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Patient profiling and predictors of response and non-response to RA therapies

Patient profiling and predictors of response and non-response to RA therapies. Workshop Summary John Isaacs Professor of Clinical Rheumatology, Newcastle University, UK. Use of biomarkers in clinical diagnosis and prognosis — Eugen Feist

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Patient profiling and predictors of response and non-response to RA therapies

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  1. Patient profiling and predictors of response and non-response to RA therapies Workshop Summary John Isaacs Professor of Clinical Rheumatology, Newcastle University, UK

  2. Use of biomarkers in clinical diagnosis and prognosis — Eugen Feist Question – How far have we come in the use of biomarkers for clinical diagnosis and prognosis of RA? ? Biomarkers and predictors of responsiveness to biologics in RA — John Isaacs Question – Is there any evidence that biomarkers can predict responsiveness to biologic therapies? ? Registry evidence – the Italian experience — Gianfranco Ferraccioli Question – Can RCT and registry data identify prognostic factors for clinical remission? ? The demand for personalised healthcare: Identifying the ‘B-cell patient’ — Philippe Dieudé Question – How close are we to ‘personalised medicine’ in RA? ? Exploring the use of biomarkers in patient profiling and prediction of response/non-response to RA therapies

  3. How far have we come in the use of biomarkers for clinical diagnosis and prognosis of RA? – Conclusions • Anti-CCP antibodies can be detected prior to RA occurrence • Detection of anti-CCP significantly improves the diagnosis of early RA • Citrullination modifies potential autoantigens and plays an important role in the pathogenesis of RA • Anti-CCP and anti-mutated citrullinated vimentin (MCV) immunoassays have comparable diagnostic sensitivity and specificity

  4. Is there any evidence that biomarkers can predict responsiveness to biologic therapies? – Conclusions • Conflicting reports of association between TNF polymorphisms and clinical response to TNF inhibitors • Rituximab shows consistent association with RF and/or anti-CCP as the biomarkers predictive of clinical response • RA patients who are seropositive (RF+ and/or anti-CCP+) appear to have an enriched response to rituximab

  5. REFLEX study: Placebo-adjusted ACR responses at Week 24 according to RF and anti-CCP status RF and/or anti-CCP positive RF negative and anti-CCP negative Patients (%) Rituximab (n=157) Rituximab (n=29) Cohen et al, 2006; Smolen et al. 2006

  6. REFLEX Study: 56 week radiographic outcomes: seropositive subgroups RF and/or anti-CCP positive RF negative and anti-CCP negative P=0.0085 P=0.0225 P =0.0018 Roche, data on file

  7. Greater knowledge of predictive factors in our patients: - better personalised treatment strategies • Appropriate ‘tailored’ treatment: - reduction in treatment failure - stops disease progression more quickly Can RCT and registry data identify prognostic factors for clinical remission? – Conclusions • Yes – prognostic for clinical response and remission have been identified, but require further investigation • TNF inhibitors: • Baseline predictors: HAQ, gender • Biomarker predictors: RF+, RF+/CCP+ and high IgA RF levels predict a poor response to TNF inhibitors • Biomarkers that can predict good clinical response to TNF inhibitors still remain to be defined

  8. Prognostic factors for clinical remission with TNF inhibitors Hosmer-Lemeshow test: p=0.935. Hosmer-Lemeshow test: p=0.554. Mancarella L et al. J Rheumatol 2007;34:1670-73

  9. Significantly better EULAR responses on follow-up in RF-negative patients Mancarella L et al: J Rheumatol 2007;34:1670-73

  10. High IgA RF levels are associated with poor clinical response to TNF inhibitors 100 p=0.017 p<0.001 p=0.190 80 60 40 20 0 IgA-RF negative(45 pts) IgA-RF low(38 pts) IgA-RF high (43 pts) Percentage of responders Bobbio-Pallavicini F et al. Ann Rheum Dis 2007;66:302-7

  11. Questions remain: • What influence do these overlapping AIDs have on the course of RA? • Do these patients with RA have a ‘strong’ B cell-driven disease? • Is rituximab more effective in this RA patient segment? • Is rituximab also effective in overlapping AIDs? • Studies ongoing to help answer these questions How close are we to ‘personalised medicine’ in RA? – Conclusions • Studies have shown: • Co-occurrence of other autoimmune diseases (AIDs) in patients with RA is frequent (18–35%) • More common in a particular RA subset: anti-CCP+ and RF+

  12. Frequency of overlapping autoimmune diseases 38% Frequency of overlapping AID in the global RA sample: 18%

  13. RA phenotype according to the overlap syndrome P<0.001 P<0.004

  14. Take home message • Biomarkers will become increasingly important in the management of the patient with synovitis • Diagnosis • Prognosis • Treatment

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