Achieving better treatment response in RA using stratified - - PowerPoint PPT Presentation

Achieving better treatment response in RA using stratified approaches

Anne Barton

Nome mencla nclatu ture re

• Personalized – to the individual
• Stratified – by groups of patients

– Stratified disease

• Anti-CCP + vs Anti-CCP –

– Stratified medicine

• By response to treatment

Rheu euma matoid toid Arthri thritis tis

• Autoimmune disease

– Anti-ccp antibodies

• Joint inflammation

– Joint damage – Disability

• Systemic features

– Premature mortality

Early effective therapy prevents damage and disability

Never treated with DMARD/S

0.5

• 0.5

Farragher et al Ann Rheum Dis 2010; 69: 689

* Adjusted for treatment decisions using marginal structural weights

Stopped first DMARD within 6 months Treated within 6 months of

• nset

>30% improvement in HAQ ~40% worsening

• f HAQ

Effect of early treatment

Mean (95%CI) difference in change in HAQ

• DMARDs
• Biologics

– Anti-TNFs IL6 inhibitors Anti-CD20

• Etanercept

Tocilizumab Rituximab

• Infliximab
• Adalimumab
• Certolizumab
• Golimumab

Treatm eatment ent of Rheu euma matoi toid d Arth thritis ritis

• Non response- up to 40%
• Cost- £10,000 per patient annually
• Severe side effects

Limi mitations tations of anti ti-TNFs TNFs

Rheu euma matoid toid arthri thritis tis tr trea eatm tment ent pa path thway way

Methotrexate Anti-TNF Rituximab

40% failure 20% failure

Quality of life Toxicity, disability

time

Standardised via NICE

Hypot pothesis esis

Treatment Response Genetic Epigenetic Transcriptome Adherence Clinical

Clinical factors

Ov Over erall all res espon ponse se pr pred ediction iction

• 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

BSRBR BS BSR

RBR

BR BSRBR BS BSR

RBR

BR

Predicting good responders

Predicted Current response probability Responder 1 Non-resp <1 Non-responders Responders

Genetic factors

Hist story

• ry of ge

genetic etic st stud udies ies

• Initially candidate genes
• Small sample sizes
• Response assessed at varying times
• First GWAS in <100 anti-TNF treated patients
• No consistent replication of findings

Biol

• log
• gics

cs in n Rh Rheu euma matoi toid d Art rthri riti tis s Gen enet etics s and nd Gen Genom

• mics

s Stu tudy dy Syn yndi dicate te

• Aim of BRAGGSS

– Investigate genetic predictors of response to anti- TNF therapy

• Large nationwide multi-centre collaboration
• Recruited patients registered with BSRBR
• DNA from 3,000 RA patients treated with anti-TNF

and other biologic drugs now collected

GWAS S of anti ti-TNF TNF res esponse

• nse
• 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 – PDE3A-SLCO1C1: suggestive association

• 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

Ge Gene nes s identi entified ied for ant nti-TNF TNF res espon ponse se

• PTPRC

– Reported by Cui et al with good/poor response – Replicated by Plant et al – Not replicated by CORRONA; Dutch GWAS

• CD84

– Cui et al 2013: GWAS 2,700 – Etanercept-treated, p = 8 x 10-8

• PDE3A-SLCO1C1

– Krintel et al, suggestive association – Acosta-Colman 2013; n = 511 samples – Not replicated in UK

Role e of ge genet etics? ics?

• Genetic studies have provided little supportive

evidence – Adherence as a confounder – The measure of response (DAS28) is inappropriate – Treatment response has little/no genetic component – Lack of power to detect modest effects

Role e of ge genet etics? ics?

• Genetic studies have provided little supportive

evidence – Adherence as a confounder – The measure of response (DAS28) is inappropriate – Treatment response has little/no genetic component – Lack of power to detect modest effects

Adherence

Impa pact ct of Inadequ adequate ate Adherenc erence e to to Anti ti-TNF TNF

When you were last due to take your biologic injection, did you take it:

• day agreed with the

nurse?

• day before or after
• within a week
• more than a week
• not at all

Assessment of adherence (n=390) Adherent Non-adherent

Characteristic β - coefficient (95% CI) P-value Disease duration

• 0.07 (-0.02 – 0.01)

0.448 Age 0.02 (0.00 – 0.04) 0.012 Female gender 0.34 (-0.08 – 0.76) 0.108 NSAID usage

• 0.13 (-0.50 – 0.25)

0.500 Marital status

• 0.32 (-0.74 – 0.11)

0.148 Ever non-adherent status 0.53 (0.12 – 0.95) 0.013

Role e of ge genet etics? ics?

• Genetic studies have provided little supportive

evidence – Adherence as a confounder – The measure of response (DAS28) is inappropriate – Treatment response has little/no genetic component – Lack of power to detect modest effects

Outcome measure

DA DAS28 S28

• 28 joints: swollen joint count, tender joint count
• ESR / CRP
• Patient overall assessment (VAS)
• Validated measure used widely in Europe
• NICE guidance

Psy sycholo chological gical fact actors

• rs
• Cordingley et al (2012):
• TJC and VAS correlate with psychological factors

more than SJC or ESR/CRP

• Depressions and anxiety scores

Role e of ge genet etics? ics?

• Genetic studies have provided little supportive

evidence – Adherence as a confounder – The measure of response (DAS28) is inappropriate – Treatment response has little/no genetic component – Lack of power to detect modest effects

Heritability of anti-TNF response

Es Esti tima mating ting her eritabilit itability y us using ng GC GCTA TA

• 1,168 BRAGGSS patients with GWAS
• Analysis Genome-wide Complex Trait Analysis (GCTA)

software

• Primary outcome

– change in (Δ): DAS28, SJC, TJC, ESR and GH

Jian Yang et al. Nat Genet. 2010 July; 42(7): 565– 569. http://www.complextraitgenomics.com/software/gcta/

Res esult ults

Phenotype All samples n=1,140 MAB n= 762 ΔDAS28 0.24 0.45 ΔSJC 0.21 0.60 ΔTJC 0.05 0.35 ΔGH 0.11 0.14 ΔESR 0.34 0.53

The variation in phenotype explained by the SNPs Currently repeating analysis using data from >4,000 samples from international consortia

Role e of ge genet etics? ics?

• Genetic studies have provided little supportive

evidence – Adherence as a confounder – The measure of response (DAS28) is inappropriate – Treatment response has little/no genetic component – Lack of power to detect modest effects

RA su susc sceptibilit eptibility y ge genes es

• 101 identified – but required >50,000 samples
• Largest effect = HLA DRB1 gene
• 3 amino-acids:

– Position 11, 71, 74 – Better model than ‘shared epitope’ (aa 70-74)

Pharm armacogenetics acogenetics in anti ti-TNF TNF res espon ponse se

• Response shows heritability
• DAS28 may require re-weighting to objective

measures

• Adherence should be accounted for where possible
• Power is an issue

Epigenetic factors

Ep Epigene genetics tics in tr trea eatment tment res espon ponse se

• Ideal for studies of treatment response

– DNA methylation relatively stable – Amenable to whole genome approaches – Baseline status / change in status

Laird P W Hum. Mol. Genet. 2005;14:R65-R76

Prelimi eliminary nary Res esult ults

• 36 good vs 36 non-responders to etanercept

DMP P-value of difference Mean (SD) β- values in responders Mean (SD) β- values in non- responders Chromosome: physical position (annotation) Cg04857395 1.46x10-8 0.72 (0.06) 0.81 (0.06) Chr.4: 3516637 (In the gene body

• f LRPAP1)

Cg16426293 1.31x10-7 0.48 (0.05) 0.54 (0.04) Chr.17: 40192112 (2068bp from ZNF385C) Cg03277049 2.22x10-7 0.31 (0.05) 0.37 (0.04) Chr.3: 156534076 (In LINC00886 non-coding RNA) Cg14862806 4.43x10-7 0.35 (0.02) 0.38 (0.03) Chr.17: 21356311

Transcriptomic factors

Prelimi eliminary nary data ta

• 29 non-responders vs 31 extremely good responders
• All on etanercept
• Microarray - compared baseline expression profiles
• BTN3A2 gene p-value 9.42 x 10-6
• Inhibits release of interferon gamma from activated T-

cells

Drug levels

Random ndom drug ug lev evels els

Variable Regression coefficient (95% CI) P value Adalimumab patients Univariate analysis Adalimumab level 0.08 (0.04-0.1) <0.0001 Anti-drug antibody status

• 0.8 (-1.2 to -0.3)

0.002 Multivariate model* Adalimumab level 0.06 (0.02-0.1) 0.009 Anti-drug antibody status

• 0.2 (-0.8 - 0.3)

0.45 * Adjustment for age, gender, BMI, disease duration and adherence Etanercept patients Univariate analysis Etanercept drug level 0.008 (-0.5- 0.03) 0.5

Predict edictor

• rs

s of drug ug levels els

Treatm eatment ent pa path thway way

Start treatment Measure drug levels Improve efficacy

Trial and error Adjust dose according to response

Quality of life Toxicity, disability

time

MAximising Therapeutic Utility for Rheumatoid Arthritis MATURA

Synovial tissue sampling: Pathobiology from RCT

Workstream 1 Workstream 2

Large scale, blood based screening from

• bservational

studies

9 industry partners

Synovial tissue sampling: Pathobiology from RCT Large scale, blood based screening from

• bservational

studies Methotrexate Anti-TNF Rituximab Tocilizumab Statistical analysis and model development

Genetic studies Epigenetic studies Expression profiling Pilot next generation sequencing Proteomic studies Deep immunological phenotyping Biomarkers for stratified medicine

New ew NICE E tr trea eatment tment pa path thway way

Methotrexate Rituximab Abatacept Tocilizumab Anti-TNF

TNF IL6 CTLA-4 anti-B cell

Tofactitinib

Jak/STAT

Drug Response Algorithm Biomarker Apply Algorithm DRUG A DRUG B DRUG C DRUG D

Treat Right First Time

Ackn knowl wledgem edgements ents

Cos Pitzalis and co- investigators Ann Morgan John Isaacs Gerry wilson Kimme Hyrich Darren Plant Jane Worthington Samantha Smith Wendy Thomson Amy Webster Steve Eyre Meghna Jani Suzanne Verstappen