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

Effect of early treatment Mean (95%CI) difference in change in HAQ 0.5 ~40% worsening of HAQ 0 >30% improvement in HAQ -0.5 Stopped first Treated within Never treated DMARD within 6 months of with DMARD/S 6 months onset * Adjusted for treatment decisions using marginal structural weights Farragher et al Ann Rheum Dis 2010; 69: 689

Treatm eatment ent of Rheu euma matoi toid d Arth thritis ritis • DMARDs • Biologics – Anti-TNFs IL6 inhibitors Anti-CD20 • Etanercept Tocilizumab Rituximab • Infliximab • Adalimumab • Certolizumab • Golimumab

Limi mitations tations of anti ti-TNFs TNFs • Non response- up to 40% • Cost- £10,000 per patient annually • Severe side effects

Rheu euma matoid toid arthri thritis tis tr trea eatm tment ent pa path thway way Standardised via NICE 40% 20% failure failure Rituximab Methotrexate Anti-TNF time Toxicity, disability Quality of life

Hypot pothesis esis Transcriptome Epigenetic Adherence Genetic Treatment Response 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 R 2 =0.17 Female gender RF/Anti-CCP R BR R BR BS R BS R BS R BR BS R BR BR BR BS BS

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

Genetic factors

Hist story ory 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 olog ogics cs in n Rh Rheu euma matoi toid d Art rthri riti tis s Gen enet etics s and nd Gen Genom omics 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 onse • 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 Assessment of adherence (n=390) When you were last due to take your biologic injection, did you take it: o day agreed with the Adherent nurse? o day before or after o within a week Non-adherent o more than a week o not at all β - coefficient (95% Characteristic P-value CI) -0.07 (-0.02 – 0.01) Disease duration 0.448 0.02 (0.00 – 0.04) Age 0.012 0.34 (-0.08 – 0.76) Female gender 0.108 -0.13 (-0.50 – 0.25) NSAID usage 0.500 -0.32 (-0.74 – 0.11) Marital status 0.148 0.53 (0.12 – 0.95) Ever non-adherent 0.013 status

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 ors • 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 G enome-wide C omplex T rait A nalysis (GCTA) software Jian Yang et al . Nat Genet. 2010 July; 42(7): 565 – 569. http://www.complextraitgenomics.com/software/gcta/ • Primary outcome – change in ( Δ ): DAS28, SJC, TJC, ESR and GH

Res esult ults The variation in phenotype explained by the SNPs Phenotype All samples MAB n=1,140 n= 762 Δ DAS28 0.24 0.45 Δ SJC 0.21 0.60 Δ TJC 0.05 0.35 Δ GH 0.11 0.14 Δ ESR 0.34 0.53 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 Laird P W Hum. Mol. Genet. 2005;14:R65-R76 • Ideal for studies of treatment response – DNA methylation relatively stable – Amenable to whole genome approaches – Baseline status / change in status

Prelimi eliminary nary Res esult ults • 36 good vs 36 non-responders to etanercept

DMP P-value of Mean (SD) β - Mean (SD) β - Chromosome: difference values in values in non- physical position responders responders (annotation) Cg04857395 1.46x10 -8 0.72 (0.06) 0.81 (0.06) Chr.4: 3516637 (In the gene body of LRPAP1 ) 1.31x10 -7 Cg16426293 0.48 (0.05) 0.54 (0.04) Chr.17: 40192112 (2068bp from ZNF385C ) 2.22x10 -7 Cg03277049 0.31 (0.05) 0.37 (0.04) Chr.3: 156534076 (In LINC00886 non-coding RNA) 4.43x10 -7 Cg14862806 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 ors s of drug ug levels els

Treatm eatment ent pa path thway way Adjust dose according Trial and to response error Measure Improve Start drug efficacy treatment levels time Toxicity, disability Quality of life

MA ximising T herapeutic U tility for R heumatoid A rthritis MATURA