gpc/) this study has a 0.80
power to detect a genotype relative risk of 1.5 with an alpha-level of 0.05. The power was evaluated under
the assumption of the following parameters: additive inheritance model, prevalence of DMARD
nonresponse phenotype = 0.4 and average risk allele frequency = 0.30 (Sulphasalazine).
Skills required of applicant :
The applicant should ideally have a background in computational biology with biomedical knowledge,
potential for basic laboratory skills (not essential but would be useful), and good computer programming
skills. The applicant must demonstrate enthusiasm and commitment to work diligently on all aspects of the
research project to completion under the leadership of his/her supervisors.
References :
1. Silman AJ, Pearson JE. Epidemiology and genetics of rheumatoid arthritis. Arthritis Res 4: S265S272 (2002).
2. van Ede, A. E. et al. The C677T mutation in the methylenetetrahydrofolate reductase gene: a
genetic risk factor for methotrexate-related elevation of liver enzymes in rheumatoid arthritis
patients. Arthritis Rheum. 44, 2525–2530 (2001).
3. Urano, W. et al. Polymorphisms in the methylenetetrahydrofolate reductase gene were associated
with both the efficacy and the toxicity of methotrexate used for the treatment of rheumatoid arthritis,
as evidenced by single locus and haplotype analyses. Pharmacogenetics 12, 183–190 (2002).
4. Fisher, M. C. & Cronstein, B. N. Metaanalysis of methylenetetrahydrofolate reductase (MTHFR)
polymorphisms affecting methotrexate toxicity. J. Rheumatol. 36, 539–545 (2009).
5. Lee, Y. H. & Song, G. G. Associations between the C677T and A1298C polymorphisms of MTHFR
and the efficacy and toxicity of methotrexate in rheumatoid arthritis: a meta-analysis. Clin. Drug
Investig. 30, 101–108 (2010).
6. Weisman, M. H. et al. Risk genotypes in folate-dependent enzymes and their association with
methotrexate-related side effects in rheumatoid arthritis. Arthritis Rheum. 54, 607–612 (2006).
7. López, P., Gómez, J., Mozo, L., Gutiérrez, C. & Suárez, A. Cytokine polymorphisms influence
treatment outcomes in SLE patients treated with antimalarial drugs. Arthritis Res. Ther. 8, R42
(2006).
8. Tanaka, E. et al. Adverse effects of sulfasalazine in patients with rheumatoid arthritis are associated
with diplotype configuration at the N‑acetyltransferase 2 gene. J. Rheumatol. 29, 2492–2499
(2002).
9. Taniguchi, A. et al. Validation of the associations between single nucleotide polymorphisms or
haplotypes and responses to disease-modifying antirheumatic drugs in patients with rheumatoid
arthritis: a proposal for prospective pharmacogenomic study in clinical practice. Pharmacogenet.
Genomics 17, 383–390 (2007).
10. Bohanec Grabar, P. et al. Genetic polymorphism of CYP1A2 and the toxicity of leflunomide
treatment in rheumatoid arthritis patients. Eur. J. Clin. Pharmacol. 64, 871–876 (2008).