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

• Treatment and being quantitative- early treatment- treatment focussed at a target- is there any difference in the way a target is defined?

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• Syndrome

Disease

Disease subsets witha pathway leading tosymptoms

244 (58%)109 (51%)42 (21%)- / -

153 (36%)88 (41%)111 (55%)+ / -

26 (6%)16 (7%)50 (25%)+ / +

negativepositive

Anti-CCP antibodies

ControlsLeiden EAC RA patients

SE-status*

Association between anti-CCP-responses and HLA-DRB1 SE-alleles

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

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?

RA consists of two syndromes: ACPA+ versus ACPA-

ACR-classification proces:define disease based on characteristic cases

ACPA+ versus ACPA-

HLA-SE

PTPN22

rs- C5-TRAF1

rs- TNFAIP3-OLIG3

rs- CTLA4

rs- STAT4

rs- CCL21rs-MMEL1-TNFRSF14

rs-CDK6, PRKCQ, KIF5A

CD40, IL2RA, IL2RB

HLA-DR3rs- IRF5rs- STAT4

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.

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

Phenotype clearly different

Joint destruction over time drug free remission rate

Fulfillment of the criteria for RA after1 Year 2 Years 3 Years

#69 CCP+ Pts 83% 90% 93%

249 CCP- Pts 18% 24% 25%

318 Pts 32% 38% 40%

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

Placebo

t = 0 t = 3 t = 12 t = 18

6 tabs

0 tabs

15 – 30 mg

6 – 12 tabs

t = 6 t = 9 t = 15

MTX Taper MTX to 0

Anti-CCP pos group (n=27)p=0.0002

Anti-CCP neg group (n=83)p=0.51

Time to diagnosis RA (months)

MTX groupPlacebo group

0 3 6 9 12 15 18 21 24 27 300

30 Months Follow-up

Anti-CCP pos group (n=27)p=0.03

Anti-CCP neg group (n=83)p=0.46

Cumulative probability (%)MTX group

Placebo group

0 25 50 75 100

Radiographic Progression

DAS in time stratifiedDAS in time stratified

MTXPlacebo

Time (months)

ACPA pos ACPA neg

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

Overview of seminar

• Treatment and being quantitative- early treatment- treatment focussed at a target- is there any difference in the way a target is defined?

Chronic,destructivepolyarthritis

Slowly progressive

Rapidlyprogressive

Generalpopulation

Undifferentiatedarthritis

Timing and Uncertainty

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 dataTom Huizinga. Personal data

Since 1993 2400 patients included with > two year follow-up

800 undifferentiated arthritis

900 RA700 other diagnosis

Diagnosis at inclusion

Lessons from Leiden Early Arthritis Cohort

40 % remission 40 % RA

Prediction Rule for Development of RA

1. What is the age? Multiply with 0.02

2. What is the gender? In case female 1 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 minutes 0.5 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 higher 1 point________

6. What is the number of swollen joints?

In case 4–10 0.5 point________

In case 11 or more 1 point________

7. What is the C-reactive protein level (mg/L)?

In case 5–50 0.5 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

Predicted Risk on RA vs Prediction Score

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

Prediction Thinking is Now Implemented in the 2010 Criteria

1. Age (multiply by 0.02)2. Gender (female 1)3. Distribution of involved joints

– Small joints hands and feet (0.5)– Symmetric (0.5)– Upper extremities (1)

or upper and lower extremities (1.5)4. VAS morning stiffness

– 26–90 mm (1)– 90 mm (2)

1. Morning stiffness 2. Arthritis of 3 or more joint areas3. Arthritis of hand joints 4. Symmetric arthritis5. Rheumatoid nodules6. Serum rheumatoid factor7. Radiographic changes

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)

5. Number of tender joints – 4–10 (0.5)– 11 or more (1)

6. Number of swollen joints – 4–10 (0.5)– 11 or more (1)

7. C-reactive protein (mg/L)– 5–50 (0.5)– 51 or more (1.5)

8. Rheumatoid factor positive (1)9. Anti-CCP antibodies positive (2)

3. Acute phase reactants– Normal CRP and normal

ESR (0)– Abnormal CRP or

abnormal ESR (1)

4. Duration of symptoms – <6 weeks (0)– ≥6 weeks (1)

Points are shown in parenthesis. Cut point for RA ≥8 points

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

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

ACR 1987 criteria1 ACR/EULAR 2010 criteria2

Early Arthritis Prediction 2007-van der Helm3

A more sensitive tool for identifying early arthritis patients

(n=2258 Leiden Early Arthritis Patients)

2010 ACR/EULAR Classification Criteria

RA at baseline no RA at baseline

1987 ACRClassification

Criteria

RA at baseline 644 82

no RA at baseline 455 1077

Total 1099 1159

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

Performance in early arthritis

Outcome Measure

MTX-initiation DMARD-initiation 5-year Persistency

Criteria Set Sens. Spec. AUC Sens. Spec. AUC Sens. Spec. AUC

1987 ACRClassification

Criteria0.61 0.74 0.67 0.54 0.87 0.71 0.53 0.75 0.61

2010 ACR/EULAR

Classification Criteria

0.84 0.60 0.72 0.74 0.74 0.74 0.71 0.65 0.65

Overview of seminar

• Treatment and being quantitative- early treatment: biology & observational- treatment focussed at a target- is there any difference in the way a target is defined?

ACPA characteristics :a biomarker of the window of opportunity

Population Undifferentiated Artritis

Reumatoide Artritis

ACPAFew isotypes

limited epitope recognitiononly low avidities

Many isotypesextensive epitope

recognitionhigh and low avidities

No changesin ACPA

characteristics

The developing autoimmune response associates with worse prognosis

Results pre-RA versus RA 2

Number of epitopes recognized by sera from:

Recognition of ≥ 1 peptide: 38% 66% p=0.013

None≥ 1 peptide

pre-RA RA

Vimentin peptide A

Vimentinpeptide B

Fibrinogen peptide A

Enolase peptide

Fibrinogen peptide B

Number of epitopes recognized increase from pre-RA to RA

Median number of peptides recognized over time

ACPA characteristics :a biomarker of the window of opportunity

Population Undifferentiated Artritis

Reumatoide Artritis

ACPAFew isotypes

limited epitope recognitiononly low avidities

Many isotypesextensive epitope

recognitionhigh and low avidities

No changesin ACPA

characteristics

What is the relevance of this developing autoimmune response during early artritis?

A broader isotype usage is associated with Radiographic progression

* comparison ≤4 isotypes versus ≥5 isotypes: p<0.05

A broader isotype usage is associated with Radiographic progression

EURIDISS

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

important

Delay < 12 weeks

associates with:

lower rate of joint destruction*

higher chance of DMARD-free remission*

Conclusion:Delay should bediminished

RA-only

Chronic,destructive

polyarthritis

Slowly progressive

Rapidlyprogressive

Generalpopulation

Undifferentiatedarthritis

Why Recommendation 1: Window of Opportunity

Window of Opportunity hypothesis:- Criteria discussion: probabilities.- Biology of probabilities: masterswitch- ACPA only know marker of this process

Overview of seminar

• Treatment and being quantitative- early treatment: biology & observational- treatment focussed at a target- is there any difference in the way a target is defined?

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)

1.Grigor et al. Lancet 2004; 364: 263–269

2.Verstappen et al. Ann Rheum Dis 2007; 66: 1443–1449

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

Comparison after 4 years EAC

Delayed treatment

0 2 4 6 8 10 12 14

Years after inclusion

1993–1995

Survival curves of RA patients and the general Dutch population

Early Arthritis Cohort Leiden

Early treatment

0 2 4 6 8 10 12 14

1996–1998

Early, aggressive treatment, goal-driven

0 2 4 6 8 10 12 14

1999–2006

RA management today

• Remission– Clinical– Radiographic

• Low disease activity

Processes

Goals“Remission”

Tools“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)

Overview of seminar

• 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?

Biomarker-based DAS

IRIDESCENTAcademic database of

relationships from abstracts

IRIDESCENTAcademic database of

relationships from abstracts

IngenuityCommercial database of curated scientific facts

Bioinformatics Knowledge

Literature ReviewHundreds of

scientific articles and posters

Literature ReviewHundreds of

scientific articles and posters

Manual Survey of Scientific

Publications

Gene Expression1416 genes with secreted

proteins profiled in 424 RA patients

Gene Expression1416 genes with secreted

proteins profiled in 424 RA patients

Protein Arrays180 proteins profiled in 410

patients

Protein Arrays180 proteins profiled in 410

patients

Proprietary Molecular Profiling

Review evidence and prioritize

Identify Assays:Analysis of Multiple

Platforms

Optimize Assays:Dilutions

RF BlockingQC metrics

396 Candidate Markers

Shen et al. Stepwise discovery of disease activity biomarkers in rheumatoid arthritis. EULAR 2010; Poster # THU0066

Pre-Analytic Validity: Results Individual Markers

Biomarker Avg. % Difference“OTC” vs. “Fresh”

R2 Conc.[log10 pg/mL]

CRP 0 1.00

EGF 1005 .58

ICAM-1 -2 0.97

IL-6 779 0.05

IL6-R 0 0.56

IL-8 83383 0.01

IL-B 2940 0.05

Leptin -29 0.94

MDC 0 0.91

MMP-1 20 0.97

MMP-3 -1 0.97

Resistin 230 0.74

SAA -4 1.00

TNF-RI 16 0.97

Biomarker Avg. % Difference“OTC” vs. “Fresh”

VCAM-1 -1 0.97

VEGF 121 0.85

YKL-40 80 0.87

COMP 1 1.00

ICAM-3 59 0.74

ICTP -7 0.87

IL-2RA 24 0.91

IP-10 10 0.98

MCSF 88 0.71

OPG 32 0.23

RANKL 0 1.00

THBD 10 0.96

TIMP-1 6 0.94

Qureshi et al. Pre-Analytical Effects of Serum Collection and Handling in Quantitative Immunoassays for Rheumatoid Arthritis; ACR 2010; Poster #1606

Training: Vectra™ DA Algorithm

• 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

IL-6SAA

YKL-40

EGFTNF-RI

LeptinVEGF-AVCAM-1

MMP-1MMP-3

Resistin

TJC28SJC28

PatientGlobal CRP

Biomarkers Used To Predict Each DAS

Component

Bakker et al. Development of a Multi-Biomarker Test for Rheumatoid Arthritis (RA) Disease Activity (Vectra™ DA). ACR 2010; Poster #1753

DAS28CRP=0.56√TJC + 0.28√SJC + 0.14PG + 0.36log(CRP+1) + 0.96TJC=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 +1PT JC=predicted TJC, PSJC=predicted SJC, PPG =predicted PG

Vectra™ DA Validation (RF+ and/or Anti-CCP+):Patient Cohort Characteristics

Parameter BRASS Leiden InFoRM Total

n 87 77 66 230

Gender, % female 83 70 76 77

Median Age (IQR) 58 (48-69) 56 (45-65) 59 (50-66) 58 (48-66)

RF-positive, % 95 91 94 93

CCP-positive, % 93 87 82 88

Median Tender Joint Count (IQR) 15 (4-22) 1 (0-6) 6 (0-21) 5 (0-18)

Median Swollen Joint Count (IQR) 12 (5-17) 0 (0-4) 4 (0-11) 4 (0-12)

Median CRP in mg/L (IQR) 7 (3-15) 7 (3-17) 6 (2-21) 7 (3-17)

Mean Patient Global VAS (IQR) 47 (25-70) 34 (17-50) 45 (16-70) 42 (19-65)

Median DAS28CRP (IQR) 5.5 (3.8-6.5) 2.7 (2.0-4.2) 4.2 (2.2-6.0) 4.1 (2.3-5.8)

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

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

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.

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).

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

BeStTreatment Strategies in Rheumatoid Arthritis

monotherapy

MTX + SSA + pred MTX + IFX

Sequential monotherapy

Step-up combination

Initial combination with prednisone

Initial combination with infliximab

BeSt trial

Each strategy further treatment steps per 3 months if DAS >2.4

Predictors RRP

Predictors Odds ratio 95% CIRF/ACPA both negative 1 positive both positive

ref2.54.0

1.01-6.11.9-8.5

Erosions 0 1-4 4

ref1.33.8

0.6-3.11.6-8.9

CRP mg/L <10 10-35 35

ref1.54.8

0.7-3.22.3-9.7

Therapy mono combi prednisone combi IFX

ref0.20.1

0.1-0.40.1-0.3

RF and ACPA

+/+-/-

1063<10

34241135

Erosions (num

Initial combination with IFX

321321

10634316311494128315104342411

+/- or -/+

RF and ACPA+/+-/-

1063<10

34241135

Erosions (num

Initial combination with IFX

321321

10634316311494128315104342411

+/- or -/+

RF and ACPA+/+-/-

Erosions (num

Initial monotherapy

+/- or -/+

433216

544222

493719

564424

786947

17115211464332162316729209544222493719564424786947

RF and ACPA+/+-/-

Erosions (num

Initial monotherapy

+/- or -/+

433216

544222

493719

564424

786947

17115211464332162316729209544222493719564424786947

RF and ACPA+/+-/-

Erosions (num

+/- or -/+

423015

4215311384641852191251610420136423015

RF and ACPA+/+-/-

Erosions (num

+/- or -/+

423015

4215311384641852191251610420136423015

Risk of RRP (%)

Matrix: RRP after 1 year of treatment

Predictors HAQ >=1Baseline predictors OR (95% CI)

Initial treatment mono

combo prednisone

combo infliximab

0.3 (0.2 - 0.5)

0.4 (0.2 - 0.6)

HAQ < 1.4

1.4 - 2.0

2.6 (1.6 - 4.2)

5.3 (2.9 - 9.5)

VAS pain < 40

(tertiles) 40 - 60

2.2 (1.3 - 3.8)

2.7 (1.4 - 5.1)

RAI < 10

(tertiles) 10 -16

1.7 (1.02 - 3.0)

2.7 (1.5 - 4.7)

> 2 45 64 69 50 68 73 >16

35 54 59 39 58 63 10-16

23 40 45 27 45 50 <10

1.4 - 2 29 47 52 25 46 51 >16

21 37 41 24 41 46 10-16

13 25 29 15 29 33 <10

<1.4 14 25 29 16 29 33 >16

9 18 21 11 21 25 10-16

5 11 14 7 13 16 <10

< 40 40-60 >60 < 40 40-60 >60

> 2 73 86 88 >16

64 80 83 10-16

51 70 74 <10

1.4 - 2 58 75 79 >16

47 66 70 10-16

34 53 58 <10

< 1.4 34 53 58 >16

25 43 48 10-16

16 30 35 <10

< 40 40-60 >60

Matrix: predicted risk HAQ ≥ 1 after 3 monthsMonotherapy

Combo with prednisone Combo with infliximab

VAS pain

High risk

Intermediate risk

Lower risk

Low risk

Differences RRP and HAQ model

• Of all 508 patients in the BeSt, 12% had a HAQ ≥ 1 after three months of treatment as well as RRP after one year.

• Thus, it seems that short-term functional ability and radiological damage progression are different concepts.

• The choice of the best initial treatment is dependent on the relevance of the respective outcome measures for an individual patient.

Which target is relevant for which patient?

Patient developssymptoms Patient visits GP

GP refers patient toRheumatologist

Guidance of treatment possible by prediction based on serum-based activity measurments

or measurements focussed at

prevention of damage versus function

Relevance of CCP-test

DELAY has a price (less remission, more destruction,

more suffering)

Measure disease activity

a quantitative approach to accurate classification of ra. tom huizinga

Documents

hydrographic surveys of rivers and lakes using a ... and...

old.kgainfo.spb.ruold.kgainfo.spb.ru/img/flash/ppt/moskva/882_...

johan huizinga: nature & significance of play as a...

c. morillas - huizinga-caillois. variaciones sobre una...

introduction to ekg and then a little more. to get an...

johan huizinga: nature & significance of play as a

play as self-realization: toward a general theory of...

johan huizinga et l'automne du moyen Âge (1919): écrire

lncs 4191 - accurate airway wall estimation using phase...

huizinga before the abyss: the von leers incident at the...

copyright & fair use educatorsstudents for educators and...

- ra - x pdf · 2008. 10. 20. · ra 011 ra 014ra 012 ra...

ra-ra moment

© 2008 osisoft, inc. | company confidential nams offshore...

homo ludens johan huizinga

demirguc kung huizinga, how does foreign entry affect the...

j. huizinga - web.stanford.edu · j. huizinga the beacon...

huizinga - erasmus and the age of reformation

gombrich d huizinga, 34p, booklett

contextualiser les théories du jeu de johan huizinga et