Linking biobanks to registries: Why and how? Anne Barton

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1 Linking biobanks to registries: Why and how? Anne Barton

2 Biobanks why should we collect samples?

3 Anti-TNF treatment in RA Cost approx. 8,000/person/year 30-40% RA patients do not respond Rare, serious adverse events Response likely to be multi-factorial

4 Predictors of treatment efficacy

5 Overall response prediction Several disease-related factors are predictive of anti-tnf response Concurrent DMARD therapy Higher baseline HAQ Score Female gender RF/Anti-CCP R 2 =0.17

6 Why should we collect samples? Clinical factors alone insufficient to predict efficacy Better targeting of treatment = more cost-effective Avoids potential harm in those unlikely to respond Clues as to mechanism of non-response?different sub-phenotypes? Different biological pathways

7 Transcriptome Epigenetic Genetic Adherence Treatment Response

8 Overall hypothesis Disease-related clinical, demographic, serological, psychological and genetic factors define specific subgroups of patients who are more or less likely to respond to anti-tnf therapy 1) Class effect 2) Individual drug effect

9 Biobanks for pharmacogenetics

10 Pharmacogenetics Hypothesis: Genetic factors influence treatment response Advantage of genetic predictors Stable can collect after treatment has started / finished Easy to assess Clues about causality

11 The British Society for Rheumatology Biologics Register BSRBR BR A national register of patients with rheumatic diseases in the UK receiving biologic therapy (up to ceiling of n=4000 per drug) All hospitals in the UK Commenced 2001 Primary aim: assess long-term safety and efficacy Watson et al, 2007

12 Biologics in Rheumatoid Arthritis Genetics and Genomics Study Syndicate Aim of BRAGGSS Investigate genetic predictors of response to anti- TNF therapy Large nationwide multi-centre collaboration Recruited patients registered with BSRBR Target: recruit 4,000 RA patients treated with etanercept, infliximab or adalimumab

13 Patient cohort Patients identified from BSRBR register Actively involved in the BSRBR Consultant based RA status Baseline and 6/12 follow-up DAS28 score Caucasian Recruitment and blood sample collection through mail correspondence (COREC 04/Q1403/37)

14 Patient recruitment Stage Numbers (%*) Centres recruited 54 Patients contacted 3965 Patients responded 3194 (81%) Patients participating 2921 (74%) Bloods received 2590 * % compared to those initially contacted

15 GWAS of anti-tnf response Plant et al 2011: GWAS 566 UK patients WTCCC 5 loci identified, none replicated Krintel et al 2012 N = 196 anti-tnf treated Danish subjects No genome-wide hits Replication of PDE3A-SLCO1C1 in Spanish cohort (n ~350) with EULAR response

16 Mirkov et al 2012 GWAS 882 Dutch patients 8 loci identified None replicated, yet Cui et al 2013: GWAS 2,700 CD84 identified, p = 8 x 10-8 Etanercept-treated

17 Candidate genes Conflicting evidence for association of TNF -308 Recent meta-analysis: no association PTPRC Reported by Cui et al with good/poor response Replicated by Plant et al Not replicated by CORRONA; Dutch GWAS

18 Role of genetics? Genetic studies have provided little supportive evidence Lack of power to detect modest effects Treatment response has little/no genetic component The measure of response (DAS28) is inappropriate

19 Predictors of toxicity

20 Anti-TNF related SAE TB monoclonals, ethnicity New-onset SLE Serious infection especially in first 6 months New-onset psoriasis Septic arthritis Non-melanoma skin cancer Infliximab Herpes Zoster Transfusion reactions

21 Why collect samples for toxicity outcomes? To target treatment better Single studies unlikely to be able to address rare SAE outcomes

22 Toxicity studies Flucloxacillin induced hepatotoxicity HLA-DRB*5701, OR > 80 Carbamazepine-induced Stevens Johnson syndrome HLA B*1502, OR ~100 Azathioprine-induced bone marrow suppression TPMT gene polymorphisms

23 Summary of toxicity predictors No studies yet undertaken Will require international collaboration to achieve sample sizes?targeted collection based on register information

24 The way forward Larger sample sizes Collaboration Prospective studies Account for confounders Test other types of predictors Anti-drug antibodies Combined algorithm

25 Biobanks how to collect

26 When to collect? Ideally prospectively Prior to biologic administration Collect multiple sample types Collect detailed clinical data Allows development of biological response signatures

27 Epigenetics in treatment response Ideal for studies of treatment response DNA methylation relatively stable Amenable to whole genome approaches Baseline status / change in status

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29 Transcriptomic studies Expression studies used to identify response predictors in breast cancer Tamoxifen: ER expression Herceptin: IFN gene expression signature reported as predictive of response to RTX

30 1 Normalised CD11c Expression Relative CD11c Expression in Non-Responders and Responders Whole genome microarrays RNA-seq Response Non-Responders Responders

31 Samples DNA EDTA Allows genetic and methylation studies RNA Paxgene vs tempus

32 Serum Proteomics / autoantibodies / metabolomics If postal collection, only stable markers can be analysed?urine / faecal collection (microbiome) Postal collection Protocols available ( anne.barton@manchester.ac.uk)

33 Costs Tempus EDTA 9p Serum 9p Plastic holders 70p Blood box - 1 Labels 7p A4 envelopes 2p Pre-payment for postage 18p

34 Acknowledgements Dr. Darren Plant Biologics in Rheumatoid Arthritis Genetics & Genomics Study Syndicate: Prof. Ann Morgan Prof. Anthony G Wilson Prof. John Isaacs Dr. Kimme Hyrich