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A quantitative approach to accurate classification of RA. Tom Huizinga

A quantitative approach to accurate classification of RA. Tom Huizinga. Overview of seminar. RA as a disease versus syndrome - perspective from a disease - perspective from a syndrome

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A quantitative approach to accurate classification of RA. Tom Huizinga

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  1. A quantitative approach to accurate classification of RA. Tom Huizinga

  2. Overview of seminar • RA as a disease versus syndrome- perspective from a disease- perspective from a syndrome • Treatment and being quantitative- early treatment- treatment focussed at a target- is there any difference in the way a target is defined?

  3. Classification: syndrome versus disease • RA=classic syndrome defined by criteria.Now new criteria based on the decision to start with MTX. • RA as a disorder based on pathogenesis • SyndromeDiseaseDisease subsets witha pathway leading tosymptoms

  4. Association between anti-CCP-responses and HLA-DRB1 SE-alleles • Leiden EAC RA patients • Controls • Anti-CCP antibodies • positive • negative SE-status* • + / + • 50 (25%) • 16 (7%) • 26 (6%) • + / - • 111 (55%) • 88 (41%) • 153 (36%) • - / - • 42 (21%) • 109 (51%) • 244 (58%) OR allele frequency: CCP+ vs Controls: 3.38 (2.61-4.38) CCP- vs Controls: 1.22 (0.93-1.60) Huizinga TW…..Criswell L, A&R, 2005

  5. RA consists of two syndromes: ACPA+ versus ACPA- ACR-classification proces:define disease based on characteristic cases ACPA+ versus ACPA- What about other risk factors?Histology?Clinical Course?Treatment response?

  6. RA consists of two syndromes: ACPA+ versus ACPA- ACR-classification proces:define disease based on characteristic cases HLA-SE PTPN22 rs- C5-TRAF1 rs- TNFAIP3-OLIG3 rs- CTLA4 rs- STAT4 rs- CCL21 rs-MMEL1-TNFRSF14 rs-CDK6, PRKCQ, KIF5A CD40, IL2RA, IL2RB HLA-DR3rs- IRF5rs- STAT4 ACPA+ versus ACPA- Raychaudhuri S et al. Nat Genet. 2008 Oct;40(10):1216-23van der Helm A & Huizinga T. Arthr Res Ther. 2008;10(2):205.Huizinga et al. A&R, Arthritis Rheum. 2005 Nov;52(11):3433-8.

  7. Conclusions • Synovitis of anti-CCP positive RA differs from anti-CCP negative: • More infiltrating lymphocytes in anti-CCP positive RA • More fibrosis and increased synovial lining layer in anti-CCP negative RA • Difference is already present early in the disease van Oosterhout M, Bajema I, Levarht EW, Toes RE, Huizinga TW, van Laar JM. Arthritis Rheum. 2008 Jan;58(1):53-60

  8. Fulfillment of the criteria for RA after 1 Year 2 Years 3 Years # 69 CCP+ Pts 83% 90% 93% 249 CCP- Pts 18% 24% 25% 318 Pts 32% 38% 40% Phenotype clearly different Joint destruction over time drug free remission rate

  9. Can the Course of UA being altered by Early Therapy ? • Undifferentiated Arthritis • ACR-criteria RA • if so verum MTX Inclusion: Primary End point: Increase MTX based on DAS Taper MTX to 0 MTX 15 mg 15 – 30 mg 0 mg t = 0 t = 15 t = 6 t = 9 t = 3 t = 12 t = 18 0 tabs 6 tabs 6 – 12 tabs Placebo

  10. 30 Months Follow-up Anti-CCP pos group (n=27) p=0.0002 Anti-CCP neg group (n=83) p=0.51 100 100 80 80 60 60 Cumulative Survival (%) 40 40 20 20 0 0 0 3 6 9 12 15 18 21 24 27 30 0 3 6 9 12 15 18 21 24 27 30 MTX group Time to diagnosis RA (months) Placebo group

  11. Radiographic Progression Anti-CCP pos group (n=27) p=0.03 Anti-CCP neg group (n=83) p=0.46 20 49 15 15 Radiographic progression (Sharp/van der Heijde score) 10 10 5 5 0 0 0 25 50 75 100 0 25 50 75 100 MTX group Cumulative probability (%) Placebo group

  12. DAS in time stratified MTX Placebo ACPA pos ACPA neg DAS Time (months)

  13. Summary of ACPA positive versus ACPA negative RA • HLA, PTPN22, smoking point to two diseases • C5-TRAF point to two diseases • Output of WGAS studies point to two diseases • Phenotypic data more “formally” studied • Histological differences • Subanalysis of PROMPT-study • Propose as new criteria RA-type 1 and RA-type 2, to get criteria closer to pathogenesis

  14. Overview of seminar • RA as a disease versus syndrome- perspective from a disease- perspective from a syndrome • Treatment and being quantitative- early treatment- treatment focussed at a target- is there any difference in the way a target is defined?

  15. Timing and Uncertainty Slowly progressive Chronic, destructive polyarthritis General population Undifferentiated arthritis Rapidly progressive • Window of Opportunity hypothesis • Concept of time not a biological basis • Criteria discussion leads to nosology – better to stick to probabilities • Biology of probabilities – masterswitch Tom Huizinga. Personal data

  16. Lessons from Leiden Early Arthritis Cohort 40 % remission 40 % RA Since 1993 2400 patients included with > two year follow-up Diagnosis at inclusion 800 undifferentiated arthritis 700 other diagnosis 900 RA

  17. Prediction Rule for Development of RA 1. What is the age? Multiply with 0.02 2. What is the gender? In case female1 point ________ 3. How is the distribution of involved joints? In case small joints hands or feet: 0.5 point ________ In case symmetric 0.5 point ________ In case upper extremities 1 point ________ Or: In case upper & lower extremities 1.5 points ________ 4. What is the length of the morning stiffness (minutes)? In case 30–59 minutes0.5 point________ In case ≥60 minutes 1 point________ 5. What is the number of tender joints? In case 4–10 0.5 point________ In case 11 or higher1 point________ 6. What is the number of swollen joints? In case 4–100.5 point________ In case 11 or more 1 point________ 7. What is the C-reactive protein level (mg/L)? In case 5–500.5 point________ In case 51 or higher 1.5 points ________ 8. Is the rheumatoid factor positive? If yes 1 point ________ 9. Are the anti-CCP antibodies positive? If yes 2 points________ TOTAL SCORE:________ van der Helm-van Mil AH, et al. Arthritis Rheum 2008;58:2241–7

  18. Predicted Risk on RA vs Prediction Score AUC 0.84 0.88 Replicated in UK, Norway, Germany, Japan, Middle east and Latin America AUC=area under the curve; van der Helm-van Mil AH, et al. Arthritis Rheum 2008;58:2241–7

  19. Prediction Thinking is Now Implemented in the 2010 Criteria ACR 1987 criteria1 ACR/EULAR 2010 criteria2 • 1. Joint involvement • 1 medium-large joint (0) • 2–10 medium-large joints • 1–3 small joints (large joints not counted) (2) • 4–10 small joints (large joints not counted (3) • >10 joints (at least one small joint) (5) • 2. Serology • Negative RF and negative ACPA (0) • Low positive RF or now positive ACPA (2) • High positive RF or high positive ACPA (3) • Acute phase reactants • Normal CRP and normal ESR (0) • Abnormal CRP or abnormal ESR (1) • Duration of symptoms • <6 weeks (0) • ≥6 weeks (1) • Morning stiffness • Arthritis of 3 or more joint areas • Arthritis of hand joints • Symmetric arthritis • Rheumatoid nodules • Serum rheumatoid factor • Radiographic changes Four of these 7 criteria must be present. Criteria 1 through 4 must have been present for at least 6 weeks Points are shown in parenthesis. Cut point for RA ≥6 points. Patients are also classified as having RA if they have (a) typical erosions; (b) long-standing disease previously satisfying the classification criteria Early Arthritis Prediction 2007-van der Helm3 • Age (multiply by 0.02) • Gender (female 1) • Distribution of involved joints • Small joints hands and feet (0.5) • Symmetric (0.5) • Upper extremities (1) or upper and lower extremities (1.5) • VAS morning stiffness • 26–90 mm (1) • 90 mm (2) • Number of tender joints • 4–10 (0.5) • 11 or more (1) • Number of swollen joints • 4–10 (0.5) • 11 or more (1) • C-reactive protein (mg/L) • 5–50 (0.5) • 51 or more (1.5) • Rheumatoid factor positive (1) • Anti-CCP antibodies positive (2) Points are shown in parenthesis. Cut point for RA ≥8 points 1. Arnett FC, et al. Arthritis Rheum 1988;31:315-24; 2. New ACR/EULAR diagnostic criteria. Presented at ACR, Philadelphia, 10–16th October 2009; 3. van der Helm-van Mil AHM, et al. Arthritis & Rheum 2007:56;433–440

  20. A more sensitive tool for identifying early arthritis patients(n=2258 Leiden Early Arthritis Patients)

  21. Earlier detection of RA 297 patients fulfilled the 1987 ACR criteria during the first year, but not at baseline 202 (68.0%) however did fulfill the 2010 criteria at baseline RA patients classified in an earlier phase of the disease

  22. Performance in early arthritis

  23. Overview of seminar • RA as a disease versus syndrome- perspective from a disease- perspective from a syndrome • Treatment and being quantitative- early treatment: biology & observational- treatment focussed at a target- is there any difference in the way a target is defined?

  24. ACPA characteristics :a biomarker of the window of opportunity Few isotypeslimited epitope recognitiononly low avidities Many isotypesextensive epitope recognitionhigh and low avidities No changesin ACPAcharacteristics ACPA Undifferentiated Artritis Reumatoide Artritis Population The developing autoimmune response associates with worse prognosis

  25. None ≥ 1 peptide Fibrinogen peptide A Enolase peptide Fibrinogen peptide B Vimentin peptide B Vimentin peptide A Results pre-RA versus RA 2 Number of epitopes recognized by sera from: pre-RA RA Recognition of ≥ 1 peptide: 38% 66% p=0.013

  26. Number of epitopes recognized increase from pre-RA to RA Median number of peptides recognized over time

  27. ACPA characteristics :a biomarker of the window of opportunity Few isotypeslimited epitope recognitiononly low avidities Many isotypesextensive epitope recognitionhigh and low avidities No changesin ACPAcharacteristics ACPA Undifferentiated Artritis Reumatoide Artritis Population What is the relevance of this developing autoimmune response during early artritis?

  28. A broader isotype usage is associated with Radiographic progression EAC * comparison ≤4 isotypes versus ≥5 isotypes: p<0.05

  29. A broader isotype usage is associated with Radiographic progression EURIDISS * comparison ≤4 isotypes versus ≥5 isotypes: p<0.05

  30. Aim of early treatment • To prevent functional disability • To prevent structural damage • To prevent comorbidity (cardiovascular disease, amyloidosis) • To prevent “MasterSwitches” turned on that induce chronicity Time is important

  31. Delay < 12 weeks associates with: lower rate of joint destruction* higher chance of DMARD-free remission* Conclusion:Delay should bediminished RA-only

  32. Why Recommendation 1: Window of Opportunity Slowly progressive Chronic, destructive polyarthritis General population Undifferentiated arthritis Rapidly progressive Window of Opportunity hypothesis:- Criteria discussion: probabilities.- Biology of probabilities: masterswitch- ACPA only know marker of this process

  33. Overview of seminar • RA as a disease versus syndrome- perspective from a disease- perspective from a syndrome • Treatment and being quantitative- early treatment: biology & observational- treatment focussed at a target- is there any difference in the way a target is defined?

  34. Importance of patient monitoring: evidence from RCT • TICORA1 • Intensive: monthly, DAS guided • Routine: every 3 months • Remission: 65% (intensive) vs. 16% (routine) • CAMERA2 • Intensive: monthly, computer program • Routine: every 3 months usual care rheumatologist • Remission: 50% (intensive) vs. 37% (routine) • Grigor et al. Lancet 2004; 364: 263–269 • Verstappen et al. Ann Rheum Dis 2007; 66: 1443–1449

  35. Importance of patient monitoring: evidence from longitudinal patient cohorts • Early Arthritis Cohort Leiden • Patients treated from ’93–’95 with Pyramid strategy • Patients treated from ’95–’98 with DMARD within two weeks

  36. Comparison after 4 years EAC

  37. Survival curves of RA patients and the general Dutch population Delayed treatment 1.0 1993–1995 0.9 Survival probability 0.8 0.7 0.6 0 2 4 6 8 10 12 14 Years after inclusion Early Arthritis Cohort Leiden

  38. Survival curves of RA patients and the general Dutch population Early treatment 1.0 1996–1998 0.9 Survival probability 0.8 0.7 0.6 0 2 4 6 8 10 12 14 Years after inclusion Early Arthritis Cohort Leiden

  39. Survival curves of RA patients and the general Dutch population Early, aggressive treatment, goal-driven 1.0 1999–2006 0.9 Survival probability 0.8 0.7 0.6 0 2 4 6 8 10 12 14 Years after inclusion Early Arthritis Cohort Leiden

  40. RA management today • Remission • Clinical • Radiographic • Low disease activity Goals “Remission” Tools Processes “More & Better” • More conventional DMARDs • Biologics available as highly effective alternatives “More & Better” • Early treatment is key • Aggressive therapy approach with better results • Disease activity measurement (e.g. DAS28)

  41. Overview of seminar • RA as a disease versus syndrome- perspective from a disease- perspective from a syndrome • Treatment and being quantitative- early treatment: biology & observational- treatment focussed at a target- is there any difference in the way a target is defined?Perspective : ?Biology?-?Swollen joint etc.?-?Function?

  42. Biomarker-based DAS Gene Expression 1416 genes with secreted proteins profiled in 424 RA patients IRIDESCENT Academic database of relationships from abstracts Literature Review Hundreds of scientific articles and posters Manual Survey of Scientific Publications Proprietary Molecular Profiling Data Bioinformatics Knowledge bases Ingenuity Commercial database of curated scientific facts Protein Arrays 180 proteins profiled in 410 patients 396 Candidate Markers Review evidence and prioritize Identify Assays: Analysis of Multiple Platforms Optimize Assays: Dilutions RF Blocking QC metrics 42 Shen et al. Stepwise discovery of disease activity biomarkers in rheumatoid arthritis. EULAR 2010; Poster # THU0066

  43. Pre-Analytic Validity: Results Individual Markers Qureshi et al. Pre-Analytical Effects of Serum Collection and Handling in Quantitative Immunoassays for Rheumatoid Arthritis; ACR 2010; Poster #1606

  44. Training: Vectra™ DA Algorithm DAS28CRP=0.56√TJC + 0.28√SJC + 0.14PG + 0.36log(CRP+1) + 0.96 TJC=tender joint count; SJC=swollen joint count; PG =patient global health Vectra DA Score =(0.56√PTJC + 0.28√PSJC + 0.14PPG + 0.36log(CRP+1) + 0.96) * 10.53 +1 PT JC=predicted TJC, PSJC=predicted SJC, PPG =predicted PG SJC28 YKL-40 TJC28 Biomarkers Used To Predict Each DAS Component IL-6 Leptin SAA VEGF-A EGF VCAM-1 TNF-RI MMP-1 MMP-3 Resistin CRP Patient Global CRP Includes 12 biomarkers and uses a formula similar to DAS28CRP Different subsets and/or weightings of biomarkers are used to estimate SJC28, TJC28, and PG Bakker et al. Development of a Multi-Biomarker Test for Rheumatoid Arthritis (RA) Disease Activity (Vectra™ DA). ACR 2010; Poster #1753

  45. Vectra™ DA Validation (RF+ and/or Anti-CCP+):Patient Cohort Characteristics Curtis et al. Validation of a Multi-Biomarker Test for Rheumatoid Arthritis (RA) Disease Activity (Vectra™ DA) in a Multi-Cohort Study. ACR 2010; Poster #1782

  46. Vectra™ DA Validation (RF+ and/or Anti-CCP+): Results Pearson Correlation = 0.56 The Vectra DA score was also associated with DAS28-CRP (p<0.05) within subgroups of RA patients who were <65 years of age, ≥65, male, female, overweight (body-mass index >25),not overweight, on anti-TNF medications, on methotrexate but not biologics and on steroids. Curtis et al. Validation of a Multi-Biomarker Test for Rheumatoid Arthritis (RA) Disease Activity (Vectra™ DA) in a Multi-Cohort Study. ACR 2010; Poster #1782; Data on file Crescendo Bioscience

  47. Vectra™ DA Validation (RF+ and/or Anti-CCP+): Ability to Detect Low Disease Activity The exploratory analysis shows that patients with low Vectra DA scores tended to have a higher likelihood of low joint counts than those with low CRP Although these results were not statistically significant, they do suggest that the Vectra DA score may more accurately detect low joint counts than CRP. Curtis et al. Validation of a Multi-Biomarker Test for Rheumatoid Arthritis (RA) Disease Activity (Vectra™ DA) in a Multi-Cohort Study. ACR 2010; Poster #1782

  48. Vectra™ DA Validation (RF+ and/or Anti-CCP+): Biomarkers Other Than CRP • In a multivariate regression analysis of predictors of the DAS28CRP using the Vectra DA score (without CRP) and CRP as predictors, both the Vectra DA score (without CRP) and CRP were statistically significant (p<0.001) • Since the DAS28CRP includes CRP itself, a multivariate regression analysis was carried out to evaluate both CRP and the Vectra DA Score (without CRP) as predictors of the DAS28CRP with CRP removed • The Vectra DA score (without CRP) was statistically significant (p<0.001), and the CRP term was not significant (p=0.22). Curtis et al. Validation of a Multi-Biomarker Test for Rheumatoid Arthritis (RA) Disease Activity (Vectra™ DA) in a Multi-Cohort Study. ACR 2010; Poster #1782

  49. FARR BeStTreatment Strategies in Rheumatoid Arthritis Predictors of HAQ response after 3 months of treatment with different strategies in recent onset active RA are different than predictors of rapid radiological progression

  50. BeSt trial Each strategy further treatment steps per 3 months if DAS >2.4

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