Top Banner
Linking biobanks to registries: Why and how? Anne Barton
34

Linking biobanks to registries: Why and how? Barton.pdf · Anne Barton . Biobanks – why should we collect samples? Anti-TNF treatment in RA • Cost approx. £8,000/person/year

Oct 20, 2020

Download

Documents

dariahiddleston
Welcome message from author
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
  • Linking biobanks to registries:

    Why and how?

    Anne Barton

  • Biobanks – why should we collect

    samples?

  • 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

  • Predictors of treatment efficacy

  • 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

    R2 =0.17

  • 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

  • Treatment Response

    Genetic

    Epigenetic Transcriptome

    Adherence

  • 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

  • Biobanks for pharmacogenetics

  • 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

  • • 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

    BSRBRBSBSRRBRBRBSRBRBSBSRRBRBRThe British Society for Rheumatology

    Biologics Register

    Watson et al, 2007

  • 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

  • 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)

  • Patient recruitment

    * % compared to those initially contacted

    Stage Numbers (%*)

    Centres recruited 54

    Patients contacted 3965

    Patients responded 3194 (81%)

    Patients participating 2921 (74%)

    Bloods received 2590

  • 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

  • • 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

  • 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

  • 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

  • Predictors of toxicity

  • 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

  • Why collect samples for toxicity

    outcomes?

    • To target treatment better

    • Single studies unlikely to be able to address rare

    SAE outcomes

  • 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

  • Summary of toxicity predictors

    • No studies yet undertaken

    • Will require international collaboration to achieve

    sample sizes

    • ?Targeted collection based on register information

  • The way forward

    • Larger sample sizes

    – Collaboration

    • Prospective studies

    – Account for confounders

    – Test other types of predictors

    – Anti-drug antibodies

    • Combined algorithm

  • Biobanks – how to collect

  • When to collect?

    • Ideally prospectively

    – Prior to biologic administration

    – Collect multiple sample types

    – Collect detailed clinical data

    • Allows development of ‘biological response

    signatures’

  • Epigenetics in treatment response

    • Ideal for studies of treatment response

    – DNA methylation relatively stable

    – Amenable to whole genome approaches

    – Baseline status / change in status

  • 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

  • 11

    .52

    2.5

    33

    .5

    Norm

    alis

    ed

    CD

    11c E

    xpre

    ssio

    n

    Response

    Relative CD11c Expression in Non-Responders and Responders

    Non-Responders Responders

    Whole genome microarrays

    RNA-seq

  • Samples

    • DNA – EDTA

    – Allows genetic and methylation studies

    • RNA

    – Paxgene vs tempus

  • • Serum

    – Proteomics / autoantibodies / metabolomics

    – If postal collection, only stable markers can be

    analysed

    • ?urine / faecal collection (microbiome)

    • Postal collection

    • Protocols available (email

    [email protected])

  • Costs

    • Tempus - £2.50

    • EDTA – 9p

    • Serum – 9p

    • Plastic holders – 70p

    • Blood box - £1

    • Labels – 7p

    • A4 envelopes – 2p

    • Pre-payment for postage – 18p

  • 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

    www.medicine.manchester.ac.uk/arc/BRAGGSS/